Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Master thesis

3,334 views

Published on

  • Login to see the comments

  • Be the first to like this

Master thesis

  1. 1. UNIVERSITY OF SOUTHAMPTON FACULTY OF ENGINEERING AND THE ENVIRONMENT INSTITUTE OF SOUND AND VIBRATION RESEARCH Removing Movement Artefact from ECG Signals Recorded from Human Subjects by Shuokai Pan Supervisor: Dr D. Simpson & Dr E. Rustighi Thesis for MSc Sound and Vibration December 2013
  2. 2. i Acknowledgements They say that success is ten percent inspiration and ninety percent perspiration. Hence I would like to express my outmost gratitude to my friends, colleagues and supervisors, Dr David Simpson and Dr Emiliano Rustighi, for giving me both. Their extraordinary ability to make complex problems perspicuous has helped me many times during the MSc project. I also appreciate their patience with me, and that they spent many hours of their valuable time for correcting my English mistakes. I am also grateful to all my classmates at ISVR. Unfortunately, the list of names is too long to be given here. Thank you for creating a pleasant and stimulating work environment. I particularly enjoyed the cultural diversity and the feeling of an international family atmosphere. Finally, I would like to express my deep gratitude to my wife, Anna Deng, for your encouragement, understanding, patience and joy you bring. I owe my parents, Shehong Pan and Chunxi Ma, for their sacrifice and endless support during both bad times and good times and for teaching me the intrinsic value of education. ShuokaiPan Southampton December, 2013
  3. 3. ii Contents Abstract........................................................................................................................- 1 - Chapter 1 Introduction....................................................................................................- 2 - 1.1 Motivation ........................................................................................................- 2 - 1.2 Objectives of the Project.....................................................................................- 3 - 1.3 Thesis Organization ...........................................................................................- 4 - 1.4 Main Contributions............................................................................................- 4 - Chapter 2......................................................................................................................- 5 - AReview of ECG Basics and Artefact..............................................................................- 5 - 2.1 The heart..........................................................................................................- 5 - 2.1.1 Anatomy and Physiology of the Heart.........................................................- 5 - 2.1.2 Electrical Conduction System of the Heart ..................................................- 6 - 2.2 ECG Measurement and Morphology ....................................................................- 7 - 2.2.1 ECG Signal Acquisition ............................................................................- 7 - 2.2.2 ECG Morphology ....................................................................................- 9 - 2.3 Artefact in ECG............................................................................................... - 10 - 2.4 Origins of Motion Artefact................................................................................ - 11 - 2.4.1 Skin Anatomy and Skin Potential ............................................................. - 12 - Chapter 3.................................................................................................................... - 15 - AReview of Adaptive Filter Theories and its Potential for Motion Artefact Reduction.......... - 15 - 3.1 Introduction .................................................................................................... - 15 - 3.2 Adaptive Filter Theories ................................................................................... - 15 - 3.2.1 Linear Adaptive Filters ........................................................................... - 17 - 3.2.2 Nonlinear Adaptive Filters ...................................................................... - 20 - 3.3 Areview of Adaptive Motion Artefact Reduction in ECG signals........................... - 23 - Chapter 4.................................................................................................................... - 27 - ECG Signal Acquisition and Motion Artefact Generation .................................................. - 27 - 4.1 Introduction .................................................................................................... - 27 - 4.2 Methods......................................................................................................... - 28 - 4.2.1 Equipment Set Up.................................................................................. - 28 - 4.2.2 Method of Stimulus Signal Generation ..................................................... - 30 - 4.2.3 Equipment Calibration ............................................................................ - 32 - 4.2.4 Experimental Procedure.......................................................................... - 36 - Chapter 5.................................................................................................................... - 39 - Application of Adaptive Filters to ECG Motion Artefact Reduction.................................... - 39 - 5.1 Introduction .................................................................................................... - 39 - 5.2 Methods......................................................................................................... - 40 - 5.2.1 Signal Preprocessing .............................................................................. - 40 -
  4. 4. iii 5.2.2 Adaptive Motion Artefact Reduction ........................................................ - 42 - 5.2.3 Performance Evaluation and Parameter Number Optimization ..................... - 43 - 5.3 Computer Simulations...................................................................................... - 45 - 5.3.1 Linear LMS Adaptive Filter..................................................................... - 48 - 5.3.2 Linear RLS Adaptive Filter ..................................................................... - 51 - 5.4 Experimental Data ........................................................................................... - 55 - 5.4.1 Sinusoidal Excitation.............................................................................. - 55 - 5.4.2 BLWN Excitation .................................................................................. - 72 - 5.4.3 Voluntary Motion ................................................................................... - 82 - 5.5 Parameter Analysis .......................................................................................... - 95 - Chapter 6.................................................................................................................... - 97 - Discussions, Conclusions and Suggestions for Future Work............................................... - 97 - 6.1 Discussions and Conclusions............................................................................. - 97 - 6.2 Suggestions for Future Work........................................................................... - 100 - Reference ................................................................................................................. - 101 - Appendix A: Ethics form............................................................................................ - 107 - Appendix B: Research Protocol....................................................................................- 112 - Appendix C: Consent Form..........................................................................................- 118 -
  5. 5. - 1 - Abstract Cardiovascular disease (CVD) is a major cause of death in many regions of the world. The electrocardiogram (ECG), being the most widely used approach, provides an effective way to monitor heart conditions and to diagnose heart dysfunctions. Traditional equipment seriously limits the daily routine of the patients while wearable systems offer convenient solutions to long term assessment of cardiac health and therapy effects. However, one of the major problems of such ambulatory ECG recordings is the presence of artefact and motion artefact are regarded as the most disturbing source as they not only overlap in the frequency domain with the ECG signal but also resemble important features of ECG waveform. One promising direction of motion artefact reduction is the use of adaptive methods which β€œclean” the noisy signals by subtracting an estimate of movement interference from additional channels of reference recordings. Previous studies have employed electrode deformation or acceleration, skin/electrode impedance, skin strain and half-cell potential as reference inputs, but most of them referred to linear filter structures and none was very successful in decreasing the motion artefact in a reproducible and consistent manner. This could be attributed to the lack of correlation between the reference signal and the induced noise. The major contributions of this study are the incorporation of nonlinear cascade models, the comparison of different evaluation standards and optimizations of filter orders. The types of motions introduced were controlled vibration of the shaker and voluntary motions of the subjects while the reference signals were either single axis acceleration of the shaker or trial axial accelerations of the subjectβ€Ÿs right hand. The performance of each structure was evaluated using NCC (Normalized Cross Correlation Coefficient), PAR (Percentage Artefact Reduction) and AIC (Akaike Information Criterion) criteria. From the results obtained in this study, it can be concluded that the linear and nonlinear adaptive methods investigated in this study are most effective in reducing artefact generated through controlled motions but less effective to those induced voluntarily. Such poor effectiveness of adaptive methods could result from other sources of disturbances or the insufficiency of the nonlinear models investigated. Possible directions for future work are listed in the end.
  6. 6. - 2 - Chapter 1 Introduction 1.1 Motivation Being one of primary causes of death in many parts of the world and costing billions of dollars every year according to American Heart Association [20], cardiovascular diseases (CVD) necessitate the advance and research for their early, accurate and cost-effective diagnosis and assessment. One of the techniques that is widely used to monitor heart health is the electrocardiogram (ECG), which is the recording of the electrical activity of the heart obtained from measuring the electrical potential between specific sites on the skin using electrodes. Although current medical equipment can provide precise information about the heart conditions, traditional ECG acquisition approaches are limited in use when patientsβ€Ÿ movements are required. This can be a burden to both the medical staff and patients when frequent, long periods or exercise recordings are required for continuous CVD monitoring, treatment or research. Recent advances in electronics, computational power and signal processing techniques have revolutionized the concept of ECG monitoring. A variety of integrated wearable systems such as iRhythm [64] and Corventis [65] have been introduced in an attempt to enable the continuous, reliable and long-term cardiovascular recording without interfering with the patientsβ€Ÿ daily routines. Despite these improvements in instrumentation, the signal quality obtained in ambulatory conditions continues to be plagued by high levels of noise induced by wearersβ€Ÿ movements, known as motion/movement artefact. For instance, in Holter monitoring or stress tests, motion artefact often lead to difficult interpretation of ECG signals since it can resemble the morphologies of the P, QRS and T waves. For event recorders or detection devices, identifying and compensating motion artefact is crucial for the accurate operation of the devices as they may lead to false alarms and wrong event detections [1]. Reducing the motion artefact would considerably extend the applicability of ambulatory monitors to situation of higher activity, as usually encountered in non-controlled daily-life situations. [1] What make the motion artefact particularly troublesome are the facts that their spectra completely overlap that of the ECG and their characteristics are constantly changing during different movements [2]. Therefore, digital filters with fixed specifications are not appropriate for motion artefact reduction since no determined prior knowledge of the operation environment is available for the systems parameters. Over the past decades, numerous more advanced methods have been proposed and investigated in literature for interference cancellation in ECG signals. From the perspective of underlying principles, they can be divided into two major categories: non-signal processing approaches and signal processing approaches. Non-signal processing
  7. 7. - 3 - approaches usually involve modifying the electrodes configurations or placement and careful preparation practice such as abrasion of the skin [25] and skin puncture [26]. These methods can be regarded as preventing the motion artefact from being generated, but have a number of practical limitations or drawbacks under ambulatory conditions. Signal processing methods, by contrast, aim at reducing the interference through mathematical manipulations such as discrete time wavelet transform [36], independent component analysis [37] and adaptive filtering [40], each of which has its respective desirable properties. All of these approaches have been shown to produce varying but promising results. Due to constraints in time, the current project will focus on adaptive filtering techniques, building on previous contributions from former ISVR students [3, 4, 62]. In addition to the motion artefact, the ECG signals may be corrupted by various other sources of noise like power line interference, baseline wander, noise from other bioelectric signals (especially electromyography - EMG) and noise arising from the instrumentation itself (amplifiers, filters). These are covered in chapter 2 for completeness but their removal is not included in this project since our concern is the movement noise reduction. From this point onward, β€žartefactβ€Ÿ will be used to refer exclusively to motion artefact. 1.2 Objectives of the Project The objectives of this study can be summarized as, 1. To investigate the coupling between acceleration recorded at the electrodes and the recorded ECG signal. 2. To refine previously proposed linear adaptive filtering algorithms and extend these to nonlinear cases for movement artefact reduction and validate them on simulated and recorded signals. 3. To investigate objective measures for evaluating the effectiveness of linear and nonlinear adaptive filtering algorithms and compare their performances. This project builds upon previous work by former ISVR students and its primary extension will be the experiments with nonlinear adaptive filters. The key assumption underlying the previously studied linear adaptive filters is the linear correlation between motion acceleration and the artefact induced. Former results have shown that this hypothesis can lead to very notable reduction in artifact in some cases, but the remaining amount of noise may indicate a certain degree of nonlinearity. Including nonlinear adaptive filtering algorithms allows for investigation of this nonlinear possibility, comparison with linear models and better selection of algorithms for practical applications. This will be the main contribution of this study.
  8. 8. - 4 - 1.3 Thesis Organization The contents of this thesis are organized as follows. Chapter 1 introduces the background information and major motivations for carrying out this study. It also describes the original contribution that will be made after the completion. Chapter 2 provides a brief overview of the heart physiology, electrocardiogram and types of artefact which might corrupt the ECG signals. Possible physical models for the generation of motion artefact are specifically discussed. Basic theories of linear and nonlinear adaptive filters are introduced in Chapter 3. The methodologies of generating motion artefact and how the corrupted ECGs of each subject were recorded experimentally are described in Chapter 4. Details of how each set of recordings were processed and representative results were explained and demonstrated in Chapter 5, which forms the most important part of this thesis. Finally, discussions, conclusions and suggestions for future work are presented in Chapter 6. 1.4 Main Contributions The major contributions from this thesis are: 1. Critically reviewed and discussed various quantitative measures for performance evaluation. The filter orders were optimized before adaptive filtering using selected criteria. 2. Validated the effectiveness of LMS and RLS adaptive filters for motion artifact reduction through computer simulations. 3. Validated the performances of single input and tri-input RLS algorithms using experimentally recorded ECGs. 4. Extended previous studies on linear adaptive methods using three nonlinear cascade models and determined the LN model to be the most suitable one for most situations investigated.
  9. 9. - 5 - Chapter 2 A Review of ECG Basics and Artefact The ECG is perhaps the most commonly known, recognized and used biomedical signal in clinical practice whose origin dates back to more than100 years. It is the manifestation of the electrical activity of the heart, and can be recorded fairly easily with surface electrodes on the limbs or chest. Not only can the rhythm of the heart be estimated by counting the readily identifiable waves, but, more importantly, the fact that the ECG waveshape is altered by cardiovascular diseases and abnormalities such as myocardial ischemia and infraction, ventricular hypertrophy and conduction problems means that it is very important in the diagnosis and treatment evaluation of these dysfunctions [5]. 2.1 The heart 2.1.1 Anatomy and Physiology of the Heart The human heart is shaped like a cone, weighs 250- 350 grams and is approximately equal to the size of the fist [6]. It is located anterior to the vertebral column and posterior to the sternum. It is composed of three layers of tissues: Myocardium, Epicardium, Endocardium. The myocardium is the thick main layer of the heart muscle, made up predominantly of cells called myocytes and responsible for pumping blood. Its outside surface is covered by a thin, glossy membrane called the epicardium. Another smooth glossy membrane, the endocardium, covers the inside surfaces of the heartβ€Ÿs 4 chambers, the valves, and the muscles that attach to those valves [7]. The human heart is a four chamber dual pump with two atria for collection of blood and two ventricles for pumping out of blood. Figure 2.1-A shows a cross section of the four chambers, direction of blood flow and the major vessels connected to the heart while Figure 2.1-B reveals its function in the blood circulatory system. The resting or filling phase of a cardiac cycle is called diastole; the contracting or pumping phase is called systole. The right atrium (RA) receives blood from the superior and inferior vena cavae, blood which has delivered oxygen and other nutrients and picked up waste products (including carbon dioxide – CO2) from all parts of the body. During atrial contraction, blood is passed from the right atrium to the right ventricle (RV) through the tricuspid valve. During ventricle systole, the impure blood in the right ventricle is pumped out through the pulmonary valve to the lungs to pick up oxygen and release CO2. The left atrium (LA) collects blood from the lungs, which is passed on during atrial
  10. 10. - 6 - contraction to the left ventricle (LV) via the mitral valve. The left ventricle is the largest and the most important cardiac chamber. It contracts the strongest as it has to pump out the oxygenated blood through the aortic valve and the aorta against the pressure of the rest of the vascular system of the body where gasses (oxygen, carbon dioxide, etc.), nutrients and waste products are again exchanged [5]. (A) [9] (B) [10] Figure (2.1) Anatomy of the heart and its function in humanβ€Ÿs circulatory system 2.1.2 Electrical Conduction System of the Heart The rhythmic contractile activity of the heart is never possible without its unique and specialized electrical excitation and conduction systems, as shown in Figure (2.2), (A) [11] (B) [12] Figure (2.2) Electrical Conduction System of the Heart. Unlike skeletal muscle which is triggered directly from the nervous system, the contractions of the heart muscle are initiated internally, by its own natural, intrinsic pacemakerβ€”the Sinoatrial node (SA node) and are only speeded up or slowed down by the delivery of neurotransmitters from the nervous system. The electrical impulses generated by the SA node excite its own train of action potentials through the processes of depolarization and repolarization. These action potentials then propagate
  11. 11. - 7 - through the atrial musculature at comparatively slow rates, causing slow-moving depolarization of the atria. When the electrical stimulus arrives at the Atrioventricular node, the only conduction pathway between the atria and the ventricles, it passes through the bundle of His, diverges into the right and left bundle branches, spreads down to the apex of the heart and curve back up the sides through the Purkinje Fibers, eventually causing rapid contraction of the ventricles. A small portion of this electrical potential can be recorded at the body surface. By applying electrodes on the skin at the selected points, the electrical potential generated by this current can be recorded as an ECG signal [13]. 2.2 ECG Measurementand Morphology 2.2.1 ECG Signal Acquisition The worldβ€Ÿs first practical electrocardiography was pioneered by a Dutch doctor and physiologist Willem Einthoven. His machine was sensitive enough to reliably measure electrical potential differences between two limbs placed in two separate jars filled with salt water. Plotting the values measured versus time gave a picture of the electrical activity of the human heart. After examining a variety of combinations of limbs and other regions of the body, Einthoven and his contemporaries found that the ECG machine only captured one dimension of the three dimensional electricity fluctuation of the heart. In other words, each of these combinations represented a different projection of the time-varying vector generating the electrical field within the heart. Hence one of such placements is called a lead (which does NOT refer to the wires used!). Since some leads gave better views of the heart than other, based upon the numerous experiments performed, Einthoven eventually established three standard "limb leads" as listed in Table 2-1[14]. The value of each lead is calculated by subtracting the electrical potential on negative electrode from that on positive electrode. Table 2-1 Three standard limb leads Lead name Negative electrode Positive electrode Lead I Right arm Left arm Lead II Right arm Left leg Lead III Left arm Left leg These three common limb leads are often shown by a diagram called "Einthoven's triangle", (Figure 2.3-A). Sometimes the electrodes are not placed on the limb but instead on almost-equivalent parts of the torso for convenience [14] as shown in Figure 2.3-B,
  12. 12. - 8 - (A) Limb leads on the limbs [15] (B) Limb leads on the torso [16] Figure (2.3) Einthoven's triangle Figure 2.5 shows a complete representation for the modern standard 12-channel ECG electrode placement. Except the above limb leads, this widely adopted configuration incorporates three additional augmented limb leads known as aVR, aVL and aVF(aV for augmented lead, R for the right arm, L for the left arm and F for the left foot) and six chest leads, all referencing the Wilsonβ€Ÿs central terminal[5] (the center of the triangle in Figure 2.5-A. The conducting jars are replaced with electrodes which are typically paste filled metal, silver/silver chloride(Ag/Agcl) electrodes attached to the skin using adhesive pads. A typical example of disposable ECG electrode is shown in Figure 2.4, Figure (2.4) Disposable ECG electrode [17] The electrical potentials acquired from those electrodes are then transmitted to an ECG amplifier, where the signals are amplified, filtered and usually analogue to digital converted for heart monitoring, and/or automatic or manual clinical assessment, as part of the diagnosis of any dysfunction. Note that even though modern electrocardiography records "12 channels", there are only ten electrodes on the body. These include 9 recording wires (3 on the limbs [right arm, left arm, and left leg] plus 6 on the chest over various regions surrounding the heart) and a 10th neutral or ground wire attached to the right leg. The specific values of the three limb leads and augmented limb leads can be derived from the calculation process expressed in Figure
  13. 13. - 9 - 2.5-A and the chest leads are obtained by subtracting the electrical potential at the Wilsonβ€Ÿs central terminal [5] from those measured by the six chest electrodes. (A)Limb leads [18] (B) Chest leads [19] Figure (2.5) 12 leads ECG electrodes placement 2.2.2 ECG Morphology A typical ECG waveform corresponding to two cardiac cycles is shown in Figure 2.6. It consists of a series of separate waves (P wave, QRS complex and T wave) corresponding to different periods of the electrical activity of the heart. The P wave is produced by atrial depolarization caused by the electrical impulse traveling from the SA node towards to the AV node. The QRS complex (which is a combination of Q, R and S waves) represents the rapid depolarization of both ventricles and the largest amplitude results from their largest muscle mass. The subsequent T wave is produced by ventricular repolarization. The significant features of the ECG signal are the individual waveforms and their durations such as the P-R, S-T, QRS and Q-T intervals [20]. The amplitude of the ECG is normally 0.1 to 3 mV, with a frequency range of 0.05 to 100 Hz [21]. Although ECG signals have similar morphology, it should also be noted that each patientβ€Ÿs ECG is unique. The patientβ€Ÿs physical size, skin type, age, and pathology as well as where the electrodes are placed, influence the results of the tracing. Figure (2.6) Typical ECG waveform [8]
  14. 14. - 10 - Figure 2.7 shows the typical outputs of a standard 12-lead ECG acquisition system. It can be observed that the waveshape of each channel changes from one to another. A well trained cardiologist will be able to deduce the 3D orientation of the cardiac electrical vector by analyzing the waveshapes in the six limb leads. Cardiac defects, if any, may also be localized by analyzing six chest leads [5]. Figure (2.7) Typical 12-lead ECG waveform [23] 2.3 Artefact in ECG The ECG measurement environment is never perfect, the heartβ€Ÿs commonly small electrical signals can be affected by a number of disturbances that are of similar frequency and often larger amplitudes. These types of artefact can seriously degrade the signal quality and frequency resolution or mask important features necessary for accurate clinical monitoring and diagnosis. Major sources of artefact are: A) Movement Artefact During long term mobile or ambulatory ECG monitoring like the Holter Recording where patientsβ€Ÿ daily routine is not interfered with, body movements or exercise can introduce distortion to the recorded ECG, known as motion artefact. Up to now, there is still no unanimous definitive physical model for this effect, only empirical models have been proposed to illustrate possible mechanisms for its generation [31, 32]. Previous research on the likely origins of motion artefact are summarized in section 2.4. Motion artefact shares the same frequency spectrum as that of ECG signals and its shape can be very similar to the ECG wave events. It is also of time-varying nature. Therefore, it is particularly difficult to reliably detect and remove this artifact only using the ECG signal or conventional fixed parameter filters. Novel signal processing approaches such as adaptive filtering are thus studied extensively in this project. B) Electromyographic Artefact (EMG artefact) Changes of electrical potentials (EMG) caused by muscle activities (not heart muscles)
  15. 15. - 11 - in the vicinity of electrodes might also be picked up during ECG acquisition period, contaminating the ECG morphology [6]. The EMG signals appear on the monitor as narrow, rapid spikes associated with muscle movement, being able to render the ECG recording completely useless [24]. Despite the fact that these signals mostly resides in a frequency range higher than that of the ECG signal and can be sufficiently suppressed from the trace though fixed filters, modern ECG measurement practice still carefully chooses electrodes positions where muscular interference is negligible. C) Power Line Interference Power line interference comes from the feeding lines of measurement systems and can be either 50Hz or 60Hz depending on different countries. It occurs through two mechanisms: capacitive and inductive coupling [6]. Capacitive coupling refers to the transfer of energy between two circuits by means of a coupling capacitance present between them, while inductive coupling is caused by mutual inductance. Typically, capacitive coupling produces high frequency artefact while inductive coupling introduces low frequency noise. For this reason inductive coupling is the dominant mechanism for electrical interference in ECG monitoring. With proper screening, its effect can be decreased significantly but not eliminated. D) Electrode Contact Noise Electrode contact noise is caused by variations in the position of the heart with respect to the electrodes and changes in the propagation medium between the heart and the electrodes [6]. Such variability is usually due to improper contact of the electrodes which interrupts for a short period the connection between patient and measuring system. The loss of contact can be permanent or intermittent, as in case when a loose electrode is brought in and out of connection with the patientβ€Ÿs skin [24]. This switching action at the input of measuring apparatus causes sudden changes in the amplitude of the ECG signal, which can be as high as the maximal recorder output with duration of a few seconds. In addition, poor conductivity between the electrodes and the skin reduces the amplitude of the ECG signal and increases the probability of disturbances [6]. E) Instrument Noise The electrical equipment used in ECG measurements also contributes noise. This is mainly from the electrode probes, cables, the amplifier and the analog-to-digital converter [6]. Unfortunately instrumentation noise cannot be eliminated as it is inherent in electronic components, but it can be reduced through more advanced equipment and careful circuit design. 2.4 Origins of MotionArtefact The origins of motion artefact have been studied by many researchers, the most widely accepted explanation comes from Tam and Webster [25] concluding that the changes in skin potential caused by skin deformation was the major source of motion
  16. 16. - 12 - artifact. The electrode/paste interface did not introduce motion artefact of any significance when recessed Ag-AgCl electrodes were used. One year later, Burbank and Webster [26] studied the properties of skin stretch artefact in relation with skin stretch history, stretch force, stretch time and electrolyte concentration. It was quantified that skin stretch induced artifact in biopotential recording can reach up to 17 mV when the skin strain is 6%. They also further demonstrated that motion artefact resulted largely from potential generator within the skin and changes in skin impedance only played a minor role. Odman and Oberg [27] researched movement- induced potentials in various electrode placements and found that potentials generated by skin deformation beneath the electrodes dominate the disturbance pattern in ECG recording. The streaming potential created by gel movements at the electrode disc was negligible but higher potential was recorded from gel movement relative to the skin. In addition, the potentials did not depend on impedance variations but were highly related to specific mechanical design. Webster [28] reviewed various types of artifacts in biopotential recording, the sources and the means for minimizing them. He concluded that the major motion artifact in ECG recording arises from the skin and not the electrode. 2.4.1 Skin Anatomy and Skin Potential For its direct relationship with motion artefact, it is necessary to investigate the structure of the skin and more importantly, how skin potential is generated and changed during motion induced deformation. Figure 2.7 shows that the skin consists of three layers: the epidermis, the dermis and the subcutaneous layer. The epidermis is made up of another three layers: stratum corneum, stratum granulosum and the stratum germinativum. The surface layer, stratum corneum, is a layer consists of dead cells, with a thickness between 0.02 mm and 0.7 mm [25]. New cells form within the germinating layer (stratum germinativum) and over about 30 days move outward through the barrier layer (stratum granulosum) to the stratum corneum on the surface, and then fall of [28]. Figure (2.7) Skin anatomy [29] The potential difference that exists between the inside and the outside of the skin is believed to be the sum of potentials generated by each layer. The greatest potential has
  17. 17. - 13 - been shown to exist across the barrier membrane between the horny layer and the granular layer [25]. Although the consensus is that the electrical behavior of the skin can be represented by a system of resistors, capacitors and generators, there is no unanimous definitive physical model but many empirical mechanisms have been suggested. Thakor and Webster [30] and Talhouet and Webster [31] approximated the skin layers as an equivalent circuit model consisting of resistors and a current supply (Figure 2.8). Thakor and Webster [30] hypothesized that the skin potential arises from a constant current source called β€žinjury currentβ€Ÿ flowing through the extracellular resistance. This injury current results from the difference of metabolic activity between the dead cells of the stratum corneum and the viable cells of the inner layers of the skin. Talhouet and Webster [31] validated this model through experiments, and quantified the change in epidermal skin impedance and skin potential through stretched force which was applied with hung weights. It was found that both the skin potential change and skin impedance change increase with weight in a logarithmic manner. The decrease of impedance of the transitional region shunted by the current was attributed to the extracellular channels increase in diameter when stretching the skin. Figure (2.8) Equivalent electrical model of skin [20]. Comert et al. [32] propose a complete electrode-skin model for both the gelled electrodes and dry electrodes, as shown in Figure 2.9. For gelled electrodes, the electrolyte has high conductivity and acts as a resistor, while for dry electrodes, the contact area may change considerably with applied pressure and motion, affecting the electrical properties of the electrode-skin interface. The half-cell potential, caused by the ion concentration gradient between the electrode and electrolyte, is denoted as πΈβ„Žπ‘. The skin potential, together with the epidermis is modeled as a voltage source in series with a parallel circuit consisting of a resistor and capacitor. In the presence of sweat, the sweat ducts start acting as a current pathway, and their contribution is modeled as Esw in series with the parallel Rsw and Csw.
  18. 18. - 14 - Figure (2.9) Complete electrode/skin interface model [32]
  19. 19. - 15 - Chapter 3 A Review of Adaptive FilterTheories and its Potentialfor Motion Artefact Reduction 3.1 Introduction Given the potential benefits and significance of understanding the origin and characteristics of the motion artefact, a wide variety of feasible methods for its cancellation have been proposed and investigated in the literature. Basically, they can be divided as non-signal processing approaches like skin abrasion or puncture [25,26] and novel electrode design [32] while signal processing approaches include discrete wavelet transform [36], principle component analysis [22], independent component analysis [37] and adaptive filtering [39,40,41,42]. The method which was focused upon in this project is adaptive filtering and this chapter provides a brief review of important adaptive filter theories that will form the basis to understand and construct practical algorithms for motion artefact cancellation. The aim of this review is to provide an explanation of the adaptive methods at a level which is suitable for both engineers and biomedical researchers. First, some preliminary principles of adaptive filtering techniques are presented. Then, linear adaptive filter theories are introduced as they are the most popular models studied before and are easier to understand. The collection of previous achievements which concludes this section is extended by incorporating the more complex nonlinear adaptive models and algorithms, as linear ones have often been shown to leave some of the motion artefact unaffected [3,4]. This may result from nonlinear effects in the motion artefact generation process and the inclusion of nonlinear models can provide a more comprehensive and rigorous evaluation of adaptive methods. 3.2 Adaptive Filter Theories In signal processing and control applications where the signals and transfer functions involved are time invariant and well specified at design stage, implementing fixed filters and controllers to achieve the desired design goals is sufficient. However, in situations where the specifications are not available or time varying, like the movement artefact model in ECG corruption, it is necessary to construct adaptive systems that are capable of self-adjustment in the changing environment. Two key components of such systems are filter structure and adaptive algorithm. Their self-learning capability is usually achieved by controlling the coefficients of a digital filter so as to optimize predefined quantities or cost functions through adaptive
  20. 20. - 16 - algorithms. The general configuration of an adaptive filtering system is illustrated in Figure 3.1, Figure (3.1) General adaptive filter configuration where the index n denotes the number of iterations, x(n) is the primary input signal, y(n) is the adaptive filter output signal and d(n) represents the desired signal. The error signal e(n), calculated as d(n)-y(n), is fed back through the adaptive algorithms to update the filter coefficients for the next iteration, aiming to decrease the cost functions. The minimization of predetermined cost functions implies the arrival of optimum state of the adaptive filter in some statistical sense. Depending on the various definitions of objective functions and realizations of filter structures, different adaptive algorithms have been developed. Additionally, distinct interpretations and manipulations of the filter inputs and outputs give rise to four main types of applications of adaptive filtering which are system identification, inverse modeling, signal prediction and interference cancellation. As the purpose of this project is to reduce as much as possible the motion artefact, the configuration of adaptive noise cancellation, shown in Figure 3.2, was adopted. The primary sensors, i.e. disposable electrodes, supply the original signal of interest buried in artefact and the reference sensors presumably supply only the noise. It is assumed that the output of the reference sensor is highly correlated with the motion artefact but uncorrelated with the desired ECG signal. An estimate of the artefact can thus be obtained from the reference signal through the adaptive filter, which is then subtracted from the corrupted ECG. The overall output of the noise canceller i.e. the modified ECG, is fed back to adjust the tap weights in the filter, trying to produce a best estimate of the desired ECG signal in some statistical sense. One point that needs more attention is the possible confusion between the naming preference in adaptive filtering theories and the meanings of the signals processed in this project. What is usually denoted as desired signal is actually the corrupted ECG and the error signal represents the reconstructed ECG, as indicated in Figure 3.2. Adaptive Filter Adaptive Algorithm x(n) y(n) e(n) d(n)
  21. 21. - 17 - Figure (3.2) Schematic of adaptive noise cancellation The adaptive algorithms are normally of recursive forms which start from some predetermined set of initial conditions, representing any prior knowledge of the environment. In a stationary environment, it has been shown that the filter coefficients are able to converge to the optimum Wiener solution after a certain number of iterations of the algorithms. In non-stationary environments, however, the tracking capability varies depending upon specific algorithm employed. As a direct consequence of the parameters being updated from one iteration to the next, the filter coefficients become data dependent. This means that an adaptive filter is, strictly speaking, a nonlinear system in the sense that it does not obey the principle of superposition. Notwithstanding this property, adaptive filters are commonly classified as linear if its input-output relationship obeys the principle of superposition whenever its parameters are fixed. Otherwise, the adaptive filters are said to be nonlinear [34]. 3.2.1 Linear Adaptive Filters The filtering process can be implemented in a number of different structures. Basically, linear adaptive filters can be divided into two major classes: finite duration impulse response (FIR) filter and infinite duration impulse response (IIR) filter. Popular FIR filter structures include transversal filter realized through a tapped delay line, lattice predictor consisting of a number of individual lattice stage and systolic array which represents a parallel computing networks ideally suited for mapping a number of important liner algebra computations. Canonic direct form IIR filters have also attracted much interest. The choice of different structure can have profound influences on filter performances such as computational complexity, stability and speed of convergence. In this project, we only focus on casual FIR adaptive filters implemented through a transversal structure, as they are the simplest model to begin with and have been proved effective in numerous applications. The block diagram of a typical transversal adaptive filter is displayed in Figure 3.3, Adaptive Filter Adaptive Algorithm r(n) y(n) e(n) Primary Signal: d(n)=s(n)+a(n) Reference Signal Desired Signal Motion Artefact
  22. 22. - 18 - Figure (3.3) Transversal adaptive filter. The number of delay elements, M, is referred to as the filter order. The output of such a transversal filter is given by, 𝑦( 𝑛) = βˆ‘ 𝑀 π‘˜ π‘₯(𝑛 βˆ’ π‘˜)π‘˜=π‘€βˆ’1 π‘˜=0 (3.1) where 𝑀 π‘˜is the tap weights, x(n) is the reference input, y(n) represents the estimated motion artefact and e(n)is the filtered ECG signal. Adaptive algorithms are the mechanisms by which the filter coefficients are updated. There is no unique solution to this problem and a huge number of algorithms have been developed to a wide variety of filter structures. Each of them offers desirable features of its own such as rate of convergence, misadjustments, tracking capability, robustness or computational complexity. From this immensity, two basic underlying methodologies can be identified for deriving linear adaptive filter algorithms. They are stochastic gradient approach and least-square approach, giving rise to the Least -Mean-Square (LMS) based algorithms and the Recursive Least-Squares (RLS) based algorithms respectively. Performances and properties of these algorithms have investigated and discussed in numerous textbooks and papers, thus a detail description is not repeated and only a quick overview is provide. Interested readers can refer to [34]. The cost function of the stochastic gradient approach is defined as the mean-square error (the mean square value of the difference between the desired response and the transversal filter output). This is a quadratic function of the tap weights and its bottom point is approached through a recursive algorithm based on the method of steepest descent. Practical implementation of this method results in the widely known least- mean-square (LMS) algorithm, whose conventional version can be expressed by the following set of equations, π‘§βˆ’1 π‘§βˆ’1 Adaptive Algorithm 𝑀0 𝑀1 𝑀2 π‘§βˆ’1 𝑀 π‘€βˆ’1 ⬚ ⬚ ⬚ ⬚ x(n) x(n-1) x(n-M+1) y(n) d(n) e(n)
  23. 23. - 19 - 𝑦( 𝑛) = 𝑾 𝑇( 𝑛) 𝑿( 𝑛) (3.2) 𝑒( 𝑛) = 𝑑( 𝑛) βˆ’ 𝑦(𝑛) (3.3) 𝑾( 𝑛 + 1) = 𝑾( 𝑛) + πœ‡π‘Ώ(𝑛)𝑒(𝑛) (3.4) where n is the number of iteration, 𝑿(𝑛) is the reference input column vector, 𝑾(𝑛) is the filter weights column vector at the nth iteration, d(n) is the primary input (Noisy ECG), y(n) is the filter output (Estimated Artefact), e(n) is the error signal (Reconstructed ECG) and πœ‡ is the step size parameter. The value of the step size parameter controls the rate of convergence and filter stability. The necessary condition for the mean square stability of LMS filters with moderate or large length is given by [34], 0 < πœ‡ < 2 𝑀𝑆 π‘šπ‘Ž π‘₯ (3.5) where 𝑆 π‘šπ‘Žπ‘₯ is the maximum value of the power spectral density of the input signal and M is the filter order. The LMS algorithm estimates the real gradient vector from the instantaneous correlation matrix and cross correlation vector, introducing gradient noise. The filter coefficients computed this way execute a random motion around the minimum point of the error performance surface, instead of terminating on the optimal Wiener solution. But the LMS algorithm is still the most widely adopted algorithm for its computational simplicity and proof of convergence in the stationary environment. In a non-stationary environment, the LMS algorithm is able to continually track the bottom of the error performance surface, provided that the input data vary slowly compared with the learning rate of the LMS algorithm [34]. Another approach to the problem of optimal filtering is least square estimation whose cost function is defined as the sum of weighted error squares. The recursive realization of the method of least squares, based upon matrix inversion lemma, results in the RLS algorithm. It belongs to the quasi-Newton adaptive algorithms and is more difficult to implement in real time than the LMS algorithm since it requires more calculations in order to provide the recovered signal. The standard RLS algorithm can be described by the following set of equations, π‘Ÿ( 𝑛) = 𝑷(𝑛 βˆ’ 1)𝑿(𝑛) (3.6) π’Œ( 𝑛) = π‘Ÿ(𝑛) 𝑿 (𝑛)π‘Ÿ(𝑛) (3.7) 𝑒( 𝑛) = 𝑑( 𝑛) βˆ’ 𝑾 (𝑛 βˆ’ 1)𝑿(𝑛) (3.8) 𝑾(n) = 𝑾( 𝑛 βˆ’ 1) + π’Œ(𝑛)𝑒(𝑛) (3.9) 𝑷( 𝑛) = βˆ’1 𝑷( 𝑛 βˆ’ 1) βˆ’ βˆ’1 π’Œ( 𝑛) 𝑿 (𝑛)𝑷(𝑛 βˆ’ 1) (3.10) where n is the number of iteration, 𝑿(𝑛) is the reference input column vector, π’Œ( 𝑛) is the gain vector, 𝑾(𝑛) is the filter weights column vector at the nth iteration, d(n) is
  24. 24. - 20 - the primary input (Noisy ECG) and e(n) is the error signal (Reconstructed ECG). The computation of w is based on a priori estimation error e and the gain vector k. The matrix P is the recursive estimation of the inverse of the correlation matrix of the input reference. The Ξ», the forgetting factor (0 < Ξ» ≀ 1), controls the rate of adaptation of the algorithm. The fundamental difference between RLS and LMS algorithm is that the step-size parameter is replaced by the inverse of the correlation matrix of the input vector. Because of this modification, the rate of convergence of the RLS algorithm is typically one order of magnitude faster than that of the LMS algorithm and also invariant to the eigenvalue spread of the ensemble average correlation matrix of the input vector. Although the RLS algorithm is more computationally demanding, the faster convergence speed is highly desirable in the current study as the motion artefact could be rapidly changing in some situations. Besides, since practical implementation is not a major concern of this project, LMS adaptive filters were only validated in computer simulations while the RLS adaptive filters were applied to both emulated ECGs and experimentally measured ECGs. 3.2.2 Nonlinear Adaptive Filters A nonlinear filter is involves a structure that produces an output which is not a linear function of the inputs. It offers a solution to model system nonlinearities when linear assumption is not sufficient or fails completely. Evidence is available showing that the mechanical stress applied and the resultant skin potential variations follow a nonlinear relationship [31]. This serves the main motivation for extending previous linear adaptive motion artefact reduction scheme to nonlinear ones. A major drawback of nonlinear structures is the increased model complexity and lack of mathematical tools, originating from the high degrees and dimensionality of the nonlinearities [35]. But this should not be a serious problem for this project as all sets of recorded data were processed offline. Popular techniques for implementing nonlinear adaptive filters include the non-recursive polynomial model based on the Volterra series expansion, the recursive polynomial model based on nonlinear difference equations, the multilayer perception neural network and the radial basis function neural network [35]. The nonlinear structures investigated in this project are three Cascade models (LN, NL, LNL) which consist of dynamic linear and static nonlinear elements (Figure 3.4). They were considered instead of Volterra or Wiener series because they provide a parsimonious and efficient method of increasing the level of nonlinearity and overcome the restriction of requiring large amounts of data to obtain robust parameter estimates. Previous work [44, 45] has also shown them to be effective in representing nonlinear biological systems. Moreover, cascade models simplify the analysis of nonlinear response of systems since their nonlinear polynomial element remains in two dimensional space regardless of their order. As they are subsets of the Volterra series, it is also possible to transform their parameters into Volterra form. The block diagrams of the three cascade models are shown in Figure 3.4.
  25. 25. - 21 - The LN (Linear Nonlinear) or Wiener model, consists of dynamic linear element followed by a static nonlinearity, which is chosen to be a polynomial function given by, π‘š(π‘₯( 𝑑)) = βˆ‘ 𝑐( π‘ž) π‘₯ π‘ž (𝑑) 𝑄 π‘ž=0 (3.10) Where Q is the polynomial order, c its coefficients and x the input signal to the nonlinearity. The output of the LN model is thus calculated as, 𝑦( 𝑑) = βˆ‘ 𝑐( π‘ž) (βˆ‘ β„Ž(𝜏)𝑒(𝑑 βˆ’ 𝜏)π‘‡βˆ’1 𝜏=0 ) π‘žπ‘„ π‘ž=0 (3.11) The NL (Nonlinear Linear) or Hammerstein model consists of a static nonlinearity followed by a dynamic linear element. Its output is given by, 𝑦( 𝑑) = βˆ‘ β„Ž(𝜏)(βˆ‘ 𝑐(π‘ž) 𝑒(𝑑 βˆ’ 𝜏) π‘žπ‘„ π‘ž=0 )π‘‡βˆ’1 𝜏=0 (3.12) The LNL (Linear Nonlinear Linear) or Wiener Hammerstein model consists of two dynamic linear elements h(𝜏) and g(𝜎), separated by a static nonlinear element. Its model output can be written as, 𝑦( 𝑑) = βˆ‘ 𝑔(𝜎)π‘‡βˆ’1 𝜎=0 βˆ‘ 𝑐( π‘ž) (βˆ‘ β„Ž(𝜏)𝑒(𝑑 βˆ’ 𝜎 βˆ’ 𝜏)π‘‡βˆ’1 𝜏=0 ) π‘žπ‘„ π‘ž=0 (3.13) Figure (3.4) Block diagrams of (A) LN, (B) NL and (C) LNL cascade models [46] . One of the most widely used iterative methods for estimating the parameters of these block structures was developed by Korenberg and Hunter [46,47], referred to as KH method in this project. They minimized the cost functions using Levenberg Marquart gradient descent method which combines the robust convergence properties of steepest descent method with the faster convergence of the Gauss Newton method [49,
  26. 26. - 22 - 50]. As there are already many discussions on these algorithms, only their summaries [46] are provided in Table 3-1, Table 3-2 and Table 3-3 for LN, NL and LNL models respectively. Table 3-1 [46] Table 3-2 [46] Table 3-3 [46] The method adopted in the current study is a variant of the standard KH method, called Smoothed Korenberg Hunter method which was developed in [46]. It estimates the linear elements of the cascade models using the least square method instead of the cross correlation approach as this approach is less dependent on the properties of the
  27. 27. - 23 - input signal. It also incorporates a Butterworth low pass filter to reduce the estimation error in the linear elements of the LN and NL models and in the second linear element of the LNL model. 3.3 Areview of Adaptive Motion Artefact Reductionin ECG signals The adaptive methods, mainly the linear ones have been investigated in numerous studies [39,40,41,42] to suppress various types of interferences in ECG recordings such as, power line disturbance, baseline wander and motion artefact, all showing promising results. As adaptive methods were adopted exclusively for motion artefact reduction in this study, a brief overview of similar previous work is presented in this section. One necessary step of adaptive interference cancellation is the recording of an additional channel of reference signal which is assumed to be highly correlated with the noise in some unknown manner but uncorrelated with the ECG measurements. One of the major differences between various adaptive filtering schemes proposed previously is the choice of relevant reference signal. Skin-electrode impedance [39], electrode acceleration [40], and skin strain [20] have all been demonstrated to be effective in representing motion artefact superimposed on ECGs. But less evidence is available showing that these algorithms could reduce the motion artefact in a consistent manner for all combinations of electrodes placements and movement types. This is possibly due to the fact that different combinations of electrodes arrangements and motions introduced produce reference signals of various qualities in the sense that some reference channels have a higher correlation with the artefact components than other. Hence different performances occur. An example of adaptive motion artefact reduction in ECG signal is shown in Figure 3.5, Figure (3.5) An example of adaptive motion artefact removal [38] Devlin et al. [39] used electrode / skin impedance variations to estimate and reduce motion artifact in ECG signals, so as to reduce false positive alarms. The time-varying impedance of the electrode / skin interface was monitored by passing a small AC current through the ECG electrode and measuring the resultant potential difference across the electrodes. Artifacts were introduced by scratching and tugging on the electrodes, and abrupt physical movements. The results showed that 82% of the false positive alarms can be eliminated, with a loss of 7% of the true positives. However,
  28. 28. - 24 - the torso impedance will introduce the possibility of impedance artifact generated by breathing or heart pumping. Tong et al. [40] compared two other kinds of reference signals which are obtained by a 2-axis anisotrophic magnetoresistive (AMR) sensor and a 3-axis accelerometer (ACC) sensor fabricated on the electrodes, as shown in Figure 3.6. ACC sensor was shown to perform better than the AMR sensor and one possible reason is that ACC signal is more linearly correlated with the motion artefact than the AMR output. Motion artifact was induced at the left arm electrode site using three methods: (1) pressing on the electrode; (2) pushing on the skin around the electrode and (3) pulling on the electrode lead wire. In one of the data sets, all of these motions were introduced simultaneously. Performance of the adaptive method was evaluated by the signal L2 norm and MaxMin statistic before and after filtering. A third percentage improvement showed that the overall noise reduction rate ranged from 24% to 91% depending upon the motion type and the reference sensor. (a) AMR sensor (b) ACC sensor Figure (3.6) Integration of a motion sensorinto an electrode [40] Based on the conclusion that motion artefact resulted from skin potential changes caused by skin deformation [26], Hamilton et al. [41] mounted a miniature displacement sensor on the ECG electrode to measure the skin stretch signal directly. It was assumed that this reference signal should more reliably reduce motion artifact since it was responsible for artefact generation. Motion interference was manually generated by pulling on either side of the skin and pushing on the center of the electrode. The results indicated that adaptive filtering could effectively suppress motion artifact. Filters with three or four coefficients achieved considerable reduction but higher orders didnβ€Ÿt contribute greatly to further interference cancellation. The applicability of the approach was limited by the expensive sensors required at that time, but this may not be a serious problem under current standards. Possibly inspired by the method adopted by Devlin and Mark [40] mentioned above, Hamilton and Curley [42] compared the effectiveness of sensor based to impedance based adaptive motion artefact removal. Instead of measuring 20kHz impedance signal, they used lower frequency impedance fluctuations since lower frequency impedance should be more sensitive to changes in skin impedance and less sensitive
  29. 29. - 25 - to changes in torso impedance. The displacement sensors were also replaced by optical bend sensors, measuring the skin stretch signal around the electrode. For each recording, motion artefact were induced by pressing directly on the electrode attached to the forearm. The experiments showed an inverse correlation between artifact attenuation and impedance frequency and impedance based algorithms produced similar performance to sensor based methods. Although artifact reduction increased with the order of the filter, the improvement was not obvious for filter orders higher than four. In contrast to attaching motion sensors on the electrodes, Raya and Sison [2] fixed either uniaxial or dual-axial accelerometer on the subjectβ€Ÿs lower back, at the lumbosacral level. Both the lead β…  ECG and accelerometer data are recorded simultaneously and band limited to retain only the necessary information while the subject was cycling on the bicycle ergometer. It was found that a single axis noise reference produced better results than dual-axis noise reference irrespective of the adaptive filtering algorithms applied. One possible explanation is that major kinematic acceleration component is usually found in the vertical direction. Although simple Lease Mean Square (LMS) algorithm posed less computational load, recursive least square (RLS) algorithm produced a superior performance. RLS converges faster than LMS and it can track the rapidly varying environment compared with LMS. In Luo and Tompkinsβ€Ÿ [43] research, the auxiliary input for adaptive filtering was the motion noise signal obtained from the voltage difference between two adjacent electrodes located on the arm, near the right biceps muscle. The artifacts were induced by raising the right lower arm from a relaxed position until the lower arm makes contact with the brachium. To validate the hypothesis that the dominant disturbance is localized in the region immediately surrounding the electrodes, two types of motion artefact were measured from two different electrodes placements while the primary input remained the same standard leadβ…‘, as shown in Figure 3.7. The reference input for the upper adaptive filter is the potential difference between electrode 1and 3 while that for the lower system is the difference between electrode 4 and 3. The experiment results proved the above assumption and the performance of RLS and LMS algorithm was also compared. It was found that RLS algorithm converged much faster than the LMS algorithm which is particularly advantageous in reducing rapidly varying brachial artifact. Furthermore, the RLS algorithm produced a considerable enhancement on low frequency baseline wander.
  30. 30. - 26 - Figure (3.7) Comparison of different motion noise references [43] All of the above studies base their artefact reduction systems on linear adaptive structures which have been shown to produce considerable signal enhancement. To the best of the authorβ€Ÿs knowledge, nonlinear systems have not been used to suppress motion artefact in ECG signals. However, it is possible that nonlinear adaptive filters can perform as well as or even better than linear ones under certain circumstances. In fact, the residual motion artifact, observed in all the above-mentioned studies, may result from the nonlinear relationship between skin stretch and motion artifacts. It has been shown that the skin potential variations changes with the stretch force in a logarithmic manner [31]. This is the major motivation for including nonlinear structures and relevant algorithms for motion artefact reduction in electrocardiogram in the current study. Examples of nonlinear noise reduction can be found in other applications, e.g powerline interference where neural network [51, 52] or fuzzy rule based adaptive nonlinear filter [53,54] have been explored. Nonlinear Bayesian filtering technique has also been applied for denoising ECG signals acquired in a magnetic resonance environment [55].
  31. 31. - 27 - Chapter 4 ECG Signal Acquisitionand Motion Artefact Generation 4.1 Introduction The content of this chapter provides an explanation of the experimental methods adopted for ECG signal acquisition and motion artefact generation. The objective of the experiment is to introduce motion artefact to ECG signals through controlled vibration of an electromagnetic shaker and voluntary motions of the subjects. This combination of movements had been used in previous undergraduate [3] and MSc [4] projects. The vibration of the shaker and the motion of the subjectβ€Ÿs hand was monitored and recorded simultaneously by single-axis accelerometer and tri-axial accelerometer respectively. These accelerations serve as reference signals to adaptively reduce motion artefact in chapter 5. To quantitatively evaluate and compare the effectiveness of different adaptive algorithms, another channel of the same lead ECG was acquired with electrodes on the chest that were negligibly affected the subjectβ€Ÿs motions. The reconstructed ECG signals were compared with this presumably ideal ECG template to determine how much motion artefact had been removed. This chapter commences by describing the equipment preparation & stimulus signal generation process. Next, the methods used to calibrate the shaker accelerometer system and relevant results are presented. It concludes by explaining the experimental procedures of ECG corruption and acceleration signals acquisition.
  32. 32. - 28 - 4.2 Methods 4.2.1 Equipment Set Up The set of equipment used in this experiment can be divided into two subsystems: vibration module and ECG acquisition module. The vibration module, aiming to introduce controlled motion through the subjectβ€Ÿs hand, consists of a computer, D/A converter, analog filter, shaker amplifier and shaker while the ECG signal is recorded using the Data Acquisition System produced by Biopac Systems, USA [66]. The vertical acceleration of the shaker, analog filter output, amplifier output and tri-axial acceleration of the subjectβ€Ÿs hand were also recorded simultaneously with ECG channels by its integrated analog to digital converter module and stored in the same computer. The block diagram and a photograph of the equipment set up are shown in Figure 4.1 and Figure 4.2 respectively. The β€žsig-accβ€Ÿ in Figure 4.1 denotes the vertical acceleration of the shaker while β€žTri-accβ€Ÿ the three axis accelerations of the subjectβ€Ÿs right hand. Figure (4.1) Block diagram of the system set-up Figure (4.2) Photograph of the system set-up A detailed explanation of each component and its functionality in the system is presented below, Computer USB-1408FS Analog Output Analog Filter Amplifier Shaker Sig-acc Subject Right Hand Signal Conditioner ECG 100C Amplifier Biopack Data Acquisition System Stimulus Signal Recorded ECG Tri-acc
  33. 33. - 29 - Vibration module: 1. USB-1408FS The stimulus signals generated in MATLAB are of digital format. They were converted to analog signals using D/A convertor, USB- 1408FS (Figure 4.3), produced by Measure- ment Computing in Hungary to drive the shaker. Figure (4.3) USB-1408FS 2. Analog low pass Filter Dual channel elliptic filter, type: VBF/23, made by Kemo Limited Electronic, UK. The voltage output of the USB-1408FS always contains harmonics of the 15 Hz sinusoidal excitation signal because of the D/A conversion process. Before the stimulus was sent to the shaker, an analog low pass filter was use to attenuate these harmonics and transmit only the fundamental frequency component. 3. Shaker Amplifier TPA series power amplifier manufactured by HH electronics, UK. The filtered stimulus signals were amplified by this amplifier to drive the shaker with the determined magnitude. 4. Shaker with a horizontal handle An electromagnetic shaker (Figure 4.4) with a horizontal handle was used to introduce controlled vibration to the subjects through the right hands. 5. Single Axis Accelerometer 6. Tri-Axial Accelerometer Model type: ADXL330, manufactured by Analog Devices, USA. The small, low power tri-axial accelerometer, shown in red window in Figure 4.5 was fixed onto the electrode to monitor and record the motion of the subjectβ€Ÿs right hand during the experiment. The orientation of its three axes is also shown in the picture using orthogonal lines. Figure (4.5) Tri-axial accelerometer on electrode [3] Figure (4.4) Shaker and single axis accelerometer Model type: 353B52, manufactured by PCB Piezotronics, USA. The vertical vibration of the shaker was monitored and recorded using this single axis accelerometer (Figure 4.4 inside the red oval window) attached on top of it. The readings of this accelerometer were amplified 100 times before being sent to the Biopac data acquisition system.
  34. 34. - 30 - Data Acquisition Module: 1. ECG 100C This an electrocardiogram amplifier for the signals detected by the disposable electrodes. The gain was set to 1000 times in this project. 2. Analog-to-Digital Converter (MP 100 System) This system consists of a universal interface module UIM 100C for organizing the ECG and reference signals and a High performance data acquisition unit MP100ACE for transmitting the digital signals to the computer. 3. Acqknowledge 3.9.0 software This software can simultaneously display and store all channels of signals recorded by the MP 100 system. It is also used to set the working parameters such as sampling frequency and recording duration depending on various motion types involved. 4.2.2 Method of Stimulus Signal Generation The excitation signals of this experiment were generated in MATLAB. The 15Hz sinusoidal stimulus signal, with amplitude values varying from minus one to one volts was generated using function: sin, while the Bandlimited Gaussian White Noise (BLWN) signal using its random number generation function: randn and Butterworth low pass filter. The sampling frequency was set to 2000 Hz. This was determined after a series of trials on different sampling frequencies and then by comparing the power spectra of the resulting output of the DAC. It was found that this was high enough to cause negligible distortions to the outputs of D/A converter. The default settings of the function randn create random numbers from a Gaussian distribution with a mean of zero and a standard deviation of one. This GWN was then forward and backward filtered by a 4th order Butterworth low pass filter with a cut off frequency of 20Hz to avoid any phase delay. The resulting sinusoidal, BLWN stimulus signals were saved into a single vector as an excitation signal. This facilitates the experimental procedure not only because it keeps the vibration intensity the same for all the subjects, but also enables the measurement of the influence of both types of induced movements in a single recording. The temporal fluctuations of the stimulus signals and their power spectra estimates are shown in Figure 4.7. The method for computing these power spectra is Welch method with block size of 8000 and 50% overlap. It can be seen that the band pass filtered GWN had much lower amplitude than the sinusoidal one. Figure (4.6) ECG acquisition module
  35. 35. - 31 - (A) (B) (C) (D) Figure (4.7) Stimulus signals generated using MATLAB functions and their power spectra. (A) Sinusoidal excitation signal and its power spectrum estimate (B). (C) BLWN excitation signal and its power spectrumestimate (D). To induce a sufficient amount of motion artefact during the sinusoidal input, the amplification level was set to introduce the maximum acceleration permitted by the university ethics committee (Appendix B). But the wide dynamic range of the bandlimited GWN creates practical difficulties for the shaker amplifier and the shaker because the huge spikes in the BLWN will almost certainly drive the shaker out of its vibrating limits at this intensity, pushing the handle to strike the upper and lower bounds of the cylinder. This will introduce erroneous acceleration readouts and potentially damage the shaker. Although it is possible to manually decrease the amplification level for BLWN stimulus during the recording session, this would probably not able to keep the vibration dose constant to each subject. To solve this problem, the amplitude of this GWN stimulus signal was firstly rescaled to have half as much energy as the sinusoidal signal, as shown in Figure 4.8-A. Next, a small number of points whose values were higher than 1.5 or low than -1.5 were set to half of their original magnitudes to reduce its dynamic spread, resulting in restricted BLWN as shown in Figure 4.8-B, 0 10 20 30 40 50 60 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 Time/s Voltage/V Sinusoidal stimulus signal 0 10 20 30 40 50 60 70 80 90 100 -100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 Frequency/Hz Power/Frequency(dB/Hz) Welch Power Spectral Density Estimate 0 10 20 30 40 50 60 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 Time/s Voltage/V Bandlimited GWN stimulus signal 0 10 20 30 40 50 60 70 80 90 100 -100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 Frequency/Hz Power/Frequency(dB/Hz) Welch Power Spectral Density Estimate
  36. 36. - 32 - (A) (B) Figure (4.8) Modified BLWN stimulus. (A) Rescaled BLWN signal. (B) Restricted BLWN signal. This modification produced negligible effects on its power spectrum and the restricted random signal can still be regarded as white in the frequency range of 0 to 20Hz. The amount of vibration energy introduced to the subject from this modified BLWN signal would be about half as much as that through sinusoidal excitation. To drive the electromagnetic shaker using the above created digital signals. The USB-1408FS D/A converter was connected to the computer via a specially designed MATLAB program which allowed the transmission of excitation signals to the shaker amplifier. The user interface for loading and outputting excitation stimulus is shown below in Figure 4.9, Figure (4.9) Program user interface of the USB-1408FS 4.2.3 Equipment Calibration The purpose of this calibration procedures presented in this section is to make sure that every part of the system functions as expected in the sense that they are able to reproduce the desired controlled motion with insignificant discrepancies. Only the vibration module was calibrated. As the vibration subsystem is made up of four components, the output of every part is recorded and analyzed in time and frequency domain for both the 15 Hz sinusoidal and bandlimited white noise excitations. The amplitudes of these excitation signals were set to the same levels as those introduced to human subjects in the recording sessions. As the motion of the shaker was monitored by a single axis accelerometer, this accelerometer was checked first. The responses of the whole subsystem were calibrated subsequently. 0 10 20 30 40 50 60 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 Time/s Voltage/V Rescaled BLWN stimulus signal 0 10 20 30 40 50 60 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 Time/s Voltage/V Restricted BLWN stimulus signal
  37. 37. - 33 - Calibration of the Single Axis Accelerometer The single axis accelerometer used is model 353B52 produced by PCB Piezotronics, USA. The sensitivity of the one used, as indicated in its calibration card, is 47.1π‘šπ‘£/ 𝑔. To validate the performance of this accelerometer, a one pound coin was placed on top of the shaker where the handle was also mounted, following the practice in [4]. The shaker was then driven by 15 Hz sinusoidal stimulus with varying levels of amplification. When this coin started jumping on the shaker, it means its acceleration had reached that of gravity gβ‰ˆ9.81m/s2 . The measured peak voltages of the output of the accelerometer (Figure 4.10-A) at this point were used to check the calibration. (A) (B) Figure (4.10) Calibration of the single axis accelerometer. The peaks of the voltage output at this vibration intensity were found to be around 47.5π‘šπ‘£/𝑔, which is very similar to the value of the calibration card. Considering the level of uncertainty within the process, especially the determination of the minimal amplitude of the coin to start bouncing, perfect agreement should not be expected. As a result, the previously calculated sensitivity level was adopted in the remaining part of the experiment and a value of 4.8π‘šπ‘£/π‘šπ‘ βˆ’2 was used. The corresponding acceleration values at the bouncing moment are also plotted in Figure 4.10-B. System Calibration with Sinusoidal Excitation Apart from the single axis accelerometer, all the other components of the vibration module were also calibrated. The shaker amplifier was set to the maximally permitted level specified in the ethics form approved by the university ethics committee. There is also the intensity which the subject experienced during the actual recording sessions. The outputs of the D/A converter, analog low pass filter, amplifier and the single axis accelerometer are shown in Figure 4.11. It can be observed that the few number of harmonics at the output of the USB-1408FS (B) were effectively removed by the low pass filter (D), although some were reintroduced by the amplifier (F). From the results in Figure 4.10-H, it is concluded that the shaker exhibited quite strong nonlinear effects which produced a series of higher harmonics. It should be noted that the 50 Hz power line interference is also quite obvious in the power spectrum. This is probably due to the fact the accelerometer is located just above the shaker which is driven by an 0 0.5 1 1.5 2 2.5 3 -60 -40 -20 0 20 40 60 Time/s Voltage/mV Voltage output of single-axis accelerometer 0 0.5 1 1.5 2 2.5 3 -15 -10 -5 0 5 10 15 Time/s Acc/ms-2 Acceleration of the shaker
  38. 38. - 34 - alternating current. This effect was also observed in the noisy limb ECG which was measured while the hand was placed onto the vibrating shaker. The other multiple peaks existing between the large harmonics might result from the aliasing effect of the sampling practice. Although higher harmonics of the fundamental excitation frequency were also noticed in many of the noisy ECG recordings, they are normally much weaker than those present in the acceleration readings. Adaptively reducing the motion artefact of the ECG signals using these as reference inputs can actually corrupt the ECG with those harmonics instead of removing them. This will be seen more clearly chapter 5. In spite of their lower magnitudes, all of the acceleration measurements are band pass and notch filtered as well. (A) (B) (C) (D) (E) (F) (G) (H) Figure (4.11) Time and frequency domain responses of different components of the vibration module for 15 Hz sinusoidal input. (A) Output voltage of DAC and its power spectrum estimate (B). (C) Output voltage of the low pass filter and its power spectrum estimate (D). (E) Output voltage of the amplifier and its power spectrum estimate (F). (G) Output voltage of the shaker accelerometer and its power spectrumestimate (H). 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 Time/s Voltage/V Output voltage of DAC for white noise input 0 10 20 30 40 50 60 70 80 90 100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 X: 15 Y: 1.568 Frequency/Hz Power/Frequency(dB/Hz) Welch Power Spectral Density Estimate 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 Time/s Voltage/V Output voltage of the low pass filter 0 10 20 30 40 50 60 70 80 90 100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 X: 15 Y: 1.459 Frequency/Hz Power/Frequency(dB/Hz) Welch Power Spectral Density Estimate 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 -3 -2 -1 0 1 2 3 Time/s Voltage/V Output voltage of the amplifier 0 10 20 30 40 50 60 70 80 90 100 -80 -70 -60 -50 -40 -30 -20 -10 0 10 Frequency/Hz Power/Frequency(dB/Hz) Welch Power Spectral Density Estimate 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 -20 -15 -10 -5 0 5 10 15 20 Time/s Acc/ms-2 Acceleration of the shaker 0 10 20 30 40 50 60 70 80 90 100 -60 -50 -40 -30 -20 -10 0 10 20 30 X: 15 Y: 23.22 Frequency/Hz Power/Frequency(dB/Hz) Welch Power Spectral Density Estimate X: 30 Y: -7.149 X: 45 Y: -12.41
  39. 39. - 35 - System Calibration with Bandlimited White Noise Excitation The same set of procedures was carried out with bandlimited white noise input. The results of calibration process are shown in Figure 4.12 from which it can be seen that the D/A converter, low pass filter and the shaker amplifier preserve the whiteness of the excitation signal with negligible level of distortion. The shaker, however, attenuated the low frequency components, giving rise to a colored white noise vibration. This could seriously limit the amount of energy transmitted to the subject through random excitation whose energy is already only half as strong as the sinusoidal excitation. The motion artefact are more difficult to reveal in the ECG recording measured under such condition, as are discussed more rigorously in later chapters. (A) (B) (C) (D) (E) (F) (G) (H) Figure (4.12) Time and frequency domain responses of different components of the vibration module for bandlimited white noise input. (A) Output voltage of DAC and its power spectrumestimate (B). (C) Output voltage of the low pass filter and its power spectrum estimate (D). (E) Output voltage of the amplifier and its power spectrum estimate (F). (G) Output voltage of the shaker accelerometer and its power spectrumestimate (H). 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 Time/s Voltage/V Output voltage of DAC for white noise input 0 10 20 30 40 50 60 70 80 90 100 -70 -60 -50 -40 -30 -20 -10 Frequency/Hz Power/Frequency(dB/Hz) Welch Power Spectral Density Estimate 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 -1 -0.5 0 0.5 1 1.5 Time/s Voltage/V Output voltage of the low pass filter 0 10 20 30 40 50 60 70 80 90 100 -80 -70 -60 -50 -40 -30 -20 -10 Frequency/Hz Power/Frequency(dB/Hz) Welch Power Spectral Density Estimate 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 Time/s Voltage/V Output voltage of the amplifier 0 10 20 30 40 50 60 70 80 90 100 -70 -60 -50 -40 -30 -20 -10 Frequency/Hz Power/Frequency(dB/Hz) Welch Power Spectral Density Estimate 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 -20 -15 -10 -5 0 5 10 15 20 Time/s Acc/ms-2 Acceleration of the shaker 0 10 20 30 40 50 60 70 80 90 100 -60 -50 -40 -30 -20 -10 0 10 Frequency/Hz Power/Frequency(dB/Hz) Welch Power Spectral Density Estimate
  40. 40. - 36 - From the perspective of adaptive filtering, the exact manners of how the shaker vibrates simply do not matter as long as we have good quality recordings of the ECG and accelerations. This is because the adaptive algorithms are specifically designed to automatically adjust its parameters depending on different inputs. This means that while the white noise is not white under practical conditions, this does not fundamentally alter the ability to assess the performance of adaptive algorithms. The reason why so much attention has been paid to the behavior of the vibration module is that strange shaker motions can possibly induce erroneous measurements of or erratic noise to both the ECG and acceleration acquisitions from which sensible interpretations of the adaptive methods can never be achieved. Vibration Intensity of the Shaker In addition to checking that every component of the system responds properly to each type of excitation signal, efforts must also be made to ensure that the vibration intensity of the shaker does not exceed the maximum level approved by the university ethics committee. The Root Mean Square (RMS) acceleration of the vibrating handle should be no higher than 10 π‘š/𝑠2 for the 15Hz sinusoidal input and 20 π‘š/𝑠2 for the band limited Gaussian white noise input. Adjusting amplification to different magnitudes, a series of measurements were carried out to reveal the relationship between RMS amplifier voltage output and RMS handle acceleration. In Figure 4.13, it can be seen that the RMS voltage of the amplifier should not be higher than 1.4V to conform to ethics requirement. As has been shown in previous studies [3,4], the motion artefact may not appear effectively in some subjectsβ€Ÿ ECG recordings. The gain of the amplifier was therefore set to just a little less than the maximum permissible level for all the subjects to maximize the possibility of inducing sufficient amount of motion artefact. As the variance of the BLWN stimulus had already been rescaled to half of that of sinusoidal stimulus, there is no need to check the random excitation energy limit. Figure (4.13) Acceleration sensitivity of the shaker 4.2.4 Experimental Procedure A description of the experimental procedures of ECG corruption and reference signal acquisition performed on seven healthy students (six male and one female) is provided in this section. These sets of ECGs and reference recordings were labeled as 0 0.2 0.4 0.6 0.8 1 1.2 1.4 0 1 2 3 4 5 6 7 8 9 10 RMS voltage from the amplifier/V RMSaccelerationoftheshaker/(m/s2 ) Calibration of the shaker for 15Hz sinusoidal input
  41. 41. - 37 - Subject 1 to Subject 7 to protect their identity. Another set of data acquired in a previous project [4] where 10 Hz sine wave vibration was also introduced through two kinds of handles (vertical and horizontal) was taken and labeled as subject 8 in this report. Because of the format of the data, the accelerations recordings of subjects (Subject 1 to 7) measured in this project have the unit of π‘š/𝑠2 while those of subject 8 were rescaled to have arbitrary unit. In order to ensure that each subject is healthy and is willing to have their data collected, a consent form (Appendix C) and a brief health questionnaire (Appendix D) were provide to them to sign and fill in before the start of each experiment. The subjects have the right to withdraw from the experiment at any moment without having to give reasons. Figure 4.14 and Table 4-1 show the schematic and the exact locations of electrodes placement for both Limb and Chest ECG recordings. As shown in Figure 4.14, two channels of the same lead β…‘ ECG were recorded simultaneously with a sampling rate of 200 Hz. Channel one ECG (Limb ECG) which was exposed to motion artefact was measured as the potential difference between the back of the right hand and the left leg, and the tri-axial accelerometer was pasted on top of the electrode by double sided adhesive. Channel two ECG (Chest ECG) was measured as the potential difference between the sternum and the left leg. This pair of electrodes is intended to be unaffected or only negligibly affected by the motion artefact and can thus serve as the β€œideal” template for algorithm evaluation after adaptive filtering. This practice of measuring one channel of noisy ECG and another channel of clean ECG for reference has been adopted by many previous studies such as [3,4,32]. These two-channel ECG recordings result in a total of six disposable electrodes attached to specific skin sites during all the three parts of this experiment. Selected skin sites were sterilized using alcohol-base wipes before electrodes attachment to improve electrical conduction. . Figure (4.14) Placement of electrodes on the subject.Adapted from [20]. Table 4-1 Electrode Locations Positive electrode Negative electrode Ground Channel One Left lower leg Middle of the sternum Left clavicle Channel Two Left lower leg Back of the right hand Left clavicle
  42. 42. - 38 - It should be noted that in most clinical situations, electrodes are seldom placed on hands because of the interference that maybe present. In this project, however, motion artefact are desired, thus such a configuration will facilitate the process of artefact generation. But this may give rise to other types of noise and artefact such as EMG, which could undermine the performance analysis of various adaptive algorithms since they are very likely to be uncorrelated with the acceleration reference signals and hence cannot be reliably eliminated. The raw ECG recordings are hence band pass filtered and notch filtered before any further analysis. In addition, despite of the careful observation of the subjectsβ€Ÿ movements, in some cases, the expected clean channel was also corrupted by the motions, as will be shown in chapter 5. But this would not be a serious problem after the signal preprocessing steps explained in chapter 5. After all of the electrodes have been securely fixed and the equipment has been switched on, every subject experienced a three-session experiment, each of which lasted no longer than 15 minutes. Details of the steps involved in each of these sessions are presented below, Sessionone: Resting state ECG The subjects were asked to remain seated and relax while one minute of their two channels resting state ECG were recorded. Sessiontwo: Shaker vibration The subjects were asked to grasp firmly but not too hard the added mass under the horizontal handle with their right hand while the shaker was driven by the following sequence of stimuli: 1. Two minutes (two sessions of one minute) of 15 Hz sinusoidal excitation. 2. Two minutes (two sessions of one minute) of bandlimited white noise excitation. The two channels of ECG signals were recorded simultaneously during the shaker vibrations. Limits on the permissible vibration intensities are 10 m/s2 for the sinusoidal excitation and 20 m/s2 for the BLWN excitation. Sessionthree: Voluntary motion This session focused on introducing motion artefact from common daily activities. The subjects were asked to take three books out of a bag placed in front of them and then put the bag to another location neat them, mainly using the right hand. This type of arrangement mimics those motions that are commonly carried out by students and are sufficiently large to generate motion artefact. From the preliminary results on these sets of recordings, the performances of adaptive methods were often observed to be very poor. Hence in some cases, the subjects were also asked to randomly and leisurely wave his right hand. Again, two channels of ECG signals and tri-axial accelerations of the right hand were recorded during the subjectβ€Ÿs motion.
  43. 43. - 39 - Chapter 5 Applicationof Adaptive Filters to ECG Motion Artefact Reduction 5.1 Introduction The aim of this chapter is to explore which method performs well under what condition and to then explore whether the relationship between subjectβ€Ÿs motions and resulting artefact can be modeled through the inclusion of nonlinearity. Outcomes of applying adaptive methods to motion artefact reduction are provided. Before experimentally recorded ECG signals were processed, computer simulations were performed to review and validate some important properties of various adaptive filters. This also provided useful guidance on method selection in real life situations. For each type of movement involved in the actual experiment, both linear and nonlinear structures and algorithms were applied to compare their performances and investigate the motion artefact generation process. To evaluate the effectiveness of different adaptive filters, a series of subjective and objective measures were reviewed and discussed. More importantly, the number of parameters each structure should contain was optimized according to the selected quantitative criteria since it can have a considerable effect on model accuracy and computational requirements but, surprisingly, is not emphasized in many previous studies [3, 4, 39, 40]. The contents of this chapter are organized as follows. Firstly, signal preprocessing methods are introduced. Next, results of computer simulations are presented, with their significance and usages briefly summarized. It concludes with adaptive motion artefact reduction in experimentally recorded ECG signals, performance evaluation and parameter analysis.
  44. 44. - 40 - 5.2 Methods This subsection commences with a description of the signal preprocessing methods before adaptive filters were applied. Next, a brief description of different adaptive filtering algorithms compared in this project is given. The section concludes with an explanation of the approaches used for algorithm performance evaluation and parameter number optimization. 5.2.1 Signal Preprocessing Despite the great care taken during the experiments, it is still impossible to completely avoid artefact arising from sources other than the subjectsβ€Ÿ motion. These types of noise are normally uncorrelated with the reference signal and hence cannot be reduced by adaptive methods. This can potentially lead to unjust interpretations of the effectiveness of the proposed techniques and cause difficulties for performance evaluation. Before any further analysis, the raw ECG and reference signal recordings were preprocessed. As explained in section 2.3, ECG signals recorded from our experimental settings tended to be corrupted by a slow time varying drift (baseline wander) (Figure 5.1-A). Because of all the monitoring devices powered by alternating current, especially the amplifier and the shaker, some of the ECG recordings and acceleration signals were also disturbed by the 50 Hz mains frequency, as shown in Figure 5.1-B and Figure 5.1-G. These two interferences can be decreased by a serial combination of high pass filter and 50 Hz notch filter. This should be the preferred approach since it preserves most of the important features of the original ECG signals and motion artefact, while attenuating contributions from other sources. However, the electrode on the subjectβ€Ÿs right hand, although placed on the bones, may also capture high frequency EMG noise from the muscles in the hand. Electrical measurement noise from the amplifier, the moving wires and the data acquisition system will also contribute to this randomness and distortions. Thus, in addition to applying a 4th order Butterworth high pass filter with cut off frequency of 0.5 Hz and a 50 Hz IIR notch filter to reduce the baseline wander and mains interference, a 4th order 35Hz Butterworth low pass filter was also applied in the forward and reverse direction, introducing zero phase delay. The Biopac ECG amplifier module has inbuilt high pass filters and 50Hz notch filter but only the 0.05 Hz high pass filter was applied in this study. The results of preprocessing a segment of raw resting state ECG are shown in Figure 5.1. The subject whose ECG was being recorded was sitting still and relaxed so that motion artefact were kept at a minimum level, but the signal quality acquired was very poor (Figure 5.1-A,B) due to the abovementioned reasons. The preprocessed acquisitions show a much higher signal to noise ratio since 50 Hz noise has been clearly attenuated and the baseline drift has been removed (Figure 5.1-C). Similar improvements can be observed in the frequency domain (Figure 5.1-D). Power spectra, estimated using the Welch method with window length of 1000 points and 50%
  45. 45. - 41 - overlap, of preprocessed acceleration recordings are shown in Figure 5.1-E,F. Comparing with Figure 4.10-H and Figure 4.11-H, only the peak at fundamental frequency and first harmonic were preserved for the sinusoidal acceleration signal and the mains frequency interference has been effectively attenuated for both types of shaker vibrations. (A) (B) (C) (D) (E) (F) Figure (5.1) Signal preprocessing of a segment of raw ECG recording where no motion was involved. (A) Raw ECG recording corrupted by baseline wander and 50 Hz mains frequency. (B)Power spectrum estimate of corrupted ECG. (C) Time domain representation of preprocessed ECG signal after band pass and 50 Hz notch filtering. (D) Power spectrum estimate of preprocessed ECG. (E) Power spectrum of preprocessed sinusoidalacceleration and (F) Power spectrum of preprocessed BLWN acceleration. 0 1 2 3 4 5 6 7 8 9 10 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 Time/s Voltage/V Raw ECG recording 0 10 20 30 40 50 60 -55 -50 -45 -40 -35 -30 -25 -20 Frequency/Hz Power/Frequency(dB/Hz) Welch Power Spectral Density Estimate 0 1 2 3 4 5 6 7 8 9 10 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 Time/s Voltage/V Preprocessed ECG 0 10 20 30 40 50 60 -90 -80 -70 -60 -50 -40 -30 -20 Frequency/Hz Power/Frequency(dB/Hz) Welch Power Spectral Density Estimate 0 10 20 30 40 50 60 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 Frequency/Hz Power/Frequency(dB/Hz) Power Spectrum of Raw and Preprocessed Acc Raw Acc Preprocessed Acc 0 10 20 30 40 50 60 -60 -50 -40 -30 -20 -10 0 10 Frequency/Hz Power/Frequency(dB/Hz) Power Spectrum of Raw and Preprocessed Acc Raw Acc Preprocessed Acc
  46. 46. - 42 - Another issue which needs consideration is that the strength of ECG signal varies between different positions of the subject, as well as the quality of electrical contact, giving the chest and limb ECG distinct amplitudes. This will result in difficulties in assessing the performances of adaptive methods as will be explained in later sections. Linear regression of the preprocessed chest and limb ECGs was carried out to reduce this difference in signal magnitudes and offset levels. A comparison of the pair of ECG recordings before and after linear regression is shown in Figure 5.2-A,B. It should be noted that before linear regression the chest ECG is much larger than the limb ECG, but after the process, they are on the similar scale. Because of this, the ECG in later parts of this thesis is denoted as having arbitrary units. (A) (B) Figure (5.2) Effects of linear regression. (A) ECG acquisitions before linear regression. (B) ECG acquisitions after linear regression.The changes in the signal amplitudes should be noted. 5.2.2 Adaptive Motion Artefact Reduction The configuration of adaptive motion artefact reduction in ECG signals used in this project is shown in Figure 5.3. The motion artefact are generated from two sources: the controlled vibration of the shaker transmitted through the right hand and voluntary movements of the subjectβ€Ÿs right hand. The differences between various approaches lie in the selected filter structure and the corresponding algorithm for parameter update. Both linear and nonlinear adaptive methods were applied in the current study, details of their mathematical backgrounds and properties can be found in Chapter 3. Figure (5.3) Schematic of motion artefact reduction system using adaptive filters. 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 -0.5 0 0.5 1 1.5 2 Time/s Voltage/V Clean Chest ECG 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 -0.5 0 0.5 1 1.5 Time/s Voltage/V Clean Limb ECG 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 -0.5 0 0.5 1 Time/s Voltage/V Clean Chest ECG 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 -0.5 0 0.5 1 Time/s Voltage/V Clean Limb ECG
  47. 47. - 43 - The adopted linear adaptive structures involved a transversal filter model and either LMS or RLS coefficient estimation algorithm. Such a filter structure was chosen mainly because of its simplicity, stability and effectiveness in adaptive noise reduction, as has been proved in numerous previous studies [40, 41, 42, 43]. Their performances and the adaptive algorithms had been initially validated using computer simulated noisy ECGs and reference accelerations. This not only provided a brief review of the important properties of linear adaptive methods, but also laid the foundation for scheme selection and interpretation in practical situations. The application of linear adaptive filters to experimentally acquired ECGs is presented next. The adaptation coefficients or forgetting factors were selected individually instead of setting to the same value for each type of motion and all subjects. The specific value was decided by comparing the Normalized Cross Correlation (NCC) between the last eight seconds of the reconstructed ECG and the clean ECG. The ones which resulted in the highest degree of resemblance (visual inspection) were selected after a series of trials. The nonlinear models investigated in this project were cascade models which include LN (Linear-Nonlinear) or Wiener model, NL (Nonlinear-Linear) or Hammerstein model and LNL (Linear-Nonlinear-Linear) or Wiener Hammerstein model. They were chosen, instead of Volterra or Wiener series, because they provide a parsimonious means of increasing the order of nonlinearity, which is especially important for practical applications [46]. Furthermore, cascade models, subsets of Volterra series, simplify the analysis and interpretation of model parameters as their nonlinear polynomial elements remain in two dimensional space regardless of their order and can be transformed into Volterra form, if so desired. Computer simulations to these cascade models were not carried out because of time constraints. The outcomes of nonlinear modeling the experimentally recorded limb ECGs were contrasted with those of linear methods to check if the motion artefact can be better represented with the additional nonlinearities. 5.2.3 Performance Evaluation and Parameter Number Optimization The model order optimization process was often emphasized less in previous studies but is of great practical importance as the parameter number has a considerable effect on model performance and computation requirement for coefficients estimation. Before the comparison of different filter structures, the number of parameters each model contains should be chosen according to some evaluation standards. The problem of determining how different adaptive approaches have performed in experimentally recorded ECGs is not as straightforward as in computer simulations where both the artefact and clean ECG are available. In simulations, the amount of motion artefact reduction and the extent of signal morphology recovery can be exactly quantified and compared using signal-to-noise-ratios(SNR)[56,57],root-mean-square error[58,59] or cross correlation coefficient[59]. But practical difficulties exist in our current experimental settings because of the following. The two channels ECG recordings are never identical, even though located on the body with the aim of measuring the same β€žECG leadβ€Ÿ. This is to be expected since the two pairs of

Γ—