The document describes a thesis submitted for the degree of Bachelor of Technology in Electrical Engineering. The thesis aims to classify electrocardiogram (ECG) waveforms in real-time to diagnose cardiac diseases. It uses the discrete Daubechies wavelet transform to preprocess ECG signals and extract features. These features are then classified using a multilayer perceptron neural network. The classification model was implemented in SIMULINK software to simulate real-time detection and verify its performance. The thesis discusses ECG basics, wavelet transforms, neural networks, and presents results of signal decomposition, network training, and SIMULINK implementation.
Classification and Detection of ECG-signals using Artificial Neural NetworksGaurav upadhyay
Electrocardiogram (ECG), a noninvasive technique is used as a primary diagnostic tool for
cardiovascular diseases. A cleaned ECG signal provides necessary information about the
electrophysiology of the heart diseases and ischemic changes that may occur. It provides
valuable information about the functional aspects of the heart and cardiovascular system. The
objective of the thesis is to automatic detection of cardiac arrhythmias in ECG signal.
Recently developed digital signal processing and pattern reorganization technique is used in
this thesis for detection of cardiac arrhythmias. The detection of cardiac arrhythmias in the
ECG signal consists of following stages: detection of QRS complex in ECG signal; feature
extraction from detected QRS complexes; classification of beats using extracted feature set
from QRS complexes. In turn automatic classification of heartbeats represents the automatic
detection of cardiac arrhythmias in ECG signal. Hence, in this thesis, we developed the
automatic algorithms for classification of heartbeats to detect cardiac arrhythmias in ECG
signal.QRS complex detection is the first step towards automatic detection of cardiac
arrhythmias in ECG signal. A novel algorithm for accurate detection of QRS complex in ECG
signal peak classification approach is used in ECG signal for determining various diseases . As
known the amplitudes and duration values of P-Q-R-S-T peaks determine the functioning of
heart of human. Therefore duration and amplitude of all peaks are found. R-R and P-R
intervals are calculated. Finally, we have obtained the necessary information for disease
detection .For detection of cardiac arrhythmias; the extracted features in the ECG signal will
be input to the classifier. The extracted features contain morphological l features of each
heartbeat in the ECG signal. This project is implemented by using MATLAB software. An
interface was created to easily select and process the signal. “.dat” format is used the for ECG
signal data. We have detected bradycardia and tachycardia. Massachusetts Institute of
Technology Beth Israel Hospital (MIT-BIH) arrhythmias database has been used for
performance analysis.
Evaluating ECG Capturing Using Sound-Card of PC/Laptopijics
The purpose of the Evaluating ECG capturing using sound-card of PC/Laptop is provided portable and low
cost ECG monitoring system using laptop and mobile phones. There is no need to interface microcontroller
or any other device to transmit ECG data. This research is based on hardware design,
implementation, signal capturing and Evaluation of an ECG processing and analyzing system which attend
the physicians in heart disease diagnosis. Some important modification is given in design part to avoid all
definitive ECG instrument problems faced in previous designs. Moreover, attenuate power frequency noise
and noise that produces from patient's body have required additional developments. The hardware design
has basically three units: transduction and conditioning Unit, interfacing unit and data processing unit.
The most focusing factor is the ECG signal/data transmits in laptop/PC via microphone pin. The live
simulation is possible using SOUNDSCOPE software in PC/Laptop. The software program that is written
in MATLAB and LAB-View performs data acquisition (record, stored, filtration) and several tasks such as
QRS detection, calculate heart rate.
International Journal of Computational Engineering Research(IJCER) ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Identification of Myocardial Infarction from Multi-Lead ECG signalIJERA Editor
Electrocardiogram (ECG) is the cheap and noninvasive method of depicting the heart activity and abnormalities.
It provides information about the functionality of the heart. It is the record of variation of bioelectric potential
with respect to time as the human heart beats. The classification of ECG signals is an important application since
the early detection of heart diseases/abnormalities can prolong life and enhance the quality of living through
appropriate treatment. Since the ECG signals, while recording are contaminated by several noises it is necessary
to preprocess the signals prior to classification. Digital filters are used to remove noise from the signal. Principal
component analysis is applied on the 12 lead signal to extract various features. The present paper shows the
unique feature, point score calculated on the basis of the features extracted from the ECG signal. The point
score calculation is tested for 40 myocardial infarction ECG signals and 25 Normal ECG signals from the PTB
Diagnostic database with 94% sensitivity.
Classification of ecg signal using artificial neural networkGaurav upadhyay
An electrocardiogram (ECG) is a bio-electrical signal which is used to record the heart's electrical activity with respect to time. Early and accurate detection is important in detecting heart diseases and choosing appropriate treatment for a patient. ECG signals are used as the parameter for detection of Cardiac diseases and most of the data comes from PhysioDataNet and MIT-BIH database .The pre-processing of ECG signal is performed with help of Wavelet toolbox and also used for feature extraction of ECG signal. The complete project is implemented on MATLAB platform. The performance of the algorithm is evaluated on MIT–BIH Database. This paper presents the application of Probabilistic Neural Networks (PNN) for the classification and detection of Electrocardiogram (ECG).
Classification of Arrhythmia from ECG Signals using MATLABDr. Amarjeet Singh
An Electrocardiogram (ECG) is defined as a test
that is performed on the heart to detect any abnormalities in
the cardiac cycle. Automatic classification of ECG has
evolved as an emerging tool in medical diagnosis for effective
treatments. The work proposed in this paper has been
implemented using MATLAB. In this paper, we have
proposed an efficient method to classify the ECG into normal
and abnormal as well as classify the various abnormalities.
To brief it, after the collection and filtering the ECG signal,
morphological and dynamic features from the signal were
obtained which was followed by two step classification
method based on the traits and characteristic evaluation.
ECG signals in this work are collected from MIT-BIH, AHA,
ESC, UCI databases. In addition to this, this paper also
provides a comparative study of various methods proposed
via different techniques. The proposed technique used helped
us process, analyze and classify the ECG signals with an
accuracy of 97% and with good convenience.
Classification and Detection of ECG-signals using Artificial Neural NetworksGaurav upadhyay
Electrocardiogram (ECG), a noninvasive technique is used as a primary diagnostic tool for
cardiovascular diseases. A cleaned ECG signal provides necessary information about the
electrophysiology of the heart diseases and ischemic changes that may occur. It provides
valuable information about the functional aspects of the heart and cardiovascular system. The
objective of the thesis is to automatic detection of cardiac arrhythmias in ECG signal.
Recently developed digital signal processing and pattern reorganization technique is used in
this thesis for detection of cardiac arrhythmias. The detection of cardiac arrhythmias in the
ECG signal consists of following stages: detection of QRS complex in ECG signal; feature
extraction from detected QRS complexes; classification of beats using extracted feature set
from QRS complexes. In turn automatic classification of heartbeats represents the automatic
detection of cardiac arrhythmias in ECG signal. Hence, in this thesis, we developed the
automatic algorithms for classification of heartbeats to detect cardiac arrhythmias in ECG
signal.QRS complex detection is the first step towards automatic detection of cardiac
arrhythmias in ECG signal. A novel algorithm for accurate detection of QRS complex in ECG
signal peak classification approach is used in ECG signal for determining various diseases . As
known the amplitudes and duration values of P-Q-R-S-T peaks determine the functioning of
heart of human. Therefore duration and amplitude of all peaks are found. R-R and P-R
intervals are calculated. Finally, we have obtained the necessary information for disease
detection .For detection of cardiac arrhythmias; the extracted features in the ECG signal will
be input to the classifier. The extracted features contain morphological l features of each
heartbeat in the ECG signal. This project is implemented by using MATLAB software. An
interface was created to easily select and process the signal. “.dat” format is used the for ECG
signal data. We have detected bradycardia and tachycardia. Massachusetts Institute of
Technology Beth Israel Hospital (MIT-BIH) arrhythmias database has been used for
performance analysis.
Evaluating ECG Capturing Using Sound-Card of PC/Laptopijics
The purpose of the Evaluating ECG capturing using sound-card of PC/Laptop is provided portable and low
cost ECG monitoring system using laptop and mobile phones. There is no need to interface microcontroller
or any other device to transmit ECG data. This research is based on hardware design,
implementation, signal capturing and Evaluation of an ECG processing and analyzing system which attend
the physicians in heart disease diagnosis. Some important modification is given in design part to avoid all
definitive ECG instrument problems faced in previous designs. Moreover, attenuate power frequency noise
and noise that produces from patient's body have required additional developments. The hardware design
has basically three units: transduction and conditioning Unit, interfacing unit and data processing unit.
The most focusing factor is the ECG signal/data transmits in laptop/PC via microphone pin. The live
simulation is possible using SOUNDSCOPE software in PC/Laptop. The software program that is written
in MATLAB and LAB-View performs data acquisition (record, stored, filtration) and several tasks such as
QRS detection, calculate heart rate.
International Journal of Computational Engineering Research(IJCER) ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Identification of Myocardial Infarction from Multi-Lead ECG signalIJERA Editor
Electrocardiogram (ECG) is the cheap and noninvasive method of depicting the heart activity and abnormalities.
It provides information about the functionality of the heart. It is the record of variation of bioelectric potential
with respect to time as the human heart beats. The classification of ECG signals is an important application since
the early detection of heart diseases/abnormalities can prolong life and enhance the quality of living through
appropriate treatment. Since the ECG signals, while recording are contaminated by several noises it is necessary
to preprocess the signals prior to classification. Digital filters are used to remove noise from the signal. Principal
component analysis is applied on the 12 lead signal to extract various features. The present paper shows the
unique feature, point score calculated on the basis of the features extracted from the ECG signal. The point
score calculation is tested for 40 myocardial infarction ECG signals and 25 Normal ECG signals from the PTB
Diagnostic database with 94% sensitivity.
Classification of ecg signal using artificial neural networkGaurav upadhyay
An electrocardiogram (ECG) is a bio-electrical signal which is used to record the heart's electrical activity with respect to time. Early and accurate detection is important in detecting heart diseases and choosing appropriate treatment for a patient. ECG signals are used as the parameter for detection of Cardiac diseases and most of the data comes from PhysioDataNet and MIT-BIH database .The pre-processing of ECG signal is performed with help of Wavelet toolbox and also used for feature extraction of ECG signal. The complete project is implemented on MATLAB platform. The performance of the algorithm is evaluated on MIT–BIH Database. This paper presents the application of Probabilistic Neural Networks (PNN) for the classification and detection of Electrocardiogram (ECG).
Classification of Arrhythmia from ECG Signals using MATLABDr. Amarjeet Singh
An Electrocardiogram (ECG) is defined as a test
that is performed on the heart to detect any abnormalities in
the cardiac cycle. Automatic classification of ECG has
evolved as an emerging tool in medical diagnosis for effective
treatments. The work proposed in this paper has been
implemented using MATLAB. In this paper, we have
proposed an efficient method to classify the ECG into normal
and abnormal as well as classify the various abnormalities.
To brief it, after the collection and filtering the ECG signal,
morphological and dynamic features from the signal were
obtained which was followed by two step classification
method based on the traits and characteristic evaluation.
ECG signals in this work are collected from MIT-BIH, AHA,
ESC, UCI databases. In addition to this, this paper also
provides a comparative study of various methods proposed
via different techniques. The proposed technique used helped
us process, analyze and classify the ECG signals with an
accuracy of 97% and with good convenience.
Classification and Detection of ECG-signals using Artificial Neural NetworksGaurav upadhyay
An electrocardiogram (ECG) is a bio-electrical signal which is used to record the heart's electrical activity with respect to time. Early and accurate detection is important in detecting heart diseases and choosing appropriate treatment for a patient. ECG signals are used as the parameter for detection of Cardiac diseases and most of the data comes from PhysioDataNet and MIT-BIH database .The pre-processing of ECG signal is performed with help of Wavelet toolbox and also used for feature extraction of ECG signal. The complete project is implemented on MATLAB platform. The performance of the algorithm is evaluated on MIT–BIH Database. This paper presents the application of Probabilistic Neural Networks (PNN) for the classification and detection of Electrocardiogram (ECG).
Acquiring Ecg Signals And Analysing For Different Heart AilmentsIJERA Editor
This paper describes and focuses on acquiring and identification of cardiac diseases using ECG waveform in LabVIEW software, which would bridge the gap between engineers and medical physicians. This model work collects the waveform of an affected person. The waveform is analyzed for diseases and then a report is sent to the doctor through mail. Initially the waveforms are collected from the person using EKG sensor with the help of surface electrodes and the hardware controlled by MCU C8051, acquires ECG and also Phonocardiogram (PCG) synchronously and the waveform is sent to the PC installed with LabVIEW software through DAQ-6211. The waveform in digital format is saved and sent to the loops containing conditions for different diseases. If the waveform parameters coincide with any of the looping statements, particular disease is indicated. Simultaneously the patient PCG report is also collected in a separate database containing all information, which will be sent to the doctor through mail.
Heart rate detection using hilbert transformeSAT Journals
Abstract The electrocardiogram (ECG) is a well known method that can be used to measure Heart Rate Variability (HRV). This paper describes a procedure for processing electrocardiogram signals (ECG) to detect Heart Rate Variability (HRV). In recent years, there have been wide-ranging studies on Heart rate variability in ECG signals and analysis of Respiratory Sinus Arrhythmia (RSA). Normally the Heart rate variability is studied based on cycle length variability, heart period variability, RR variability and RR interval tachogram. The HRV provides information about the sympathetic-parasympathetic autonomic stability and consequently about the risk of unpredicted cardiac death. The heart beats in ECG signal are detected by detecting R-Peaks in ECG signals and used to determine useful information about the various cardiac abnormalities. The temporal locations of the R-wave are identified as the locations of the QRS complexes. In the presence of poor signal-to-noise ratios or pathological signals and wrong placement of ECG electrodes, the QRS complex may be missed or falsely detected and may lead to poor results in calculating heart beat in turn inter-beat intervals. We have studied the effects of number of common elements of QRS detection methods using MIT/BIH arrhythmia database and devised a simple and effective method. In this method, first the ECG signal is preprocessed using band-pass filter; later the Hilbert Transform is applied on filtered ECG signal to enhance the presence of QRS complexes, to detect R-Peaks by setting a threshold and finally the RR-intervals are calculated to determine Heart Rate. We have implemented our method using MATLAB on ECG signal which is obtained from MIT/BIH arrhythmia database. Our MATLAB implementation results in the detection of QRS complexes in ECG signal, locate the R-Peaks, computes Heart Rate (HR) by calculating RR-internal and plotting of HR signal to show the information about HRV. Index Terms: ECG, QRS complex, R-Peaks, HRV, Heart Rate signal, RSA, Hilbert Transform, Arrhythmia, MIT/BIH, MATLAB and Lynn’s filters
A Wireless ECG Plaster for Real-Time Cardiac Health Monitoring in Body Senso...ecgpapers
In this paper we present a wireless ECG plaster
that can be used for real-time monitoring of ECG in cardiac
patients. The proposed device is light weight (25 grams),
wearable and can wirelessly transmit the patient’s ECG signal to
mobile phone or PC using ZigBee. The device has a battery life of
around 26 hours while in continuous operation, owing to the
proposed ultra-low power ECG acquisition front end chip. The
prototype has been verified in clinical trials.
PERFORMANCE EVALUATION OF ARTIFICIAL NEURAL NETWORKS FOR CARDIAC ARRHYTHMIA C...IAEME Publication
In this paper an effective and most reliable method for appropriate classification of cardiac arrhythmia using automatic Artificial Neural Network (ANN) has been proposed. The results are encouraging and are found to have produced a very confident and efficient arrhythmia classification, which is easily applicable in diagnostic decision support system. The authors have employed 3 neural network classifiers to classify three types of beats of ECG signal, namely Normal (N), and two abnormal beats Right Bundle Branch Block (RBBB) and Premature Ventricular Contraction (PVC). The classifiers used in this paper are K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC) and Multi-Class Support Vector Machine (MSVM). The performance of the classifiers is evaluated using 5 parametric measures namely Sensitivity (Se), Specificity (Sp), Precision (Pr), Bit Error Rate (BER) and Accuracy (A). Hence MSVM classifier using Crammers method is very effective for proper ECG beat classification.
Removal of artifacts in EEG by averaging andNamratha Dcruz
This is a presentation on removal of artifacts in EEG by averaging and adaptive algorithms which covers a small topic in the elective Bio medical signal processing for M.Tech in Signal Processing
AR-based Method for ECG Classification and Patient RecognitionCSCJournals
The electrocardiogram (ECG) is the recording of heart activity obtained by measuring the signals from electrical contacts placed on the skin of the patient. By analyzing ECG, it is possible to detect the rate and consistency of heartbeats and identify possible irregularities in heart operation. This paper describes a set of techniques employed to pre-process the ECG signals and extract a set of features – autoregressive (AR) signal parameters used to characterise ECG signal. Extracted parameters are in this work used to accomplish two tasks. Firstly, AR features belonging to each ECG signal are classified in groups corresponding to three different heart conditions – normal, arrhythmia and ventricular arrhythmia. Obtained classification results indicate accurate, zero-error classification of patients according to their heart condition using the proposed method. Sets of extracted AR coefficients are then extended by adding an additional parameter – power of AR modelling error and a suitability of developed technique for individual patient identification is investigated. Individual feature sets for each group of detected QRS sections are classified in p clusters where p represents the number of patients in each group. Developed system has been tested using ECG signals available in MIT/BIH and Politecnico of Milano VCG/ECG database. Achieved recognition rates indicate that patient identification using ECG signals could be considered as a possible approach in some applications using the system developed in this work. Pre-processing stages, applied parameter extraction techniques and some intermediate and final classification results are described and presented in this paper.
This paper aims to present a very-large-scale integration (VLSI) friendly electrocardiogram (ECG) QRS detector for body sensor networks. Baseline wandering and background noise are removed from original ECG signal by mathematical morphological method. The performance of the algorithm is evaluated with standard MIT-BIH arrhythmia database and wearable exercise ECG Data. Corresponding power and area efficient VLSI architecture is reduced by replacing the one of the Ripple Carry Adder in the Carry select adder with Binary to Excess 1 converter
Classification and Detection of ECG-signals using Artificial Neural NetworksGaurav upadhyay
An electrocardiogram (ECG) is a bio-electrical signal which is used to record the heart's electrical activity with respect to time. Early and accurate detection is important in detecting heart diseases and choosing appropriate treatment for a patient. ECG signals are used as the parameter for detection of Cardiac diseases and most of the data comes from PhysioDataNet and MIT-BIH database .The pre-processing of ECG signal is performed with help of Wavelet toolbox and also used for feature extraction of ECG signal. The complete project is implemented on MATLAB platform. The performance of the algorithm is evaluated on MIT–BIH Database. This paper presents the application of Probabilistic Neural Networks (PNN) for the classification and detection of Electrocardiogram (ECG).
Acquiring Ecg Signals And Analysing For Different Heart AilmentsIJERA Editor
This paper describes and focuses on acquiring and identification of cardiac diseases using ECG waveform in LabVIEW software, which would bridge the gap between engineers and medical physicians. This model work collects the waveform of an affected person. The waveform is analyzed for diseases and then a report is sent to the doctor through mail. Initially the waveforms are collected from the person using EKG sensor with the help of surface electrodes and the hardware controlled by MCU C8051, acquires ECG and also Phonocardiogram (PCG) synchronously and the waveform is sent to the PC installed with LabVIEW software through DAQ-6211. The waveform in digital format is saved and sent to the loops containing conditions for different diseases. If the waveform parameters coincide with any of the looping statements, particular disease is indicated. Simultaneously the patient PCG report is also collected in a separate database containing all information, which will be sent to the doctor through mail.
Heart rate detection using hilbert transformeSAT Journals
Abstract The electrocardiogram (ECG) is a well known method that can be used to measure Heart Rate Variability (HRV). This paper describes a procedure for processing electrocardiogram signals (ECG) to detect Heart Rate Variability (HRV). In recent years, there have been wide-ranging studies on Heart rate variability in ECG signals and analysis of Respiratory Sinus Arrhythmia (RSA). Normally the Heart rate variability is studied based on cycle length variability, heart period variability, RR variability and RR interval tachogram. The HRV provides information about the sympathetic-parasympathetic autonomic stability and consequently about the risk of unpredicted cardiac death. The heart beats in ECG signal are detected by detecting R-Peaks in ECG signals and used to determine useful information about the various cardiac abnormalities. The temporal locations of the R-wave are identified as the locations of the QRS complexes. In the presence of poor signal-to-noise ratios or pathological signals and wrong placement of ECG electrodes, the QRS complex may be missed or falsely detected and may lead to poor results in calculating heart beat in turn inter-beat intervals. We have studied the effects of number of common elements of QRS detection methods using MIT/BIH arrhythmia database and devised a simple and effective method. In this method, first the ECG signal is preprocessed using band-pass filter; later the Hilbert Transform is applied on filtered ECG signal to enhance the presence of QRS complexes, to detect R-Peaks by setting a threshold and finally the RR-intervals are calculated to determine Heart Rate. We have implemented our method using MATLAB on ECG signal which is obtained from MIT/BIH arrhythmia database. Our MATLAB implementation results in the detection of QRS complexes in ECG signal, locate the R-Peaks, computes Heart Rate (HR) by calculating RR-internal and plotting of HR signal to show the information about HRV. Index Terms: ECG, QRS complex, R-Peaks, HRV, Heart Rate signal, RSA, Hilbert Transform, Arrhythmia, MIT/BIH, MATLAB and Lynn’s filters
A Wireless ECG Plaster for Real-Time Cardiac Health Monitoring in Body Senso...ecgpapers
In this paper we present a wireless ECG plaster
that can be used for real-time monitoring of ECG in cardiac
patients. The proposed device is light weight (25 grams),
wearable and can wirelessly transmit the patient’s ECG signal to
mobile phone or PC using ZigBee. The device has a battery life of
around 26 hours while in continuous operation, owing to the
proposed ultra-low power ECG acquisition front end chip. The
prototype has been verified in clinical trials.
PERFORMANCE EVALUATION OF ARTIFICIAL NEURAL NETWORKS FOR CARDIAC ARRHYTHMIA C...IAEME Publication
In this paper an effective and most reliable method for appropriate classification of cardiac arrhythmia using automatic Artificial Neural Network (ANN) has been proposed. The results are encouraging and are found to have produced a very confident and efficient arrhythmia classification, which is easily applicable in diagnostic decision support system. The authors have employed 3 neural network classifiers to classify three types of beats of ECG signal, namely Normal (N), and two abnormal beats Right Bundle Branch Block (RBBB) and Premature Ventricular Contraction (PVC). The classifiers used in this paper are K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC) and Multi-Class Support Vector Machine (MSVM). The performance of the classifiers is evaluated using 5 parametric measures namely Sensitivity (Se), Specificity (Sp), Precision (Pr), Bit Error Rate (BER) and Accuracy (A). Hence MSVM classifier using Crammers method is very effective for proper ECG beat classification.
Removal of artifacts in EEG by averaging andNamratha Dcruz
This is a presentation on removal of artifacts in EEG by averaging and adaptive algorithms which covers a small topic in the elective Bio medical signal processing for M.Tech in Signal Processing
AR-based Method for ECG Classification and Patient RecognitionCSCJournals
The electrocardiogram (ECG) is the recording of heart activity obtained by measuring the signals from electrical contacts placed on the skin of the patient. By analyzing ECG, it is possible to detect the rate and consistency of heartbeats and identify possible irregularities in heart operation. This paper describes a set of techniques employed to pre-process the ECG signals and extract a set of features – autoregressive (AR) signal parameters used to characterise ECG signal. Extracted parameters are in this work used to accomplish two tasks. Firstly, AR features belonging to each ECG signal are classified in groups corresponding to three different heart conditions – normal, arrhythmia and ventricular arrhythmia. Obtained classification results indicate accurate, zero-error classification of patients according to their heart condition using the proposed method. Sets of extracted AR coefficients are then extended by adding an additional parameter – power of AR modelling error and a suitability of developed technique for individual patient identification is investigated. Individual feature sets for each group of detected QRS sections are classified in p clusters where p represents the number of patients in each group. Developed system has been tested using ECG signals available in MIT/BIH and Politecnico of Milano VCG/ECG database. Achieved recognition rates indicate that patient identification using ECG signals could be considered as a possible approach in some applications using the system developed in this work. Pre-processing stages, applied parameter extraction techniques and some intermediate and final classification results are described and presented in this paper.
This paper aims to present a very-large-scale integration (VLSI) friendly electrocardiogram (ECG) QRS detector for body sensor networks. Baseline wandering and background noise are removed from original ECG signal by mathematical morphological method. The performance of the algorithm is evaluated with standard MIT-BIH arrhythmia database and wearable exercise ECG Data. Corresponding power and area efficient VLSI architecture is reduced by replacing the one of the Ripple Carry Adder in the Carry select adder with Binary to Excess 1 converter
Analysis of Human Electrocardiogram for Biometric Recognition Using Analytic ...CSCJournals
The electrocardiograph (ECG) contains cardiac features unique to each individual. By analyzing ECG, it should therefore be possible not only to detect the rate and consistency of heartbeats but to also extract other signal features in order to identify ECG records belonging to individual subjects. In this paper, a new approach for automatic analysis of single lead ECG for human recognition is proposed and evaluated. Eighteen temporal, amplitude, width and autoregressive (AR) model parameters are extracted from each ECG beat and classified in order to identify each individual. Proposed system uses pre-processing stage to decrease the effects of noise and other unwanted artifacts usually present in raw ECG data. Following pre-processing steps, ECG stream is partitioned into separate windows where each window includes single beat of ECG signal. Window estimation is based on the localization of the R peaks in the ECG stream that detected by Filter bank method for QRS complex detection. ECG features – temporal, amplitude and AR coefficients are then extracted and used as an input to K-nn and SVM classification algorithms in order to identify the individual subjects and beats. Signal pre-processing techniques, applied feature extraction methods and some intermediate and final classification results are presented in this paper.
Classification of cardiac vascular disease from ecg signals for enhancing mod...hiij
“Why to be in frustration we will do new creation f
or salvation”. Based on these words we grapes your
attention towards saving a life of a heart patient
with the use of ECG in Public Health Care Center by
transmitting ECG signals to nearby hospital server.
In this paper we analyze the abnormalities found i
n the
ECG signals by identifying the Normal, Bradycardia
Arrhythmia, Tachycardia Arrhythmia and Ischemia
signal using the method of Neuro Fuzzy Classifier.
Daubechies Wavelet Transforms is used for feature
extraction and Adaptive Neuro Fuzzy Inference Syste
m (ANFIS) is used for classification. The compressi
on
algorithm is performed by using Huffman coding.
Project Report on ECG Transmitter using Agilent ADS (Advance System Design)Manu Mitra
In 1996, Intellidesign Pty Ltd (then IntelliMed) was approached by a cardiologist to design an ECG Holter monitor. This original device was a two or three lead, single channel ECG device, which could continuously record for a maximum of one hour. Additionally, the device had Polar Chest Strap capabilities for the added functionality as a Heart Rate monitor. The device could operate in four different modes: 1-hour ECG Recording Mode, in which the device would record one continuous hour of near diagnostic quality ECG trace during exercise; Event Recording Mode, in which the device would record up to 60, one minute segments around a recorded event, over a period of up to 24 hours; Heart rate Recording Mode, in which the unit would have the capacity to record up to 24 hours of heart rate information; and ECG Telemetry Mode, in which the unit would transmit, via a Radio Frequency (RF) link, a real-time ECG signal to a receiver unit. The purpose of this project is to design ECG transmitter using the software Agilent ADS.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Electrocardiograph based Arrythmia Detection SystemDr. Amarjeet Singh
Cardiac disorders turn out to be a serious disease if
not diagnosed and treated at the earliest. Arrhythmia is a
cardiac disorder that exists as a result of irregular heart beat
conditions. There are several variants in this type of disorder
which can be only diagnosed only when patient is under an
intensive care conditions and also the patient with such
disorder do not experience and physical symptoms. Such
diseases turn out to be deadly if not treated early. A detection
system is thus required which is capable of detecting these
arrhythmias in real time and aid in the diagnosis. An FPGA
based arrhythmia detection system is designed and
implemented here which can detect second degree AV block
type of arrhythmia. The designed system was simulated and
tested with ECG signal from MIT-BH database and the
results revealed that a robust arrhythmia detection system
was implemented.
Right-Leg-Driven Circuit and Instrumentation Amplifier in ECG Acquisition SystemJewel Haque
In an ECG (Electrocardiogram) acquisition system, instrumentation amplifiers are commonly used to amplify the small electrical signals generated by the heart and reject common-mode noise. The term "Right-Leg-Driven Circuit" (RLD) is a technique used to improve the common-mode rejection ratio (CMRR) in instrumentation amplifiers, specifically in ECG applications where high CMRR is crucial for accurate signal acquisition.
Here's an overview of how a Right-Leg-Driven Circuit and an Instrumentation Amplifier are used in an ECG acquisition system:
Instrumentation Amplifier (IA):
An Instrumentation Amplifier is a differential amplifier with high input impedance and high common-mode rejection capability. It amplifies the voltage difference between two input terminals (ECG electrodes in this case) while rejecting any common-mode signals (such as interference or noise) that are present on both input terminals.
The IA typically consists of three operational amplifiers (Op-Amps) configured to provide the desired amplification and filtering.
Right-Leg-Driven Circuit (RLD):
The Right-Leg-Driven Circuit is a technique used to reduce the common-mode voltage at the patient's right leg electrode. This is important because the reference electrode (usually the right leg electrode) should ideally have a common-mode voltage close to the patient's common-mode voltage to ensure accurate ECG measurements and patient safety.
RLD works by actively driving the right leg electrode to match the common-mode voltage of the patient. This is usually done using an auxiliary electrode placed on the patient's right leg and a feedback loop to maintain the voltage at the right leg electrode close to the patient's common-mode voltage.
The RLD circuit is connected to one of the inputs of the instrumentation amplifier to effectively subtract the common-mode voltage from the ECG signal, further improving CMRR.
The combined use of an Instrumentation Amplifier and a Right-Leg-Driven Circuit helps achieve high common-mode rejection and accurate ECG signal acquisition by:
Amplifying the small differential ECG signal.
Rejecting common-mode noise and interference.
Reducing the common-mode voltage at the right leg electrode to minimize the risk of electrical shock to the patient and to ensure accurate measurements.
ECG acquisition systems are critical for monitoring heart health, and these techniques are essential to obtain reliable and accurate ECG waveforms while maintaining patient safety by minimizing common-mode voltage differences. Implementing such systems requires careful design and consideration of electrode placement, amplifier characteristics, and signal processing techniques to achieve optimal performance.
DENOISING OF ECG SIGNAL USING FILTERS AND WAVELET TRANSFORMIJEEE
This paper presents a comparison of methods for denoising the Electrocardiogram signal. The methods are applied on
MIT-BIH arrhythmia database and implemented using MATLAB software.
Abstract: Electrocardiogram is a machine that is used for the detection and the analysis of the peaks of the ECG signal. ECG signal is used for the detection of various diseases related to the heart. The cardiac arrhythmia shows abnormalities of heart that is considered as the major threat to the human. The peaks that are present in the ECG signal are used for detection of the disease. The R peak of the ECG signal is used for the detection of the disease, the arrhythmia is detected as Tachycardia and Bradycardia. This paper presents a study of the ECG signal, peaks and of the various techniques that are used for the detection of disease.
Survey on electrocardiography signal analysis and diabetes mellitus: unraveli...IJECEIAES
Electrocardiography (ECG) is crucial in the medical field to assess cardiovascular diseases. ECG signal generates information, i.e., QRS complexes that imply the cardiac health of the human body. It is depicted in the form of a graph with voltage versus time interval. A distorted, inverted, lagged, small waveform implies an abnormality in a cardiac system. This study highlights the generation of an ECG signal, QRS complexes undertoned towards different diseases, event detection, and signal processing methods. It has become crucial to highlight the possibilities and advances that can be derived from an ECG signal. Throughout this study, an instance of diabetes mellitus (DM) is considered for creating concrete awareness and understanding of an ECG signal in DM. This study focuses on finding the correlation between ECG and DM. Detection of DM from ECG signal is also studied. The findings of this survey paper conclude that the correlation between DM individuals with cardiovascular complications has autonomic neuropathy, which may lead to myocardial infarction. It is also found that the QRS complex and its abnormalities are not specific to complications in DM. However, non-invasive detection of diabetes through ECG signals demonstrates future research potential.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Electrocardiogram (ECG) flag is the electrical action of the human heart. The ECG contains imperative data about the general execution of the human heart framework. In this way, exact examination of the ECG flag is extremely critical however difficult undertaking. ECG flag is regularly low adequacy and polluted with various kinds of commotions due to its estimation procedure e.g. control line obstruction, amplifier clamor and standard meander. Benchmark meander is a sort of organic commotion caused by the arbitrary development of patient amid ECG estimation and misshapes the ST fragment of the ECG waveform. In this paper, we present a far reaching near investigation of five generally utilized versatile filtering calculations for the evacuation of low recurrence clamor. We perform broad investigations on the Physionet MIT BIH ECG database and contrast the flag with commotion proportion (SNR), combination rate, and time many-sided quality of these calculations. It is discovered that modified LMS has better execution than others regarding SNR and assembly rate.
Classifying electrocardiograph waveforms using trained deep learning neural n...IAESIJAI
Due to the rise in cardiac patients, an automated system that can identify different heart disorders has been created to lighten and distribute the duty of physicians. This research uses three different electrocardiograph (ECG) signals as indicators of a person's cardiac problems: Normal sinus rhythm (NSR), arrhythmia (ARR), and congestive heart failure (CHF). The continuous wavelet transform (CWT) provides the mechanism for classifying the 190 individual cases of ECG data into a 2-dimensional time-frequency representation. In this paper, the modified GoogLeNet is used for ECG data classification. Using a transfer learning approach and adjustments to parts of the output layers, ECG classification was conducted and the effectiveness of convolutional neural network (CNN) designs was tested. By comparing the results that the optimized neural network and GoogLeNet both had classification accuracy about of 80% and 100%, respectively. The GoogLeNet provide the best result in term of accuracy and training time.
Electrocardiograph is a biomedical device that measures electrical potential generated by
electrical activity that occurs due to the heart’s pumping action. The graphical presentation of the
Electrocardiogram (ECG) can be interpreted so that normal and abnormal rhythms of the heart can be detected
and diagnosed. Design, construction and manufacturing of this device in Africa would improve access to health
care, create employment and improve the African economy. The major materials considered for the
implementation include the instrumentation amplifier AD624, Low Noise JFET Operational Amplifier TL074, a
clinical standard 12-lead ECG electrode, various electrical and electronic components such as resistors,
capacitors and diodes for protection and an oscilloscope. The electrodes connected to the body convert the
heart signal into electrical voltage. These voltages obtained from the body are too small for the oscilloscope to
capture and so are amplified using AD624. Noise from the environment affects the ECG signal. To suppress the
noise, the signal from the amplifier is filtered. According to the International Electrotechnical Commission
(IEC) specification, the bandwidth required for an ECG filtering is between 0.5Hz – 150Hz. Band-pass filtering
is used. The signal obtained from the band pass filter stage is then passed through a notch filter to further
eliminate 50 Hz noise from the power line. The result is then displayed on an oscilloscope. The
Electrocardiograph was tested on different subjects and the results compare favourably with results obtained
with imported ECG monitor.
1. NATIONAL INSTITUTE OF TECHNOLOGY, ROURKELA
REALTIME
CLASSIFICATION OF ECG
WAVEFORMS FOR
DIAGNOSIS OF DISEASES
By
Soumya Ranjan Mishra
And
K Goutham
Under the supervision of
Dr. Dipti Patra
Electrical Engineering
NIT Rourkela
2009-2010
3. National Institute Of Technology
Rourkela
CERTIFICATECERTIFICATECERTIFICATECERTIFICATE
This is to certify that the thesis entitled, ”real-time classification of ECG
signals for diagnosis of diseases” by Soumya Ranjan Mishra and K Goutham
in partial requirements for the curriculum requirement of Bachelor of
technology in Electrical Engineering at National Institute Of Technology,
Rourkela, is an authentic work carried out by them under my supervision and
guidance.
To the best of my knowledge, the matter embodied in the thesis has not been
submitted to any other University/Institute for the award of any degree.
Date: Dr. Dipti Patra
Electrical Engineering
NIT, Rourkela
4. CONTENTS
1. Abstract ………………………………….……………….………01
2. Introduction ………………………………………….………….02
3. Background ……………………………………….……….……..04
3.1. The Electrocardiogram………………….……….……….04
4. Material and methods……………………….………….…………07
4.1. The wavelet transform………..……….………………….07
4.2. The Daubechies wavelet transform……………………….08
4.3. Multilayered Perceptron based Neural Network………….12
5. Results and inference…………..………………………………….14
5.1. Wavelet decomposition…………………………………..14
5.2. Neural networks………………………………………….17
6. SIMULINK model implementation………………………………19
6.1. SIMULINK results…………………………..…………..22
7. Conclusion and future work……………...……………………….23
8. References…………………………………………….…………..24
5. ACKNOWLEDGEMENT
We would like to express our sincere gratitude towards our teacher and
guide, Dr. Dipti Patra who infused us with the drive to carry on the project
work by her consistent support and encouragement throughout the project
work. Without her support, the thesis would have never been completed.
We would like to thank the faculty of Electrical Engineering department of
NIT, Rourkela for being the panacea to all our queries.
Last but not the least, we would like to thank all our dear friends for being
their wikipediaic knowledge and unrelenting support.
Regards,
Soumya Ranjan Mishra
K Goutham
6. 1 | 2 0 1 0
ABSTRACT
Signal Processing is undoubtedly the best real time implementation of a specific
problem. Wavelet Transform is a very powerful technique for feature extraction and can be used
along with neural network structures to build computationally efficient models for diagnosis of
Biosignals (ECG in this case). This work utilises the above techniques for diagnosis of an ECG
signal by determining its nature as well as exploring the possibility for real-time implementation
of the above model. Daubechies wavelet transform and multi-layered perceptron are the
computational techniques used for the realisation of the above model. The ECG signals were
obtained from the MIT-BIH arrhythmia database and are used for the identification of four
different types of arrythmias. The identification was implemented real-time in SIMULINK, to
simulate the detection model under test condition and verify its workability.
7. 2 | 2 0 1 0
INTRODUCTION
Electrocardiography deals with the electrical activity of the heart. Monitored by
placing sensors at limb extremities of the subject, the electrocardiogram (ECG) is a record of
the origin and propagation of electrical potential through cardiac muscles. It is considered a
representative signal of cardiac physiology, useful in diagnosing cardiac disorders. [Acharya,
Dua, Bhat, Iyengar, Roo,2002];[Owoski and Linh, 2001]; [Ceylan and Ozbay, 2007].
The medical state of the heart is determined by the shape of the Electrocardiogram,
which contains important pointers to different types of diseases afflicting the heart.
However, the electrocardiogram signals are irregular in nature and occur randomly at
different time intervals during a day. Thus arises the need for continuous monitoring of the
ECG signals, which by nature are complex to comprehend and hence there is a possibility of
the analyst missing vital information which can be crucial in determining the nature of the
disease. Thus computer based automated analysis is recommended for early and accurate
diagnosis. [Acharya, Roo, Dua, Iyengar, Bhat, 2002]
The biggest challenge faced by the models for automatic heart beat classification is
the variability of the ECG waveforms from one patient to another even within the same
person. However, different types of arrhythmias have certain characteristics which are
common among all the patients. Thus the objective of a heart beat classifier is to identify
those characteristics so that the diagnosis can be general and as reliable as possible. One of
such methods which can be reliably used for ECG classification is the use of neural
networks. Neural networks are one of the most efficient pattern recognition tools because of
their high nonlinear structure and tendency to minimise error in test inputs by adapting
itself to the input output pattern and thus establishing a nonlinear relationship between the
input and output.
However the performance of a neural network is highly dependent on the number of
8. 3 | 2 0 1 0
input elements in the computational layers.
A large number of elements would lead to a large number of multiplication and
additions and the network would become expensive on computing resources.
Thus to reduce the number of inputs a pre-processing layer is used. This pre-
processor uses wavelet transform to "smooth" out the ECG waveforms and reduce the
number of samples while preserving all the distinct signal features such as local maxima and
minima. Also the use of wavelet transform makes the model to be implemented easily in real
time processing by the use of FIR filters.
The model so obtained was implemented in a real-time model which was simulated
with Simulink software package. The basic processing strategy is shown below. Each bock
represents a processing milestone. The first block is the pre-processor which was described
previously and the second block is the neural network block which does the actual
processing.
DISCRETE
WAVELET
TRANSFORM
MULTILAYERED
PERCEPTRONINPUT DIAGNOSIS
Fig. 1.1 general processing architecture
9. 4 | 2 0 1 0
BACKGROUND
1. The Electrocardiogram:
Electrocardiography (ECG) is a transthoracic interpretation of the electrical
activity of the heart over time captured and externally recorded by skin electrodes.
[ECG simplified by Ashwin Kumar]. The ECG works by detecting and amplifying the
tiny electrical changes on the skin that are caused when the heart muscle "depolarises"
during each heartbeat. At rest, each heart muscle cell has a charge across its outer wall,
or cell membrane. Reducing this charge towards zero is called de-polarisation, which
activates the mechanisms in the cell that cause it to contract. During each heartbeat a
healthy heart will have an orderly progression of a wave of depolarisation that is
triggered by the cells in the sinoatrial node, spreads out through the atrium, passes
through "intrinsic conduction pathways" and then spreads all over the ventricles. This is
detected as tiny rises and falls in the voltage between two electrodes placed either side of
the heart which is displayed as a wavy line either on a screen or on paper. This display
indicates the overall rhythm of the heart and weaknesses in different parts of the heart
muscle.
Usually more than 2 electrodes are used and they can be combined into a
number of pairs. (For example: Left arm (LA), right arm (RA) and left leg (LL)
electrodes form the pairs: LA+RA, LA+LL, RA+LL) The output from each pair is
known as a lead. Each lead is said to look at the heart from a different angle. Different
types of ECGs can be referred to by the number of leads that are recorded, for example
3-lead, 5-lead or 12-lead ECGs (sometimes simply "a 12-lead"). A 12-lead ECG is one
in which 12 different electrical signals are recorded at approximately the same time and
will often be used as a one-off recording of an ECG, typically printed out as a paper
copy. 3- and 5-lead ECGs tend to be monitored continuously and viewed only on the
screen of an appropriate monitoring device, for example during an operation or whilst
being transported in an ambulance. There may, or may not be any permanent record of
10. 5 | 2 0 1 0
a 3- or 5-lead ECG depending on the equipment used.
The ECG waveform is shown in the figure 2.1here. The ECG waveform can be
broken down into three important parts each denoting a peak on the either side
represented by P, Q, R, S, T. each of them
represent a vital processes in the heart and
those processes have been illustrated in table
2.1. In case of a disease afflicting the heart,
the waves get distorted according to the area
which is not functioning normally. Thus by
inspection of the ECG waveform the nature
of disease can be found out easily.
TABLE 2.1
Feature Description Duration
RR
interval
The interval between an R wave and the next R wave is the inverse of the heart
rate. Normal resting heart rate is between 50 and 100 bpm
0.6 to
1.2s
P wave
During normal atrial depolarization, the main electrical vector is directed from
the SA node towards the AV node, and spreads from the right atrium to the left
atrium. This turns into the P wave on the ECG.
80ms
PR
interval
The PR interval is measured from the beginning of the P wave to the beginning
of the QRS complex. The PR interval reflects the time the electrical impulse
takes to travel from the sinus node through the AV node and entering the
ventricles. The PR interval is therefore a good estimate of AV node function.
120 to
200ms
PR
segment
The PR segment connects the P wave and the QRS complex. This coincides with
the electrical conduction from the AV node to the bundle of His to the bundle
branches and then to the Purkinje Fibers. This electrical activity does not
produce a contraction directly and is merely traveling down towards the
ventricles and this shows up flat on the ECG. The PR interval is more clinically
relevant.
50 to
120ms
QRS
complex
The QRS complex reflects the rapid depolarization of the right and left
ventricles. They have a large muscle mass compared to the atria and so the QRS
complex usually has a much larger amplitude than the P-wave.
80 to
120ms
J-point
The point at which the QRS complex finishes and the ST segment begins. Used
to measure the degree of ST elevation or depression present.
N/A
11. 6 | 2 0 1 0
Feature Description Duration
ST
segment
The ST segment connects the QRS complex and the T wave. The ST segment
represents the period when the ventricles are depolarized. It is isoelectric.
80 to
120ms
T wave
The T wave represents the repolarization (or recovery) of the ventricles. The
interval from the beginning of the QRS complex to the apex of the T wave is
referred to as the absolute refractory period. The last half of the T wave is referred
to as the relative refractory period (or vulnerable period).
160ms
ST
interval
The ST interval is measured from the J point to the end of the T wave. 320ms
QT
interval
The QT interval is measured from the beginning of the QRS complex to the end of
the T wave. A prolonged QT interval is a risk factor for ventricular tachyarrhythmia
and sudden death. It varies with heart rate and for clinical relevance requires a
correction for this, giving the QTc.
300 to
430ms
U wave
The U wave is not always seen. It is typically low amplitude, and, by definition,
follows the T wave.
Table 2.1: the description and duration of each wave in the ECG waveform
12. 7 | 2 0 1 0
MATERIAL AND METHODS
For efficient recognition and less computationally expensive method of pattern
recognition the multi-layered perceptron based neural network is used here along with
wavelet compression of the input signal.
The preclassification task is performed by performing wavelet compression which
reduces the number of samples by a factor of 4.The multi-layered perceptron based neural
network is sued for further processing and final pattern classification.
The input data is clustered as a result of training of the neural network. In the end
a SIMULINK model was developed and it implemented all the result obtained in the
offline analysis. SIMULINK model helped in developing a scheme for real time
implementation of the above process.
The Wavelet Transform
Frequency spectrum analysis is one of the best methods for analysis of a signal.
However Fourier analysis of the signal can decompose the signal into sinusoidal entities
and the filters implementing it remove certain frequencies from the spectrum. However,
this might not be useful in preserving the peaks(local maxima and minima) of the signal
and may lead to loss of important data pointers which are crucial to diagnosis of the
condition.
However, if wavelet transform based data compression is used the peaks (as well as
gaps, though they are not important in this case) can be preserved. This will preserve the
important pointer and structures in the signal. The wavelet transform can be seen as an
extension to the Fourier transform save it works on a multiscale basis unlike Fourier
transform which works on a single domain(frequency domain).The multiscale structure
of the wavelet transform decomposes the signal into a number of scales, each scale
representing a particular coarseness under study.[Ceylan and Ozbay ,2007].
13. 8 | 2 0 1 0
The process of wavelet transform is shown. After extensive experimentation on
different types of wavelets the Daubechies wavelet of order 2 is used for the purpose of
pre-processing of the signal as it produced the best "Smoothing" effect and preserved
important local maxima and minima. The wavelet coefficients were computed using
MATLAB software package. The structure of the wavelet transform is given in figure 3.1.
Fig 3.1. wavelet decomposition structure
The Daubechies wavelet transform
Named after Ingrid Daubechies, the Daubechies wavelets are a family
of orthogonal wavelets defining a discrete wavelet transform and characterized by a
maximal number of vanishing moments for some given support. With each wavelet type
of this class, there is a scaling function (also called father wavelet) which generates an
orthogonal multi resolution analysis.
Daubechies wavelets are chosen to have the highest number A of vanishing
moments, for given support width N=2A, and among the 2A−1 possible solutions the one
is chosen whose scaling filter has external phase. Both the scaling sequence and the
wavelet sequence will here be normalized to have summed equal 2 and sum of squares
equal 2. In some applications, they are normalised to have sum√2, so that both sequences
and all shifts of them by an even number of coefficients are orthonormal to each other.
Using the general representation for a scaling sequence of an orthogonal discrete
wavelet transform with approximation order A,
h(n)
g(n)
h(n)
g(n)
Original
signal
downsampler
A1
A2
D1
D2
14. 9 | 2 0 1 0
a(Z) = 21-A(1+Z)Ap(Z),
with N=2A, p having real coefficients, p (1) =1 and degree ( p) =A-1, one can
write the orthogonality condition as
ܽ(ܼ)ܽ(ܼ) + ܽ(ܼିଵ)ܽ(−ܼିଵ) = 4
OR
(2 − ܺ)
ܲ(ܺ) + ܺ
ܲ(2 − ܺ) = 2
, …..(1)
with the Laurent-polynomial ܺ =
ଵ
ଶ
(2 − ܼ − ܼିଵ
) generating all symmetric
sequences and ܺ(−ܼ) = 2 − ܺ(ܼ). Further, P(X) stands for the symmetric Laurent-
polynomial P(X(Z)) = p(Z)p(Z − 1).
Since X(eiw) = 1 − cos(w) and p(eiw)p(e − iw) = | p(eiw) | 2, P takes
nonnegative values on the segment [0,2]. Equation (1) has one minimal solution for
each A, which can be obtained by division in the ring of truncated power series in X,.
ܲ(ܺ) = ൬
ܣ + ݇ − 1
ܣ − 1
൰
ିଵ
ୀ
2ି
ܺି
Obviously, this has positive values on (0,2)
The homogeneous equation for (1) is antisymmetric about X=1 and has thus the
general solution XA
(X − 1)R((X − 1)2
), with R some polynomial with real coefficients.
That the sum
P(X) = PA(X) + XA(X − 1)R((X − 1)2)
shall be nonnegative on the interval [0,2] translates into a set of linear restrictions
on the coefficients of R. The values of P on the interval [0,2] are bounded by some
quantity 4A − r
, maximizing r results in a linear program with infinitely many inequality
conditions.
15. 10 | 2 0 1 0
To solve P(X(Z)) = p(Z)p(Z − 1) for p one uses a technique called spectral
factorization resp. Fejer-Riesz-algorithm. The polynomial P(X) splits into linear factors
ܲ(ܺ) = (ܺ − ߤଵ) … (ܺ − ߤே), where N=A+1+2deg(R).
Each linear factor represents a Laurent-polynomial
ܺ(ܼ) − ߤ = −
1
2
ܼ + 1 − ߤ −
1
2
ܼିଵ
that can be factored into two linear factors. One can assign either one of the two
linear factors to p (Z), thus one obtains 2N
possible solutions. For external phase one
chooses the one that has all complex roots of p(Z) inside or on the unit circle and is thus
real.
Different orders of Daubechies wavelet transforms are shown below
Fig 3.2:Daubechies wavelet of order 2
16. 11 | 2 0 1 0
Fig 3.3:Daubechies wavelet of order 3
Fig 3.4:Daubechies wavelet of order 4
17. 12 | 2 0 1 0
Multilayered Perceptron based Neural Network
Neural Networks today are synonymous with pattern recognition. The parallel
processing and non-linear architecture make them ideal for finding relationship between
the input and output through various adaptive algorithms. The type of neural network
model used here is Multilayer Perceptron based. There are three layers namely the input,
output and the hidden layer having 52, 3 and 10 elements respectively. Each element in
the hidden and the output layer is fully connected to the elements in the input and
hidden layer respectively.
Back propagation algorithm utilises the Levenberg-Marquardt algorithm for
training of the network. It is a quasi-Newton method and is designed to approach the
second order training speed without having to compute the Hessian matrix. When the
performing function has a form of squares (as in typical feed forward networks).The
Hessian matrix can be approximated as H = J
J and gradient computed as G = J
݁. J is
Jacobian matrix containing first derivatives of network errors with respect to weights and
biases. Levenberg-Marquardt algorithm uses the following learning rule.
ݓାଵ = ݓ − ሾܬ்
ܬ + ߤܫሿିଵ
ܬ்
݁
Where µ is the gradient descent.
The main advantage of the Levenberg-Marquardt algorithm is the very fast
training. For instance, in this work during the offline analysis the network was seen to
train within 70 epochs. However the algorithm is very resource expensive and uses a large
amount of memory. To reduce the memory usage, a modification is made to the
algorithm by modifying the generation of the Hessian matrix performed by the formula
given below.
ܪ = ܬ்
ܬ = ሾܬଵ
்
ܬଶ
்ሿ
ܬଵ
ܬଶ
൨ = ܬଵ
்
ܬଵ + ܬଶ
்
ܬଶ
18. 13 | 2 0 1 0
This reduces the memory usage significantly though the training time is increased.
This is used only when the memory availability is less as in small low cost circuits.
The structure of the multi-layered perceptron network is shown below.
fig 3.5 Multilayered perceptron based neural network
19. 14 | 2 0 1 0
RESULTS AND INFERENCE
1. Wavelet decomposition
The objective of this analysis was to determine the wavelet that produces result that is the
closest to the original signal. Different types of wavelet analysis are shown below.
a. Daubechies decomposition of order 1 (Same as Harr wavelet decomposition):
The stages of wavelet decomposition using Daubechies wavelet of order 1 is as follows
the transform was performed on the first 250 samples
Fig 4.1 wavelet decomposition using Daubechies wavelet of order 1
20. 15 | 2 0 1 0
Inference: this wavelet decomposition provides a step output and on higher levels of
decomposition, the signal loses its identity, as the peaks are lost. Thus, this signal is unfit for
use in neural networks.
b. Daubechies wavelet of order 2: As in the former case, this wavelet decomposition
also considers first 250 samples and the results from decomposition are shown below.
Fig 4.2 : wavelet decomposition of second order up to level 3
Inference: The result obtained here can be seen to be smoothed until the second level of
the wavelet transform. After the second level of transform, the signal becomes distorted.
21. 16 | 2 0 1 0
Two level wavelet transform is more apt for processing as the best smoothing can be
achieved with 2 levels without sacrificing accuracy. The number of samples is reduced to
one-fourth of the initial number of samples.
C. Daubechies wavelet decomposition of order 3: As in the former case, this wavelet
decomposition also takes first 250 samples into account and the results from
decomposition are shown below.
Fig 4.1 wavelet decomposition using Daubechies wavelet of order 1
Inference: this decomposition leads to a large deviation from original hence not
recommended for further processing.
22. 17 | 2 0 1 0
Note: Higher order wavelet decomposition produces more deviation and was not taken into
account for further processing. Thus, the second order Daubechies wavelet transform was
used for further processing in the neural network.
2. Neural network analysis:
The samples, obtained after preprocessing in the preprocessor, which utilizes wavelet
transform to reduce the number of samples to one-fourth of the original, were fed to the
neural network for final processing. The neural network was trained to obtain the final
weights and biases. The performance parameters during training of the network are
shown below.
a. Training performance: given by variation of mean square error with number of
epochs. The following graph is obtained.
Fig 4.4 :neural network training performance
23. 18 | 2 0 1 0
It can be observed that the mean square error decreases rapidly till epoch 30 and after
that decreases slowly. A total number of 293 epochs are shown in the in the above
figure. The rapid decrease in the mean square error can be attributed to the use of the
Levenberg-Marquardt algorithm for training of the neural network.
b. Other performance parameters and training state: the following training state
parameters are also obtained during the Neural Network analysis.
Fig 4.5: training state parameters during the training of the network
Note: the weights obtained from the above network are utilized for implementation in
SIMULINK model for real-time implementation.
Note: the accuracy during the training of the network was found to be 99.5%. (Only 1
out of the 200 samples tested returned a negative result). The recognition accuracy can be
increased by training the above neural network with a very large number of samples.
24. 19 | 2 0 1 0
SIMULINK MODEL IMPLEMENTATION
The offline analysis was followed by the implementation of the results so obtained
in a SIMULINK model for simulation of a real-time implementation of the model. The
different parts of the model are described below.
Basically the system structure is same as described in the first chapter. However
the final SIMULINK structure looks like the figure given below. The additional blocks
are due to the different input and output compatibility of the blocks in SIMULINK. For
example, the DWT block takes in a frame based input of frame size of two elements and
the input blocks outputs a frame size of one. Therefore, a buffer block is needed to
convert the frame size from one to two.
Fig : SIMULINK diagram for the whole structure
The individual blocks and their usage are described below.
(i) Input block: This block holds the input values of the signal, which is passed on to
the preprocessing blocks for wavelet decomposition. The frame size of the signal is
one, which is incompatible with the DWT block, which takes in an input of frame
rate 2. Thus, some other blocks are added to the network in between those blocks.
(ii) Matrix flip block: the function of this block is to flip the matrix input from input
block to form a column matrix. This makes the input format of the buffer
compatible with the input block.
(iii) Data type conversion: converts the data format for compatibility.
(iv) Buffer: buffer adjusts the frame rate so that the frame rate is same as that required
by the DWT block.
25. 20 | 2 0 1 0
(v) DWT block: This part does the actual processing in the preprocessing part of the
model. As in the offline analysis, the DWT block decomposes the signal into
approximation and detail parts using the Daubechies wavelet of order 2, and in
the process reduces the number of samples to one fourth of the original. This
makes processing at the neural network part lot simpler.
(vi) Unbuffer: This block has the same functionality as the buffer block; the only
difference being the frame size is converted from two to one, which makes it
compatible with the neural network block.
(vii) Neural network block: the neural network block recognizes the type of
arrhythmia, thus diagnosing the disease.
(viii) Scope: used for visualization of the output.
Some other figures that form a part of the SIMULINK implementation are shown below.
27. 22 |2010
Simulink results
The results of simulation are shown below. The simulation is done with the help of a
synthesized input signal. The topmost figure is the source signal and the subsequent are
normal sinus atrial fibrillation and supraventricular arrhythmia respectively.
fig 5.2 SIMULINK simulation result
28. Ϯϯ ͮ Ϯ Ϭ ϭ Ϭ
CONCLUSION AND FUTURE WORK
Realtime ECG processing holds a great potential for development. Automated
arrhythmia detection could not only help in early detection of diseases but also in
reducing the workload of the medical data analyst. The aim of Discrete Wavelet
Transform is to reduce the number of samples and eventually reducing the complexity of
the neural network and the computation time of the neural network.
However, modern technology has made intensive processing highly feasible and
economical. Computing platforms such as FPGA, PLD, DSP and microprocessors can be
used for interfacing the model with the actual Holter Device.
Of all devices mentioned above FPGA is the most promising because of its speed
and flexibility. FPGA platforms provide great support for many types of interfacing
standards and are hence recommended for implementation in a realtime scenario.
Microprocessors, though not as fast as the FPGA platform also holds great promise as
they are relatively in expensive and are easier to program.
The algorithms given here utilise data from 19 subjects. Training the model with a
large number of test data would greatly enhance the accuracy and hence the reliability of
the system.
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