This document discusses using independent component analysis (ICA) to separate electrocardiogram (ECG) signals recorded using high-density montages. It conducted experiments on five subjects using a 98-channel ECG system to record signals. An ICA algorithm was used to separate the P-wave, QRS complex, and T-wave components from the mixed signals. Results showed the components could be clearly separated, confirming ICA is an effective tool for high-density ECG analysis.
Day by day the scope & use of the electronics concepts in bio-medical field is increasing step by step. In this paper the review of newly developed concepts is done for the monitoring of the ECG signal. This paper also reviews a power and area efficient electrocardiogram (ECG) acquisition and signal processing application sensor node. Further the study of IoT frame work for ECG monitoring has been carried out. Ms. Dhanashri Yamagekar | Dr. Pradip Bhaskar"Real time ECG Monitoring: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7065.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/7065/real-time-ecg-monitoring-a-review/ms-dhanashri-yamagekar
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 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).
Wavelet based Signal Processing for Compression a Methodology for on-line Tel...iosrjce
IOSR journal of VLSI and Signal Processing (IOSRJVSP) is a double blind peer reviewed International Journal that publishes articles which contribute new results in all areas of VLSI Design & Signal Processing. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & Signal Processing concepts and establishing new collaborations in these areas.
Design and realization of microelectronic systems using VLSI/ULSI technologies require close collaboration among scientists and engineers in the fields of systems architecture, logic and circuit design, chips and wafer fabrication, packaging, testing and systems applications. Generation of specifications, design and verification must be performed at all abstraction levels, including the system, register-transfer, logic, circuit, transistor and process levels
Day by day the scope & use of the electronics concepts in bio-medical field is increasing step by step. In this paper the review of newly developed concepts is done for the monitoring of the ECG signal. This paper also reviews a power and area efficient electrocardiogram (ECG) acquisition and signal processing application sensor node. Further the study of IoT frame work for ECG monitoring has been carried out. Ms. Dhanashri Yamagekar | Dr. Pradip Bhaskar"Real time ECG Monitoring: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7065.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/7065/real-time-ecg-monitoring-a-review/ms-dhanashri-yamagekar
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 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).
Wavelet based Signal Processing for Compression a Methodology for on-line Tel...iosrjce
IOSR journal of VLSI and Signal Processing (IOSRJVSP) is a double blind peer reviewed International Journal that publishes articles which contribute new results in all areas of VLSI Design & Signal Processing. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & Signal Processing concepts and establishing new collaborations in these areas.
Design and realization of microelectronic systems using VLSI/ULSI technologies require close collaboration among scientists and engineers in the fields of systems architecture, logic and circuit design, chips and wafer fabrication, packaging, testing and systems applications. Generation of specifications, design and verification must be performed at all abstraction levels, including the system, register-transfer, logic, circuit, transistor and process levels
A Joint QRS Detection and Data Compression Scheme for Wearable Sensorsecgpapers
Abstract—This paper presents a novel electrocardiogram (ECG)
processing technique for joint data compression and QRS detection
in awireless wearable sensor. The proposed algorithm is aimed
at lowering the average complexity per task by sharing the computational
load among multiple essential signal-processing tasks
needed for wearable devices. The compression algorithm, which is
based on an adaptive linear data prediction scheme, achieves a lossless
bit compression ratio of 2.286x. The QRS detection algorithm
achieves a sensitivity (Se) of 99.64% and positive prediction (+P)
of 99.81% when tested with the MIT/BIH Arrhythmia database.
Lower overall complexity and good performance renders the proposed
technique suitable for wearable/ambulatory ECG devices.
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.
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.
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.
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.
A low-cost electro-cardiograph machine equipped with sensitivity and paper sp...TELKOMNIKA JOURNAL
The price of electrocardiograph (ECG) machine on the market is very high. Currently, the technology used is still very complicated and ineffective, and the ECG machine cannot be connected to other devices. A new development of a low-cost ECG machine with a customized design was needed to integrate the machine with other devices. Therefore, the purpose of this study is to develop a low-cost ECG machine which can be connected to other devices and equipped with sensitivity and paper speed setting. So that portable ECG machines can be produced and used at small clinics in the society. In this study, the main controller of the 12 channels ECG machines was supported by ATMEGA16 microcontroller, that is available on the market at low prices. The main part of the ECG amplifier is built using a high common mode rejection ratio (CMRR) instrumentation amplifier (AD620) and a bandpass filter which the cutoff frequency for highpass filter and lowpass filter are 0.05 Hz and 100 Hz, respectively. In order to complement the previous study, some features were introduced such as selectivity and motor speed option. In this study, 10 participants are involved for data acquisition,and an ECG phantom was used to calibrate the machine. The performance of the ECG machine was evaluated using standard measurement namely relative percentage error (% error) and uncertainty (UA). The result shows that %error from all of the feature is less than 2% and the UA is 0.0 which shows that the ECG machine is feasible for diagnostic purposes.
Performance Evaluation of Percent Root Mean Square Difference for ECG Signals...CSCJournals
Electrocardiogram (ECG) signal compression is playing a vital role in biomedical applications. The signal compression is meant for detection and removing the redundant information from the ECG signal. Wavelet transform methods are very powerful tools for signal and image compression and decompression. This paper deals with the comparative study of ECG signal compression using preprocessing and without preprocessing approach on the ECG data. The performance and efficiency results are presented in terms of percent root mean square difference (PRD). Finally, the new PRD technique has been proposed for performance measurement and compared with the existing PRD technique; which has shown that proposed new PRD technique achieved minimum value of PRD with improved results.
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
A new approach for Reducing Noise in ECG signal employing Gradient Descent Me...paperpublications3
Abstract: ECG is the main tool used by the physicians for identifying and for interpretation of Heart condition. The ECG should be free from noise and of good quality for the correct diagnosis. In real time situations ECG are corrupted by many types of noises. The high frequency noise is one of them. In this thesis, analysis has been carried out the use of neural network for denoising the ECG signal. A multilayer artificial neural network (ANN) is designed. Here gradient descent method (GDM) is used for training of artificial neural network. The noisy ECG signal is given as input to the neural network. The output of neural network is compared with De-noised(original) ECG signal and value of Root Mean Square Error(RMSE) is computed. In training process the weights are updated until the value of RMSE is minimized. Several iteration has to be performed in order to find Minimum Mean Square Error(MMSE). At MMSE network weights are finalized. Subsequently, network parameters are used for Noise reduction. The comparison with other technique shows that the neural networks method is able to better preserve the signal waveform at system output with reduced noise. Our results shows better accuracy in terms of parameters root mean square error, signal to noise ratio and smoothness (RMSE,SNR and R) as compare to GOWT[18].The database has been collected from MIT-BIH arrhythmias database.
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
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
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
A Joint QRS Detection and Data Compression Scheme for Wearable Sensorsecgpapers
Abstract—This paper presents a novel electrocardiogram (ECG)
processing technique for joint data compression and QRS detection
in awireless wearable sensor. The proposed algorithm is aimed
at lowering the average complexity per task by sharing the computational
load among multiple essential signal-processing tasks
needed for wearable devices. The compression algorithm, which is
based on an adaptive linear data prediction scheme, achieves a lossless
bit compression ratio of 2.286x. The QRS detection algorithm
achieves a sensitivity (Se) of 99.64% and positive prediction (+P)
of 99.81% when tested with the MIT/BIH Arrhythmia database.
Lower overall complexity and good performance renders the proposed
technique suitable for wearable/ambulatory ECG devices.
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.
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.
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.
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.
A low-cost electro-cardiograph machine equipped with sensitivity and paper sp...TELKOMNIKA JOURNAL
The price of electrocardiograph (ECG) machine on the market is very high. Currently, the technology used is still very complicated and ineffective, and the ECG machine cannot be connected to other devices. A new development of a low-cost ECG machine with a customized design was needed to integrate the machine with other devices. Therefore, the purpose of this study is to develop a low-cost ECG machine which can be connected to other devices and equipped with sensitivity and paper speed setting. So that portable ECG machines can be produced and used at small clinics in the society. In this study, the main controller of the 12 channels ECG machines was supported by ATMEGA16 microcontroller, that is available on the market at low prices. The main part of the ECG amplifier is built using a high common mode rejection ratio (CMRR) instrumentation amplifier (AD620) and a bandpass filter which the cutoff frequency for highpass filter and lowpass filter are 0.05 Hz and 100 Hz, respectively. In order to complement the previous study, some features were introduced such as selectivity and motor speed option. In this study, 10 participants are involved for data acquisition,and an ECG phantom was used to calibrate the machine. The performance of the ECG machine was evaluated using standard measurement namely relative percentage error (% error) and uncertainty (UA). The result shows that %error from all of the feature is less than 2% and the UA is 0.0 which shows that the ECG machine is feasible for diagnostic purposes.
Performance Evaluation of Percent Root Mean Square Difference for ECG Signals...CSCJournals
Electrocardiogram (ECG) signal compression is playing a vital role in biomedical applications. The signal compression is meant for detection and removing the redundant information from the ECG signal. Wavelet transform methods are very powerful tools for signal and image compression and decompression. This paper deals with the comparative study of ECG signal compression using preprocessing and without preprocessing approach on the ECG data. The performance and efficiency results are presented in terms of percent root mean square difference (PRD). Finally, the new PRD technique has been proposed for performance measurement and compared with the existing PRD technique; which has shown that proposed new PRD technique achieved minimum value of PRD with improved results.
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
A new approach for Reducing Noise in ECG signal employing Gradient Descent Me...paperpublications3
Abstract: ECG is the main tool used by the physicians for identifying and for interpretation of Heart condition. The ECG should be free from noise and of good quality for the correct diagnosis. In real time situations ECG are corrupted by many types of noises. The high frequency noise is one of them. In this thesis, analysis has been carried out the use of neural network for denoising the ECG signal. A multilayer artificial neural network (ANN) is designed. Here gradient descent method (GDM) is used for training of artificial neural network. The noisy ECG signal is given as input to the neural network. The output of neural network is compared with De-noised(original) ECG signal and value of Root Mean Square Error(RMSE) is computed. In training process the weights are updated until the value of RMSE is minimized. Several iteration has to be performed in order to find Minimum Mean Square Error(MMSE). At MMSE network weights are finalized. Subsequently, network parameters are used for Noise reduction. The comparison with other technique shows that the neural networks method is able to better preserve the signal waveform at system output with reduced noise. Our results shows better accuracy in terms of parameters root mean square error, signal to noise ratio and smoothness (RMSE,SNR and R) as compare to GOWT[18].The database has been collected from MIT-BIH arrhythmias database.
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
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
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
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
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
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
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
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
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
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
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.
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.
FPGA based Heart Arrhythmia’s Detection AlgorithmIDES Editor
Electrocardiogram (ECG) signal has been widely used
for heart diagnoses .In this paper, we presents the design of
Heart Arrhythmias Detector using Verilog HDL based on been
mapped on small commercially available FPGAs (Field
Programmable Gate Arrays). Majority of the deaths occurs
before emergency services can step in to intervene. In this
research work, we have implemented QRS detection device
developed by Ahlstrom and Tompkins in Verilog HDL. The
generated source has been simulated for validation and tested
on software Verilogger Pro6.5. We have collected data from
MIT-BIH Arrhythmia Database for test of proposed digital
system and this data have given MIT-BIH data as an input of
our proposed device using test bench software. We have
compared our device output with MATLAB output and
calculating the error percentage and got desire research key
point of RR interval between the peaks of QRS signal. The
proposed system also investigated with different database of
MIT-BIH for detect different heart Arrhythmias and proposed
device give output exactly same according to our QRS detection
algorithm.
A Simple and Robust Algorithm for the Detection of QRS ComplexesIJRES Journal
The objective of this paper is to develop an easy, efficient and robust algorithm for the analysis of electrocardiogram signals. The technique used in this algorithm is based on the use of Moving Average Filters and Adaptive Thresholding for QRS complex detection. Several established ECG databases published on PhysioNet with sampling frequency ranging from 128Hz- 1KHz, were used for analyzing the technique. The accuracy of the algorithm is determined on the basis of two statistical parameters: sensitivity (SE) and Positive Predictivity (+P).
Less computational approach to detect QRS complexes in ECG rhythmsCSITiaesprime
Electrocardiogram (ECG) signals are normally affected by artifacts that require manual assessment or use of other reference signals. Currently, Cardiographs are used to achieve basic necessary heart rate monitoring in real conditions. This work aims to study and identify main ECG features, QRS complexes, as one of the steps of a comprehensive ECG signal analysis. The proposed algorithm suggested an automatic recognition of QRS complexes in ECG rhythm. This method is designed based on several filter structure composes low pass, difference and summation filters. The filtered signal is fed to an adaptive threshold function to detect QRS complexes. The algorithm was validated and results were checked with experimental data based on sensitivity test.
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.
A nonlinearities inverse distance weighting spatial interpolation approach ap...IJECEIAES
Spatial interpolation of a surface electromyography (sEMG) signal from a set of signals recorded from a multi-electrode array is a challenge in biomedical signal processing. Consequently, it could be useful to increase the electrodes' density in detecting the skeletal muscles' motor units under detection's vacancy. This paper used two types of spatial interpolation methods for estimation: Inverse distance weighted (IDW) and Kriging. Furthermore, a new technique is proposed using a modified nonlinearity formula based on IDW. A set of EMG signals recorded from the noninvasive multi-electrode grid from different types of subjects, sex, age, and type of muscles have been studied when muscles are under regular tension activity. A goodness of fit measure (R2) is used to evaluate the proposed technique. The interpolated signals are compared with the actual signals; the Goodness of fit measure's value is almost 99%, with a processing time of 100msec. The resulting technique is shown to be of high accuracy and matching of spatial interpolated signals to actual signals compared with IDW and Kriging techniques.
CLASSIFICATION OF ECG ARRHYTHMIAS USING /DISCRETE WAVELET TRANSFORM AND NEURA...IJCSEA Journal
Automatic recognition of cardiac arrhythmias is important for diagnosis of cardiac abnormalies. Several algorithms have been proposed to classify ECG arrhythmias; however, they cannot perform very well. Therefore, in this paper, an expert system for ElectroCardioGram (ECG) arrhythmia classification is proposed. Discrete wavelet transform is used for processing ECG recordings, and extracting some features, and the Multi-Layer Perceptron (MLP) neural network performs the classification task. Two types of arrhythmias can be detected by the proposed system. Some recordings of the MIT-BIH arrhythmias database have been used for training and testing our neural network based classifier. The simulation results show that the classification accuracy of our algorithm is 96.5% using 10 files including normal and two arrhythmias.
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.
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.
Feature extraction of electrocardiogram signal using machine learning classif...IJECEIAES
In the various field of life person identification is an essential and important task. This helps for the investigation of criminal activities and used in various type of forensic applications like surveillance. For biometric recognition iris, face, voice and fingerprint have a limited fabrication and from there the exact decision regarding liveliness of the subject can be drawn. The aim of the approach is to construct a biometric recognition system based on ECG which processes the raw ECG signal. The entire process is supported by different filters for noise elimination and ECG characteristics waves gone through time domain analysis. Based on the analysis an efficient feature extraction model is developed where several best P-QRS-T signal parts are taken and the positions of the fragmented signals are normalized depends on the priorities of their positions. The calculation of domain features done 72 times. It checks the data sets (train and test) and from feature vector matching to each of the individual signal, separately. The performance and utility of the system are analyzed and feature vectors are examined by different classification algorithms of machine learning. The leading algorithms like K-nearest neighbor, artificial neural network and support vector machine are used to classify different features of ECG, and it is tested using standard cardiac database i.e. the MIT-BIH ECG -ID database.
An ECG Compressed Sensing Method of Low Power Body Area NetworkNooria Sukmaningtyas
Aimed at low power problem in body area network, an ECG compressed sensing method of low
power body area network based on the compressed sensing theory was proposed. Random binary
matrices were used as the sensing matrix to measure ECG signals on the sensor nodes. After measured
value is transmitted to remote monitoring center, ECG signal sparse representation under the discrete
cosine transform and block sparse Bayesian learning reconstruction algorithm is used to reconstruct the
ECG signals. The simulation results show that the 30% of overall signal can get reconstruction signal
which’s SNR is more than 60dB, each numbers in each rank of sensing matrix can be controlled below 5,
which reduces the power of sensor node sampling, calculation and transmission. The method has the
advantages of low power, high accuracy of signal reconstruction and easy to hardware implementation.
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.
International Journal of Computational Engineering Research(IJCER)
Jq2517021708
1. V.K.Srivastava, Dr. Devendra Prasad / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1702-1708
CAPTURING ECG SIGNALS BY ICA
V.K.Srivastava, Dr. Devendra Prasad
Associate Professor, Deptt. Of Computer Sc.& Engg., HCTM Technical campus, Kaithal, Haryana, India
Associate Professor, Deptt. Of Computer Sc.& Engg., M.M. University, Ambala, Haryana, India
I. INTRODUCTION
Study of electrocardiogram (ECG) signals II. ANALYSIS METHODS
plays a very vital role to diagnose the Basically our works depend on the theory
malfunctioning of the heart. Electrocardiogram of ICA, which was adopted to solve the problem of
(ECG) machine permits deduction of many blind source separation [1]. Comon proposed the
electrical and mechanical defects of the heart, by mathematical framework for Independent
measuring potentials on the body surface. The ECG Component Analysis (ICA). Previously ECG
can record non invasive measurements of heart applications focused mainly on two directions :- De
activity, due to the confounds, created by mixing of Lathauwer first applied ICA to separate fetal ECG
volume-conducted signals at the body surface from maternal body surface ECG recordings [2][3],
electrodes, however, a healthy subject could have successfully addressing a longstanding problem.
abnormal heart rhythm and a known cardiac- ICA was used to remove the artifacts from ECG
impaired instead of a normal heart rhythm. Thus, recordings [4]-[6] and also used to remove artifacts
diagnoses, based on visual observations of recorded from both animal [7] and human [8]. But it also to
ECG signals, may not be accurate. So keeping this, be noted that they only made use of the conventional
we will use and compare the various methods and 12-lead ECG signal from which only ten channels of
techniques to analyze electronic heart signals. One recorded data were available for analysis, also, did
such method is ICA which refers to a family of not instruct subjects to perform multiple activities.
related algorithms that performs blind source ICA separated components accounting for the QRS
separation, separating the recorded signals into complex from those accounting for the P-wave and
component signals that are maximally statistically T-wave. However they were unable to further
independent in some sense. In this research work, separate P-wave from T-wave. Due to this, more
we propose an approach to make use of independent reliable methods are designed to collect high-density
component analysis (ICA) techniques to analyze ECG signals and used ICA to separate the
electronic heart signals recorded using high-density underlying heart activities. ICA concept and
montages. Although ICA and principal component algorithm are as given hereunder:
analysis (PCA), being linear decomposition
technique, have been applied to analyze atrial N source signals s={s1(t),…….sn(t)},
activities in ECG. In these studies only conventional linearly mixed by multiplying a mixing matrix A,
12-lead ECG data were used, and therefore, the produce N mixture signals x={ x1(t),…….xn(t)} =
number of cardiac sources that could be separated As. Given the signal mixtures x, we would like to
was limited to 12. Recordings with more channels recover a version u=Wx. The key assumptions used
have been used by other researchers, but they did in ICA to solve this problem is that the source
not test the application of ICA to their data. Here, signals are as statistically independent as possible
98-channel ECG is recorded to facilitate use of ICA- during the time course of the recordings. Statistical
based spatial filter to separate different heart independence means the joint probability density
activities. A gradient ascent ICA algorithm is function (pdf) of the output sources can be
adopted to separate the P-wave, QRS complex, and factorized to the product of the marginal pdfs of
T-wave. The separated components are further back- each source :
projected to body surface potential maps. The N
experiments are conducted on five subjects to show p(u) = p (u )
i 1
i i (1)
the effectiveness of our approach. Results from five
subjects show that P-, QRS-, and T-waves can be
clearly separated from the recordings, confirming In practice, however, some approximation
that ICA might be an effective and useful tool for to independence or to related quantities must be
high-density ECG analysis, interpretation and used for finite data lengths. In our work, we
diagnosis. employed the gradient ascent algorithm as
The rest of the work is divided as given below : implemented by Makeig [9] based on the infomax
Section II – Analysis Methods algorithm, which has been found to be effective for
Section III- Methodology analysis of biomedical signals.
Section IV- Results and Analysis To evaluate the resulting independent
Section V- Conclusions & Future work components, back-projection is used to plot body
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2. V.K.Srivastava, Dr. Devendra Prasad / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1702-1708
surface projection maps for each component. From 6. LabView software;
the ICA algorithm, we obtained unmixing matrix W. 7. adhesive pads;
The signal mixing matrix will be W-1, i.e., x= W-1u. The Biosemi system incorporated 128 pin-
Let W-1 (:i) denote ith column of W-1 , and u(i:) type electrodes designed for EEG recordings with
denote the ith row of u, then the back-projection of latex head caps dotted with plastic electrode holders
component i,pi is At most, 101 of these were used in our experiments.
pi=W-1(:i)xu(i:). (2) The electrode holders were attached to the skin of
the chest and back of the subject by adhesive pads.
The column vector W-1 (:i) represents the The active Ag/AgCl electrodes do not require skin
relative (signed) weight of the ith component in preparation, but do require electrode gel to act as a
each body surface channel. The back-projection conductor between the skin and the electrodes. The
map of each component may be plotted for each signals recorded at the electrodes are synchronously
subject. The computation of back-projection maps converted to 24-bit digital format at 256 Hz per
is strongly reminiscent of more general body surface channel, and are saved in a computer.
potential mapping (BSPM) techniques, as is the use
of high density electrodes. In BSPM, 32 to 256 B. Procedures
ECG electrodes are used to recird the body surface The experiments were performed on five
potential field created by the beating heart. The electrocardiographically normal volunteer subjects
BSPM system has been used in several hundreds of as approved by an Institutional Review Board. We
patient recording representing most common cardiac started each experiment by attaching 36–49
diseases such as is chemic heart disease and electrode wells to the chest and an equal number to
ventricular and supraventricular arrhythmias [30]– the back of the subject. Then, after an adequate
[32]. The main advantage of BSPM, compared to amount of electrode gel was injected into each well,
12-lead ECG, is the ability to visualize the cardiac the electrodes were plugged in. Three additional
potential distribution across the whole thorax. electrodes were placed on the subject’s left arm,
Several methods have been proposed for detecting right arm, and left leg as unipolar limb leads to form
the vulnerability to life-threatening arrhythmias the Wilson central terminal, and a grounding node
from BSPM recordings and from near-equivalent electrode was placed on the subject’s waist before
magnetocardiographic signals [30], [31]. In such the electrodes are connected to the recording
methods, the signals from each channel are analyzed system. A DRL circuit is used in order to minimize
by signal processing methods such as beat-locked the artifacts. The DRL circuit is able to read what it
averaging, late potential analysis, spectral believes to be noise (usually common mode noise)
turbulence analysis, QRST integral analysis, and and feeds a very small amount of electricity back
spatial parameter mapping. High-density ECG into the body to actively negate this noise. This
recording makes more signals available for analysis, technique is highly effective and has been used in
necessitating automated signal processing methods, medical recording devices such as ECG and EEG.
and making the method more robust to individual Subjects were recorded in a relaxed standing
channel noise or dropouts. However, different from position. Although a supine position is ideal in ECG
previous methods, we visualize the back-projection recording, it was not used in this case because of the
of each individual ECG source to the body surface, risk of damage to the electrodes placed on the back
obtained by ICA decomposition, instead of of the subject. Subjects were asked to do the
visualizing the sum of these sources, i.e., the whole following four kinds of activities:
recorded body surface signals. The back-projected 1) Stand still and breathe normally for 90 s. This
component maps may reflect properties of each was used as a baseline for comparison to
ECG signal component, providing us with more signals recording, during the other activities.
information about different heart activities 2) Breathe in and hold the breath for intervals of 10
compared to BSPM visualizations of the whole s during a period of 90 s. This was
signal mixtures. used to tire the cardiac muscles so that the
contractions of the various muscles would
III. EXPERIMENTS begin to separate as shown in the wave forms.
A. Equipments and Setup 3) Maintain a horse stance for 60 s, followed by
Our experiments use BioSemi’s Active ECG recording. This exercise was used
Two base system. A list of the main necessary to tire subjects so that their heart would beat
equipment is as follows : faster and the differing cardiac muscles
1. 16 x 8-channel amplifier/converter modules; contractions would allow better ICA separations.
2. 4 x 32 pin-type electrodes; 4) Lean toward different orientations. One subject
3. Packer Signa electrode gel; was asked to lean forward and to the
4. 128 electrode holders; left, and the other four subjects were asked to
5. Common mode sense (CMS)/driven right lean to all four cardinal orientations
leg(DRL) electrodes; (forward, backward, left, and right). Ninety-
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3. V.K.Srivastava, Dr. Devendra Prasad / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1702-1708
second recordings were made during each frequency in pass band 40 Hz. Fig. 2 shows some
orientation. recorded raw mixture signals from subject 1. The
Only the last 50 s recording of each ECG left portions of the two figures show the wave forms
recording is analyzed, to avoid the higher noise during standing still, and the right portions the wave
levels in the early phase of the recordings. We used forms following the horse stance. Notice that the
multiple activity conditions to make the heart electrodes on the chest receive much stronger
perform slightly differently based on the concept signals than those on the back. This is reasonable
that ICA could then pick up the subtle differences in since the human heart is closer to the front of the
activity patterns, and thereby better decompose the body.
recorded signals into different components
accounting for P-waves, QRS complex, and T-
waves.
IV. EXPERIMENTAL RESULTS AND
ANALYSIS
We performed six experiments on five
subjects, with 72 electrodes (two 6 × 6
matrices on the chest and the back) and 98
electrodes (two 7 × 7 matrices on the chest and
the back), respectively. Two experiments on subject
1 were performed to verify the stability of our
results. the 98 electrodes (or channels) are numbered
as shown in Fig. 1—note that the left and right
orientations on the chest and back are reversed so
they can form a circle around the body.
Fig.2 Subject 1 mixture waveforms. (a) Still
standing. (b) Horse stance.
A-Separated Components
The ICA algorithm was then applied to the
recorded signals concatenated across all recordings
for each subject. By definition, W is a square matrix;
therefore, 72 and 98 components were produced,
respectively. However, for each subject, only a few
of them had relatively large amplitudes and
meaningful waveforms. We believe that the rest
account for breath related and other noise, and thus,
ignored them in further analysis. From the results,
we made the following observations:
1) The ICA algorithm was able to identify and
separate the overlapping spatial projections of the P-
wave, QRS complex, and T-wave. In the
experiments for all subjects, the T-wave is clearly
separated from the QRS complex. P-waves are
identified in three out of five subjects—through
observing the original recorded signals, we
confirmed that in the two cases where P-waves are
Fig.1 not separated, the P-waves have very small
LAYOUTS OF magnitude and thus are buried in noise. We believe
ELECTRODES this is reasonable because QRS complex, which
In the 6 × 6 case, they are arranged in a reflects the depolarization of the ventricles, is a
similar fashion. For the first subject, five activities complicated process that may consist of multiple
are recorded: still standing, holding breath, horse heart activities. The separation results confirmed
stance, and leaning forward and left; for the other that high-quality solutions can be obtained using a
four subjects, seven activities are recorded, dense channel array. Only the conventional 12-lead
including the additional leaning backward and ECG signals were used in [5]. In their experiments,
rightward poses. The recorded raw data were first P-waves and T-waves could not be separated, and
processed by two-way least square finite impulse the separation of T-waves from QRS-complex is not
response (FIR) filtering, with the low-edge as clear as ours. This suggests that high-density
frequency in pass band 0.1 Hz and the high-edge channels are beneficial in separation. We tried
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4. V.K.Srivastava, Dr. Devendra Prasad / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1702-1708
reducing the number of channels in our experiments wave was also decomposed to two components,
in [33]. We showed that using 12 or 24 channels, the suggesting that the T-wave probably does not have a
ICA algorithm is still able to separate different spatially fixed source.
components, though the quality is compromised. 4) Note that the components accounting for the QRS
Also,the positions of these 12 or 24 channels should com- plex have different peak latencies (in the
be carefully chosen to obtain good results. This figure, they have been sorted in ascending order).
requires further investigation. This could represent a wave propagation sequence.
2) Recordings during or following a variety of Thus, further analysis of these components
activities are highly useful for successful ICA should prove useful, particularly modeling of the
source separation. Decomposing different physical positions of the sources that produce these
combinations of recordings associated with different components. To show that our experimental results
activities showed that recordings during or could be stably reproduced, we conducted the same
following at least three activities were needed to experiments on subject 1 once more. The same
separate the T-waves, and that decomposing more analysis was performed on the obtained data. Very
recordings produced better separation. We believe similar components were obtained from both
this is because different sources exhibit different experiments. To evaluate this similarity, we
behaviors under various circumstances, For calculated the peak time difference of the related
example, the shape of the T-waves and their latency components from both experiments, with respect to
relative to the QRS complex were different for the peak time of the first component. The results are
different activities. In addition, since ICA must learn shown in Table I.
the relatively large unmixing matrix, more data are TABLE 1
required to decompose more channels. For PEAK TIME DIFFERENCE FOR TWO
example, to separate 100 channels, ICA must learn EXPERIMENTSON SUBJECT 1
100 × 100 unmixing weights, typically requiring Compon 1 2 3 4 5 6 7
some multiple of this many data points (e.g., some ent
multiple of 40 s using 256 Hz sampling). For Experime 0 11. 23. 39. 45. 59. 252.
successful decomposition of 100 or more EEG nt 1 m 2 2 0 5 4 4
channels, the scaling factor may be 30 or more. An s ms ms ms ms ms ms
equivalent requirement, for our recordings, would Experime 0 15. 28. 41. 45. 63. 257.
call for using 11–20 min or more of data. Here, our nt 2 m 8 8 3 7 1 9
best results were obtained by concatenating all the s ms ms ms ms ms ms
recordings for the subject (in all, 4–6 min of data). It
is true that concatenating multichannel ECG data The peak latency difference among
acquired with different conditions can potentially components in the two experiments, reflecting the
introduce more components. However, such a propagation speed of the cardiac waves, are very
variety is good to create differences in the close except for the second and third components.
composition of QRS complex or other ECG feature Thus, our results were reproduceable. The
waveforms. For example, the interval between Q discrepancy on the second and third components
and S waves may be changed in different recording may be due to the different body conditions or due
conditions. Thus, when ICA sees the variability of to numerical errors, which need to be further
the Q-S interval, it may separate these two important investigated.
features into two or more different components.
However, if we only decompose channel ECG data B. Back Projection
with single condition, wherein the Q-S interval is The back-projections of different
pretty much fixed, then we would not be able to components are computed according to (2) and
separate the S-wave from the Q. As a result, this compared with the original channel waveforms. The
helps us to more consistently decompose the ECG purpose is to show that the separated components
recordings into more independent components. To account for different sections of the cardiac cycle.
be able to see more independent components is We find that each component accounts for different
exactly the purpose here. Accordingly, to be able to parts of QRS complex, confirming that ICA
acquire more ECG channels would also be essential separates sources of different sections of the cardiac
to increase the possibility to cover the components cycle. As an example, the back-projection maps for
we potentially increased by concatenating ECG each meaningful component of subject 2 are plotted.
channel data recorded in different conditions. In the leftmost column, the column vectors W−1 (:,
3)Typically, ICA separated seven to eight different i), which represent (signed) weight of the ith
meaningful components from the original component in each body surface channel, are
waveforms. Multiple components accounted for the plotted. For each vector, the 98 elements are
QRS components, indicating that the QRS complex organized, reflecting their physical locations in the
is generated by a collection of underlying sources or chest and back of the subjects. The differing
by a moving wave of activity. For subject 2, the T- weights, proportional to different potential values in
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5. V.K.Srivastava, Dr. Devendra Prasad / International Journal of Engineering Research and
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Vol. 2, Issue 5, September- October 2012, pp.1702-1708
the body surface, are mapped to different colors, method is still in its infancy. Fruitful directions for
where red represents the largest magnitude and blue further research include the following:
represent the smallest one. When plotting, we use 1) We design experiments to record high-density (98
finer grid rather than the original two 7 × 7 matrices channels) ECG from the body surface. The recorded
and interpolate the values in order to obtain smooth signals have high quality and are stable when
maps. For each component, we also show the 3-D subjects do various activities. Moreover, multi-
maps, together with the estimated dipole vectors that channel recording is necessary to use ICA to
point from the most negative potential position to separate underlying components.
the most positive potential position. The left 3-D 2) We employ a gradient ascent ICA algorithm to
map is shown from the front view and the right one separate signal components from the recorded body
from a side view. The most right column shows the surface mixtures. For all five subjects, we are able to
time course of the most relevant independent separate the somewhat different but highly
component. In most of the maps, the weights are overlapping projections to the body surface of the P-
concentrated in the right frontal chest, as expected. wave, QRS complex, and T-wave, and to recover
Furthermore, portion of the chest and back receiving the continuous signals projecting from their cardiac
the most concentrated signal is different for different generators, demonstrating that our method is
components. The maximal projection of the effective for identifying individual sources of ECG
different QRS components moves downward and signals.
the ensuing T-wave projection maximum is still 3) Our approach appears promising for
lower. This is consistent with the physiological distinguishing between different underlying cardiac
understanding of the origins of these features. The conditions, compared to traditional ECG analysis.
QRS complex represents the depolarization of the Although results of applying ICA to high-density
ventricle, while the T wave indicates the ECG show great promise, the analysis method is
repolarization of the ventricle. Since the still in its infancy. Fruitful directions for further
depolarization needs to spread from the research include the following.
atrioventricular (AV) node to all parts of the 1) Determining the best placements for the
ventricles, the electrical sources effectively moves electrodes. One possibility is to place more
downward, as reflected in the body surface electrodes on the chest closer to the heart and fewer
projection maps for the different components on other body locations. However, non uniform
accounting for the QRS complex. placement should be based on more knowledge and
understanding of heart physiology.
V. DISCUSSION AND FUTURE WORK 2) The relationship between the number of
Here, we report a novel experimental electrodes, the length of the recording, and the
approach to collecting stable, high-density ECG performance of source separation (and subsequent
signals and application of ICA to separate spatially source localization) should be studied
fixed and temporally independent source activities systematically. In preliminary experiments, we
from the recorded signals. The major contributions attempted to reduce the number of electrodes, but
of this research are as follows. found the results to be inferior [33]. We plan to
1) We design experiments to record high-density (98 perform a more comprehensive study to assess the
channels) ECG from the body surface. The recorded performance of ICA as a function of the number of
signals have high quality and are stable when sensors since in actual clinical applications, it is
subjects do various activities. Moreover, multi- preferable to reduce the number of channels if
channel recording is necessary to use ICA to possible, as this would reduce both the time required
separate underlying components. and the cost of the supplies and equipment.
2) We employ a gradient ascent ICA algorithm to 3) It is of importance to discover to what extent
separate signal components from the recorded body different cardiac sources can be decomposed using
surface mixtures. For all five subjects, we are able recordings during or following different activities.
to separate the somewhat different but highly Currently, we use seven conditions in our
overlapping projections to the body surface of the P- experiments; from these, components accounting for
wave, QRS complex, and T-wave, and to recover the P-wave, QRS complex, and T-wave could be
the continuous signals projecting from their cardiac separated. It should be interesting to determine
generators, demonstrating that our method is whether these components are ―atomic,‖ i.e.,
effective for identifying individual sources of ECG whether each of them is generated by a single
signals. cardiac source, and consequently, cannot be further
3) Our approach appears promising for decomposed. Possible ways of testing this
distinguishing between different underlying cardiac conjecture include adding additional activities to the
conditions, compared to traditional ECG analysis. experiments, acquiring more data, or comparing
Although results of applying ICA to high- results of different algorithms for decomposing the
density ECG show great promise, the analysis mixed body surface signals.
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6. V.K.Srivastava, Dr. Devendra Prasad / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1702-1708
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