An Efficient Automated Algorithm for Distinguishing Normal and Abnormal ECG Signal

Abstract Objective The present study aims to simulate an alarm system for online detecting normal electrocardiogram (ECG) signals from abnormal ECG so that an individual's heart condition can be accurately and quickly monitored at any moment, and any possible serious dangers can be prevented. Materials and methods First, the data from Physionet database were used to analyze the ECG signal. The data were collected equally from both males and females, and the data length varied between several seconds to several minutes. The heart rate variability (HRV) signal, which reflects heart fluctuations in different time intervals, was used due to the low spatial accuracy of ECG signal and its time constraint, as well as the similarity of this signal with the normal signal in some diseases. In this study, the proposed algorithm provided a return map as well as extracted nonlinear features of the HRV signal, in addition to the application of the statistical characteristics of the signal. Then, artificial neural networks were used in the field of ECG signal processing such as multilayer perceptron (MLP) and support vector machine (SVM), as well as optimal features, to categorize normal signals from abnormal ones. Results In this paper, the area under the curve (AUC) of the ROC was used to determine the performance level of introduced classifiers. The results of simulation in MATLAB medium showed that AUC for MLP and SVM neural networks was 89.3% and 94.7%, respectively. Also, the results of the proposed method indicated that the more nonlinear features extracted from the ECG signal could classify normal signals from the patient. Conclusion The ECG signal representing the electrical activity of the heart at different time intervals involves some important information. The signal is considered as one of the common tools used by physicians to diagnose various cardiovascular diseases, but unfortunately the proper diagnosis of disease in many cases is accompanied by an error due to limited time accuracy and hiding some important information related to this signal from the physicians' vision leading to the risks of irreparable harm for patients. Based on the results, designing the proposed alarm system can help physicians with higher speed and accuracy in the field of diagnosing normal people from patients and can be used as a complementary system in hospitals.

[1]  Anthony Kaveh,et al.  Automated classification of coronary atherosclerosis using single lead ECG , 2013, 2013 IEEE Conference on Wireless Sensor (ICWISE).

[2]  R. Ganesh Kumar,et al.  Investigation and classification of ECG beat using Input Output Additional Weighted Feed Forward Neural Network , 2013, 2013 International Conference on Signal Processing , Image Processing & Pattern Recognition.

[3]  Richard B. Reilly,et al.  Automatic classification of ECG beats using waveform shape and heart beat interval features , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[4]  Marina Ronzhina,et al.  ECG features and methods for automatic classification of ventricular premature and ischemic heartbeats: A comprehensive experimental study , 2017, Scientific Reports.

[5]  Majid Pouladian,et al.  Detection ischemic episodes from electrocardiogram signal using wavelet transform , 2009 .

[6]  U. Rajendra Acharya,et al.  Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network , 2017, Knowl. Based Syst..

[7]  U. Rajendra Acharya,et al.  An automated diagnosis of depression using three-channel bandwidth-duration localized wavelet filter bank with EEG signals , 2018, Cognitive Systems Research.

[8]  U. Rajendra Acharya,et al.  A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension , 2017, Pattern Recognit. Lett..

[9]  G. Bortolan,et al.  Ranking of pattern recognition parameters for premature ventricular contractions classification by neural networks , 2004, Physiological measurement.

[10]  R. Kavitha,et al.  An Effective Classification of Heart Rate Data using PSO-FCM Clustering and Enhanced Support Vector Machine , 2015 .

[11]  Krishnanand Balasundaram,et al.  A classification scheme for ventricular arrhythmias using wavelets analysis , 2012, Medical & Biological Engineering & Computing.

[12]  Esmaeil Hajinasrollah,et al.  Non-linear feature extraction from HRV signal for mortality prediction of ICU cardiovascular patient , 2016, Journal of medical engineering & technology.

[13]  W.J. Tompkins,et al.  A patient-adaptable ECG beat classifier using a mixture of experts approach , 1997, IEEE Transactions on Biomedical Engineering.

[14]  U. Rajendra Acharya,et al.  An accurate sleep stages classification system using a new class of optimally time-frequency localized three-band wavelet filter bank , 2018, Comput. Biol. Medicine.

[15]  K. I. Ramachandran,et al.  Heart murmur detection and classification using wavelet transform and Hilbert phase envelope , 2015, 2015 Twenty First National Conference on Communications (NCC).

[16]  Jacqueline J. George,et al.  Heart disease diagnostic graphical user interface using fractal dimension , 2013, 2013 INTERNATIONAL CONFERENCE ON COMPUTING, ELECTRICAL AND ELECTRONIC ENGINEERING (ICCEEE).

[17]  M. Ashtiyani,et al.  Heart Rate Variability Classification using Support Vector Machine and Genetic Algorithm , 2018, Journal of biomedical physics & engineering.

[18]  Seyed Kamaledin Setarehdan,et al.  New algorithm of mortality risk prediction for cardiovascular patients admitted in intensive care unit. , 2015, International journal of clinical and experimental medicine.

[19]  Seyed Kamaledin Setarehdan,et al.  Analysis of heart rate variability as a predictor of mortality in cardiovascular patients of intensive care unit , 2015 .

[20]  S. K. Setarehdan,et al.  Detection of myocardial infarction using high-frequency QRS components , 2011, 2011 19th Iranian Conference on Electrical Engineering.

[21]  Muhammad Arif,et al.  Automatic Detection and Localization of Myocardial Infarction Using Back Propagation Neural Networks , 2010, 2010 4th International Conference on Bioinformatics and Biomedical Engineering.

[22]  Ali Khazaee,et al.  Heart Beat Classification Using Particle Swarm Optimization , 2013 .

[23]  R I Kitney,et al.  Biomedical signal processing (in four parts) , 1990, Medical and Biological Engineering and Computing.

[24]  Yi-Li Tseng,et al.  Comparison of Support-Vector Machine and Sparse Representation Using a Modified Rule-Based Method for Automated Myocardial Ischemia Detection , 2016, Comput. Math. Methods Medicine.

[25]  Ram Bilas Pachori,et al.  Optimal duration-bandwidth localized antisymmetric biorthogonal wavelet filters , 2017, Signal Process..

[26]  Aboul Ella Hassanien,et al.  Combining Support Vector Machine and Elephant Herding Optimization for Cardiac Arrhythmias , 2018, ArXiv.

[27]  U. Rajendra Acharya,et al.  Application of higher-order spectra for the characterization of Coronary artery disease using electrocardiogram signals , 2017, Biomed. Signal Process. Control..

[28]  Mohamed Moustafa Azmy.  Classification of normal and abnormal heart sounds using new mother wavelet and support vector machines , 2015, 2015 4th International Conference on Electrical Engineering (ICEE).

[29]  R. Pallas-Areny,et al.  Automatic detection of ECG ventricular activity waves using continuous spline wavelet transform , 2005, 2005 2nd International Conference on Electrical and Electronics Engineering.

[30]  U. Rajendra Acharya,et al.  Analysis of knee-joint vibroarthographic signals using bandwidth-duration localized three-channel filter bank , 2018, Comput. Electr. Eng..

[31]  Li Shi,et al.  Recognition of ST segment of electrocardiogram based on wavelet transform , 2007 .

[32]  Ram Bilas Pachori,et al.  A parametrization technique to design joint time-frequency optimized discrete-time biorthogonal wavelet bases , 2017, Signal Process..

[33]  Manish Sharma,et al.  Application of an optimal class of antisymmetric wavelet filter banks for obstructive sleep apnea diagnosis using ECG signals , 2018, Comput. Biol. Medicine.

[34]  H. Farhadi,et al.  Heart rate variability as a biomarker for epilepsy seizure prediction. , 2017, Bratislavske lekarske listy.

[35]  Hadi Seyedarabi,et al.  Discriminant Analysis Between Myocardial Infarction Patients and Healthy Subjects Using Wavelet Transformed Signal Averaged Electrocardiogram and Probabilistic Neural Network , 2013, Journal of medical signals and sensors.

[36]  U. Rajendra Acharya,et al.  MMSFL-OWFB: A novel class of orthogonal wavelet filters for epileptic seizure detection , 2018, Knowl. Based Syst..

[37]  U. Rajendra Acharya,et al.  A novel automated diagnostic system for classification of myocardial infarction ECG signals using an optimal biorthogonal filter bank , 2018, Comput. Biol. Medicine.

[38]  J. S. Sahambi,et al.  DSP based ST-segment analysis: the wavelet approach , 1997, Proceedings of the 1997 16 Southern Biomedical Engineering Conference.

[39]  J. Lucas,et al.  Summary health statistics for U.S. adults: national health interview survey, 2012. , 2014, Vital and health statistics. Series 10, Data from the National Health Survey.

[40]  J. Moraes,et al.  A real time QRS complex classification method using Mahalanobis distance , 2002, Computers in Cardiology.