Automated detection of heart ailments from 12-lead ECG using complex wavelet sub-band bi-spectrum features

The complex wavelet sub-band bi-spectrum (CWSB) features are proposed for detection and classification of myocardial infarction (MI), heart muscle disease (HMD) and bundle branch block (BBB) from 12-lead ECG. The dual tree CW transform of 12-lead ECG produces CW coefficients at different sub-bands. The higher-order CW analysis is used for evaluation of CWSB. The mean of the absolute value of CWSB, and the number of negative phase angle and the number of positive phase angle features from the phase of CWSB of 12-lead ECG are evaluated. Extreme learning machine and support vector machine (SVM) classifiers are used to evaluate the performance of CWSB features. Experimental results show that the proposed CWSB features of 12-lead ECG and the SVM classifier are successful for classification of various heart pathologies. The individual accuracy values for MI, HMD and BBB classes are obtained as 98.37, 97.39 and 96.40%, respectively, using SVM classifier and radial basis function kernel function. A comparison has also been made with existing 12-lead ECG-based cardiac disease detection techniques.

[1]  В. Е. Дворников,et al.  Тестирование показателей дисперсионного картирования на базе данных «The PTB diagnostic ECG database» , 2010 .

[2]  Jiasong Mu,et al.  Throat polyp detection based on compressed big data of voice with support vector machine algorithm , 2014, EURASIP Journal on Advances in Signal Processing.

[3]  Patrick E. McSharry,et al.  Advanced Methods And Tools for ECG Data Analysis , 2006 .

[4]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  Samarendra Dandapat,et al.  Multiscale Energy and Eigenspace Approach to Detection and Localization of Myocardial Infarction , 2015, IEEE Transactions on Biomedical Engineering.

[6]  Samarendra Dandapat,et al.  Detection of Cardiac Abnormalities from Multilead ECG using Multiscale Phase Alternation Features , 2016, Journal of Medical Systems.

[7]  Maarten L. Simoons,et al.  The third universal definition of myocardial infarction , 2013 .

[8]  L. Edenbrandt,et al.  Acute myocardial infarction detected in the 12-lead ECG by artificial neural networks. , 1997, Circulation.

[9]  Li Sun,et al.  ECG Analysis Using Multiple Instance Learning for Myocardial Infarction Detection , 2012, IEEE Transactions on Biomedical Engineering.

[10]  U. Rajendra Acharya,et al.  Application of higher order statistics for atrial arrhythmia classification , 2013, Biomed. Signal Process. Control..

[11]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[12]  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..

[13]  M. Greenwood.  An Introduction to Medical Statistics , 1932, Nature.

[14]  Michael J Ackerman,et al.  Abnormal electrocardiographic findings in athletes: recognising changes suggestive of cardiomyopathy , 2013, British Journal of Sports Medicine.

[15]  Leontios J. Hadjileontiadis,et al.  EEG-Based Tonic Cold Pain Characterization Using Wavelet Higher Order Spectral Features , 2015, IEEE Transactions on Biomedical Engineering.

[16]  S Dandapat,et al.  A new way of quantifying diagnostic information from multilead electrocardiogram for cardiac disease classification. , 2014, Healthcare technology letters.

[17]  U. RajendraAcharya.  Advances in cardiac signal processing , 2007 .

[18]  M. Arthanari,et al.  Classification of electrocardiogram signals with support vector machines and extreme learning machine , 2011, Neural Computing and Applications.

[19]  Chandan Chakraborty,et al.  Cardiac decision making using higher order spectra , 2013, Biomed. Signal Process. Control..

[20]  Chenn-Jung Huang,et al.  Application of wrapper approach and composite classifier to the stock trend prediction , 2008, Expert Syst. Appl..

[21]  Richard Baraniuk,et al.  The Dual-tree Complex Wavelet Transform , 2007 .

[22]  Mark E Josephson,et al.  Frequency content and characteristics of ventricular conduction. , 2015, Journal of electrocardiology.

[23]  Hagit Shatkay,et al.  Utilizing ECG-Based Heartbeat Classification for Hypertrophic Cardiomyopathy Identification , 2015, IEEE Transactions on NanoBioscience.

[24]  A. Goldberger.  Clinical Electrocardiography: A Simplified Approach , 1977 .

[25]  Madhuchhanda Mitra,et al.  Application of Cross Wavelet Transform for ECG Pattern Analysis and Classification , 2014, IEEE Transactions on Instrumentation and Measurement.

[26]  U. Rajendra Acharya,et al.  Characterization of ECG beats from cardiac arrhythmia using discrete cosine transform in PCA framework , 2013, Knowl. Based Syst..

[27]  Mattias Ohlsson,et al.  Detecting acute myocardial infarction in the 12-lead ECG using Hermite expansions and neural networks , 2004, Artif. Intell. Medicine.

[28]  Manu Thomas,et al.  Automatic ECG arrhythmia classification using dual tree complex wavelet based features , 2015 .

[29]  David Atienza,et al.  A multi-lead ECG classification based on random projection features , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[30]  U. Rajendra Acharya,et al.  Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads , 2016, Knowl. Based Syst..

[31]  Liqing Zhang,et al.  Cardiology knowledge free ECG feature extraction using generalized tensor rank one discriminant analysis , 2014, EURASIP J. Adv. Signal Process..

[32]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .