Automated Cardiac Health Diagnosis: A Time-Domain Approach

Cardiological problems are one of the leading causes of human fatality. Electrocardiogram is a major noninvasive tool for monitoring heart conditions. The human vision is not suitable to identify the minute changes in Electrocardiogram wave amplitude and time intervals; hence an automatic diagnostic tool is necessary for precise abnormality detection. This paper presents a classification method to classify seven heartbeat conditions-normal and six classes of abnormalities. The algorithm implements a time domain approach to obtain the statistical features from the Electrocardiogram beats extracted from the arrhythmia database. This objective of this work is to find the suitability of time domain features to arrhythmia classification with machine learning. The statistical features are extracted from raw ECG signal, the time derivative, time integral and 5-point first derivative stencil of the ECG data. The cardiac abnormality classification is implemented with Support Vector Machine. The attained classification accuracy is upto 93% for chosen input feature pairs for binary Support Vector Machine.

[1]  Ligor Nikolla,et al.  WAVELET TRANSFORM APPLIED IN ECG SIGNAL PROCESSING , 2015 .

[2]  Marta Borowska,et al.  Entropy-Based Algorithms in the Analysis of Biomedical Signals , 2015 .

[3]  Sundari Tribhuvanam,et al.  ECG Abnormality Classification with Single Beat Analysis , 2019, 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN).

[4]  Praveen Sankaran,et al.  ECG Beat Classification Using Evidential K -Nearest Neighbours☆ , 2016 .

[5]  Chandan Chakraborty,et al.  Application of principal component analysis to ECG signals for automated diagnosis of cardiac health , 2012, Expert Syst. Appl..

[6]  Gregory T. A. Kovacs,et al.  Robust Neural-Network-Based Classification of Premature Ventricular Contractions Using Wavelet Transform and Timing Interval Features , 2006, IEEE Transactions on Biomedical Engineering.

[7]  Rodrigo Castañeda-Miranda,et al.  DSP-based arrhythmia classification using wavelet transform and probabilistic neural network , 2017, Biomed. Signal Process. Control..

[8]  Cristina Stolojescu.  ECG Signals Classification using Statistical and Time-Frequency Features , 2012 .

[9]  Kumar Narayanan,et al.  The 12-lead electrocardiogram and risk of sudden death: current utility and future prospects. , 2015, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.

[10]  Mohammad Pooyan,et al.  A Novel Approach to Predict Sudden Cardiac Death (SCD) Using Nonlinear and Time-Frequency Analyses from HRV Signals , 2014, PloS one.

[11]  A. Kachouri,et al.  Classification of Cardiac Arrhythmia Using WT, HRV, and Fuzzy C-Means Clustering , 2011 .

[12]  R. Rodrígueza,et al.  Feature extraction of electrocardiogram signals by applying adaptive threshold and principal component analysis , 2015 .

[13]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

[14]  Vps Naidu,et al.  Bearing Health Condition Monitoring: TimeDomain Analysis , 2014 .

[15]  Marti A. Hearst.  Trends & Controversies: Support Vector Machines , 1998, IEEE Intell. Syst..

[16]  William Robson Schwartz,et al.  ECG-based heartbeat classification for arrhythmia detection: A survey , 2016, Comput. Methods Programs Biomed..

[17]  Guodong Guo,et al.  Support Vector Machines Applications , 2014 .