A Computer Based System for ECG Arrhythmia Classification

Biological signals can be classified according to its various characteristics like waveform shape, statistical structure and temporal properties. Among various bioelectric signals, one of the most familiar signal is the ECG. It is a signal derived from the electrical activity of the heart. The heart is an important organ which supplies body with oxygen. ECG is widely used in monitoring the health condition of the human. Cardiac arrhythmias can affect electrical system of the heart muscles and cause abnormal heart rhythms that can lead to insufficient pumping of blood and death risks. An important step towards identifying an arrhythmia is the classification of heartbeats. Modern analysis of electrical activity of the heart uses simple as well as sophisticated algorithms of digital signal processing. With the advent of technology, automatic classification of electrocardiogram signals through human-computer interactive systems has received great attention. This chapter discusses some computer assisted classification techniques based on statistical features extracted from ECG signal.

[1]  K. Pandey,et al.  CFD Analysis of Wall Injection with Large Sized Cavity Based Scramjet Combustion at Mach 2 , 2011 .

[2]  Kandarpa Kumar Sarma,et al.  Classification of ECG using some novel features , 2013, 2013 1st International Conference on Emerging Trends and Applications in Computer Science.

[3]  Cota Navin Gupta,et al.  Neural Network Classification of Premature Heartbeats , 2005 .

[4]  Philip de Chazal,et al.  Automatic classification of heartbeats using ECG morphology and heartbeat interval features , 2004, IEEE Transactions on Biomedical Engineering.

[5]  Imad Barhumi,et al.  ECG signal classification using support vector machine based on wavelet multiresolution analysis , 2012, 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA).

[6]  Faiza Iftikhar,et al.  Rhythm Disorders Heart Beat Classification of an Elec-trocardiogram Signal , 2012 .

[7]  Anthony Choi,et al.  Using neural networks to predict cardiac arrhythmias , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[8]  John D. Enderle,et al.  Introduction to Biomedical Engineering , 1999 .

[9]  G.B. Moody,et al.  The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[10]  Nishchal K. Verma,et al.  Arrhythmia classification using SVM with selected features , 2012 .

[11]  George Manis,et al.  Heartbeat Time Series Classification With Support Vector Machines , 2009, IEEE Transactions on Information Technology in Biomedicine.

[12]  C. Koley,et al.  Wavelet Aided SVM Analysis of ECG Signals for Cardiac Abnormality Detection , 2005, 2005 Annual IEEE India Conference - Indicon.

[13]  Elif Derya Übeyli ECG beats classification using multiclass support vector machines with error correcting output codes , 2007, Digit. Signal Process..

[14]  R. Kumar,et al.  Cardiac arrhythmias detection in an ECG beat signal using fast fourier transform and artificial neural network , 2011 .

[15]  Kandarpa Kumar Sarma,et al.  ECG classification using wavelet subband energy based features , 2014, 2014 International Conference on Signal Processing and Integrated Networks (SPIN).

[16]  Stanislaw Osowski,et al.  Support vector machine-based expert system for reliable heartbeat recognition , 2004, IEEE Transactions on Biomedical Engineering.

[17]  Elif Derya Übeyli,et al.  ECG beat classifier designed by combined neural network model , 2005, Pattern Recognit..

[18]  Nahit Emanet,et al.  ECG beat classification by using discrete wavelet transform and Random Forest algorithm , 2009, 2009 Fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control.

[19]  H. Gholam Hosseini,et al.  A multi-stage neural network classifier for ECG events , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.