Cardiac Arrhythmia Detection By ECG Feature Extraction

Electrocardiogram (ECG) is a noninvasive technique used as a primary diagnostic tool for detecting cardiovascular diseases. One of the important cardiovascular diseases is cardiac arrhythmia. Computer-assisted cardiac arrhythmia detection and classification can play a significant role in the management of cardiac disorders. This paper presents an algorithm developed using Python 2.6 simulation tool for the detection of cardiac arrhythmias e.g. premature ventricular contracture (PVC), right bundle branch block (R or RBBB) and left bundle branch block (L or LBBB) by extracting various features and vital intervals (i.e. RR, QRS, etc) from the ECG waveform. The proposed method was tested over the MIT-BIH Arrhythmias Database.

[1]  Chusak Thanawattano,et al.  Cardiac Arrhythmia Detection based on Signal Variation Characteristic , 2008, 2008 International Conference on BioMedical Engineering and Informatics.

[2]  Asim Loan,et al.  Cardiac arrhythmia detection using instantaneous frequency estimation of ECG signals , 2010, 2010 International Conference on Information and Emerging Technologies.

[3]  Manabendra Bhuyan,et al.  Wavelet and energy based approach for PVC detection , 2009, 2009 International Conference on Emerging Trends in Electronic and Photonic Devices & Systems.

[4]  D. Ge,et al.  Cardiac arrhythmia classification using autoregressive modeling , 2002, Biomedical engineering online.

[5]  Zhi Gang Wang,et al.  Automatic analysis of the high-frequency electrocardiogram , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[6]  A. Luna,et al.  Basic Electrocardiography: Normal and Abnormal ECG Patterns , 2007 .

[7]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[8]  J. S. Sahambi,et al.  Classification of ECG arrhythmias using multi-resolution analysis and neural networks , 2003, TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region.

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