The week's pick
Reseach Article
Rhythm Disorders ñ Heart Beat Classification of an Elec-trocardiogram Signal
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 39 - Number 11 |
| Year of Publication: 2012 |
| Authors: Faiza Iftikhar, Ayesha Shams, Arfa Dilawari |
10.5120/4867-7292
|
Faiza Iftikhar, Ayesha Shams, Arfa Dilawari . Rhythm Disorders ñ Heart Beat Classification of an Elec-trocardiogram Signal. International Journal of Computer Applications. 39, 11 ( February 2012), 38-44. DOI=10.5120/4867-7292
Abstract
Arrhythmia disorders play a vital role in heart diseases pro-gression. Detection and treatment of arrhythmia disorders can help indirectly in controlling the heart disease. In hospitals, physicians classify the beats after examining the electrocardi-ogram (ECG) report. Sometimes, physicians are not that expert to diagnose the arrhythmias correctly and accurately. In these circumstances, there is a need for automatic and accurate heart beat classifier which takes the ECG signal as an input and classify it into different rhythm disorders. In this paper, an arrhythmia disorder classifier is designed and developed using Feedforward Backpropagation neural network. The supervised network is trained based on the features extracted from the ECG databases of MIT-BIH. The trained network will classify the beats into premature atrial/ventricular contraction (PAC/PVC), left/right bundle branch block (LBBB/RBBB), paced beat and normal beat. This automatic system will make the treatment faster even in the absence of expert physicians.
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