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Identification of Cardiac Arrhythmias using ECG

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Author(s): Pooja Sharma | Ms.Lakhwinder Kaur

Journal: International Journal of Computer Technology and Applications
ISSN 2229-6093

Volume: 03;
Issue: 01;
Start page: 293;
Date: 2012;
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Keywords: Artificial neural networks | Electrocardiogram | Feature extraction.

ABSTRACT
Heart failure is the most common reason of death nowadays, but if the medical help is given directly, the patient’s life may be saved in many cases. Numerous heart diseases can be detected by means of analyzing electrocardiograms (ECG). Artificial Neural Networks (ANN) are computer-based expert systems that have proved to be useful in pattern recognition tasks. ANN can be used in different phases of the decision-making process, from classification to diagnostic procedures. This work concentrates on a review followed by a novel method. The purpose of the review is to assess the evidence of healthcare benefits involving the application of artificial neural networks to the clinical functions of diagnosis, prognosis and survival analysis, in ECG signals. The developed method is based on a compound neural network (CNN), to classify ECGs as normal or carrying an Atrio Ventricular heart Block (AVB). This method uses three different feed forward multilayer neural networks. A single output unit encodes the probability of AVB occurrences. A value between 0 and 0.1 is the desired output for a normal ECG; a value between 0.1 and 1 would infer an occurrence of an AVB. The results show that this compound network has a good performance in detecting AVBs, with a sensitivity of 90.7% and a specificity of 86.05%. The accuracy value is 87.9%.
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