Comparison of Different Classification Methods in Real Time Arrhythmia Detection
Abstract
Developments in medicine and increased health awareness has increased demand for health care. However, there is not enough human resources, device, and health care organizations to cover it. This demand can be reduced through telemedicine. Especially the monitoring of the ECG signal of the patient with heart disorders in rural areas is very important. Developments in information technology starts to provide important contribution to the clinical decision support systems for early detection and diagnosis. Features are composed of hjorth descriptors, correlation of signal with an artificial ecg signal, standard deviation of intrinsic. mode function, ratio of power to activity and standard deviation. Different classification methods are compared using these features. Matlab is used for feature extraction and classification. The performance of the method is tested on data used obtained from the PhysioNet database.
Keywords
References
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