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.