ECG signal classification using Hjorth Descriptor

ECG signal occurs due to heart's electrical activity and helps detect and record people's heart health. Many methods have been developed to classify ECG signal automatically. In this research, Hjorth Descriptor is used as a method for feature extraction. K-Nearest Neighbor (KNN) and Multilayer Perceptron (MLP) are used as classifier in classification stage. Experiment result showed that both K-NN and MLP achieved accuracy up to 100% for 50% of test data. Results of 99.33% accuracy were obtained for 10-fold cross validation. Hence, Hjorth Descriptor generates a good feature related to ECG signal classification process.