Patient monitors with arrhythmia detection will enhance the quality of living of human by aiding in prediction of diseases in much early stage. In this work we have developed an algorithm for classifying the ECG signals into normal and arrhythmic signal. Here we have detected the R peaks from denoised ECG signal with an accuracy of 97.56%. Extracted features from the signal in both time and frequency domain and the signals are classified into normal and abnormal signals using support vector algorithm. The accuracy of the algorithm is tested by applying on MIT-BIH arrhythmia database and we obtained an overall 80% classifier accuracy.
Dr. Lavanya R., Swathi, O. N., and Ganesan, M., “Peak Detection and Feature Extraction for the Diagnosis of Heart Diseases”, Proceedings in IEEE International Conference in Computing, Communications and Informatics. 2017.