Publication Type:

Conference Proceedings

Source:

2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), p.2388-2391 (2017)

Keywords:

abnormal signals, arrhythmia detection, arrhythmic signal, Classification algorithms, denoised ECG signal, Diseases, Electrocardiography, Feature extraction, frequency domain, Heart, Heart Diseases, medical signal detection, medical signal processing, MIT-BIH arrhythmia database, noise reduction, normal signals, Patient monitoring, patient monitors, R peak detection, signal classification, Signal denoising, Standards, support vector algorithm, Support Vector Machine, Support vector machines, Wavelet denoising

Abstract:

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.

Cite this Research Publication

O. N. Swathi, Ganesan, M., and Lavanya, R., “R Peak Detection and Feature Extraction for the Diagnosis of Heart Diseases”, 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). pp. 2388-2391, 2017.