Amit Awasthi, Niresh Sharma
firstname.lastname@example.org , email@example.com
Amit Awasthi1, Niresh Sharma2
1 M. Tech. Scholar, Dept. of Computer Science Engineering, RKDF Engineering College, Bhopal
2Assistant Professor, Dept. of Computer Science Engineering, RKDF Engineering College, Bhopal
Volume - 5,
Issue - 1,
Year - 2014
This research work is supervised by ANN based algorithm to classify the ECG waveforms. The ECG waveform gives the almost all information about activity of the heart, which is depending on the electrical activity of the heart. In this paper we are focused only five features of ECG signal P, Q, R, S, T. This is achieved by extracting the various features and duration of ECG waveform P-wave, PR segment, PR interval, QRS Complex, ST segment, T-wave, ST- interval, QTc and QRS voltage. ECG signal and heart rate are used the parameter for detection diseases, most of the data comes from PhysioDataNet and MIT-BIH data base. This research is focused on to find out best neural network structure which classifies the abnormalities of heart diseases. This technique also identifies the normal region for classification of abnormalities; because of ECG waveform is varying from person to person at different condition.
Cite this article:
Amit Awasthi, Niresh Sharma. Possibilities of Using Neural Network for ECG Classification. Research J. Engineering and Tech. 5(1): Jan.-Mar. 2014 page 13-16.
Amit Awasthi, Niresh Sharma. Possibilities of Using Neural Network for ECG Classification. Research J. Engineering and Tech. 5(1): Jan.-Mar. 2014 page 13-16. Available on: https://ijersonline.org/AbstractView.aspx?PID=2014-5-1-5