Abstract
This article deals with an original approach of acquiring and processing electrocardiogram (ECG) and phonocardiogram (PCG) signals for the diagnosis of cardiac arrhythmias in order to remedy the difficulties encountered with the ECG. Indeed, it integrates an analysis tool based on wavelet transforms for the characterization of ECG signals and a classification system from multilayer perceptron neural network of five categories of cardiac arrhythmias: normal (N), left bundle branch block (LBBB), right bundle branch block (RBBB), premature atrial contraction (PAC) and premature ventricular contraction (PVC). The digitization of the signals is made from an Arduino Mega 2560 board. The realized system has been tested on 6 patients and the results are visualized on a smart phone turning under android operating system. These results are in agreement with medical previsions. Recognition rates are as follows: 100% for class N, 100% for class LBBB, 75% for class RBBB, 90.9% for class PVC and 100% for class PAC. We obtain a generalization rate of 92.9%.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Innovative Research in Science, Engineering and Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
