Abstract
Atrial fibrillation (AF) is one among the foremost common heart arrhythmias. It is terribly tough to discover unless a precise arrhythmia episode happens throughout the exploration. If the diagnosis and the treatment is delayed the Atrial fibrillation can lead to heart strokes and causes death, therefore automatic detection of AF is an urgent need. The analysis of ECG recordings is considered as one of the typical process of detecting AF. The ECG signals analysed by considering normal rhythm (N), other arrhythmias (O) and Atrial Fibrillation(A) and noises. In this paper the proposed technique is validated by considering open accessible public dataset. In the proposed method initially pre-processing of ECG signal is performed, next extraction of features, optimizing the features using genetic algorithm (GA) and finally classifying using support vector machine (SVM) classifier. The proposed algorithm achieves overall accuracy of 95.8% and by considering top 10 features the rate of accuracy is 96.8% which is better compared to the existing algorithm with an SNR of dB. The experimental results are performed using MATLAB and uggest that by availing the short ECG recording also the detection of AF is obtained accurately.
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 Technology and Exploring Engineering
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.
