Detection of Atrial Fibrillation Based on Optimized Electrocardiogram (ECG) Recordings
Sreenivasulu Ummadisetty1, Madhavi T2, Reddi Sridevi3
1Sreenivasulu Ummadisetty, Assistant Professor, Department of EECE, GIT, GITAM Deemed to Be University, Visakhapatnam (Andhra Pradesh), India.
2T. Madhavi, Professor, Department of EECE, GIT, GITAM Deemed to Be University, Visakhapatnam (Andhra Pradesh), India.
3R Sridevi, Assistant Professor, Department of ECE, Dr. L.B College of Engineering, Visakhapatnam (Andhra Pradesh), India.
Manuscript received on 23 November 2019 | Revised Manuscript received on 11 December 2019 | Manuscript Published on 30 December 2019 | PP: 198-203 | Volume-9 Issue-2S3 December 2019 | Retrieval Number: B10491292S319/2019©BEIESP | DOI: 10.35940/ijitee.B1049.1292S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Trial Fibrillation, ECG Recordings, Genetic Algorithm, SVM.
Scope of the Article: Algorithm Engineering