Atrial Fibrillation Detection Using ECG Recordings Based on Genetic Optimization

Recently, mobile healthcare is emerging using portable and wearable devices. This article uses short-single-lead ECG signals to develop an automatic AF detection system. Heart rate variability (HRV) and frequency analysis are used for feature extraction. The innovative contribution is to develop a Genetic Optimization Algorithm for detecting atrial fibrillation in short ECG recordings. The validation of the results is carried out with the publicly available dataset which comprises short ECG recordings by the support vector machine algorithm. The accuracy varies from 94.2 to 96.5 for N versus A classification under noise levels ranging up to 30 dB. A maximum accuracy of 82.7% is obtained for N versus A versus O.