Abstract
Automatic atrial fibrillation (AF) detection is essential for preventing stroke due to silent heart diseases. In this paper, we propose an automatic AF detection by using electrocardiogram (ECG) signals and convolutional neural network. The proposed method is tested by using the ECG signals from Physionet. On the benchmark performance metrics, the proposed method achieved an average accuracy of 98.26% for detecting AF events. The proposed method can achieve the AF event detection with a processing time of 0.77±0.037 ms with the selection of optimal hyperparameters. The method has great potential in detection of AF events in ECG signal.
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 callDisclaimer: 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.
