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https://dspace.iiti.ac.in/handle/123456789/11875
| Title: | Convolutional Neural Network Based Atrial Fibrillation Detection from ECG Signal |
| Authors: | Phukan, Nabasmita Pachori, Ram Bilas |
| Keywords: | Atrial Fibrillation;Cardiovascular Diseases Diagnosis;Convolutional Neural Network;ECG Arrhythmia Classification;Electrocardiogram (ECG) Signal |
| Issue Date: | 2022 |
| Publisher: | Institute of Electrical and Electronics Engineers Inc. |
| Citation: | Phukan, N., Manikandan, M. S., & Pachori, R. B. (2022). Convolutional neural network based atrial fibrillation detection from ECG signal. Paper presented at the 2022 4th International Conference on Cognitive Computing and Information Processing, CCIP 2022, doi:10.1109/CCIP57447.2022.10058671 Retrieved from www.scopus.com |
| 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. © 2022 IEEE. |
| URI: | https://doi.org/10.1109/CCIP57447.2022.10058671 https://dspace.iiti.ac.in/handle/123456789/11875 |
| Type of Material: | Conference Paper |
| Appears in Collections: | Department of Electrical Engineering |
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