Article ; Online: A Deep Learning Scheme for Detecting Atrial Fibrillation Based on Fusion of Raw and Discrete Wavelet Transformed ECG Features.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
2022 Volume 2022, Page(s) 1024–1027
Abstract: Atrial fibrillation is the most common sustained cardiac arrhythmia and the electrocardiogram (ECG) is a powerful non-invasive tool for its clinical diagnosis. Automatic AF detection remains a very challenging task due to the high inter-patient ... ...
| Abstract | Atrial fibrillation is the most common sustained cardiac arrhythmia and the electrocardiogram (ECG) is a powerful non-invasive tool for its clinical diagnosis. Automatic AF detection remains a very challenging task due to the high inter-patient variability of ECGs. In this paper, an automatic AF detection scheme is proposed based on a deep learning network that utilizes both raw ECG signal and its discrete wavelet transform (DWT) version. In order to utilize the time-frequency characteristics of the ECG signal, first level DWT is applied and both high and low frequency components are then utilized in the 1D CNN network in parallel. If only the transformed data are utilized in the network, original variations in the data may not be explored, which also contains useful information to identify the abnormalities. A multi-phase training scheme is proposed which facilitates parallel optimization for efficient gradient propagation. In the proposed network, features are directly extracted from raw ECG and DWT coefficients, followed by 2 fully connected layers to process features furthermore and to detect arrhythmia in the recordings. Classification performance of the proposed method is tested on PhysioNet-2017 dataset and it offers superior performance in detecting AF from normal, alternating and noisy cases in comparison to some state-of-the-art methods. |
|---|---|
| MeSH term(s) | Atrial Fibrillation/diagnosis ; Deep Learning ; Diagnosis, Computer-Assisted/methods ; Electrocardiography/methods ; Humans ; Wavelet Analysis |
| Language | English |
| Publishing date | 2022-09-09 |
| Publishing country | United States |
| Document type | Journal Article |
| ISSN | 2694-0604 |
| ISSN (online) | 2694-0604 |
| DOI | 10.1109/EMBC48229.2022.9870829 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
More links
Kategorien
Order via subito
This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.
Inter-library loan at ZB MED
Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.