Uncertainty-Aware Multi-view Arrhythmia Classification from ECG

We propose a deep neural architecture that performs uncertainty-aware multi-view classification of arrhythmia from ECG. Our method learns two different views (1D and 2D) of single-lead ECG to capture different types of information. We use a fusion technique to reduce the conflict between the differe...

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Bibliographic Details
Main Authors: Ashhad, Mohd, Rahmani, Sana, Fayiz, Mohammed, Etemad, Ali, Hashemi, Javad
Format: Conference Proceeding
Language:English
Subjects:
Arrhythmia
ECG
Electrocardiography
Fuses
heartbeat classification
multi-view learning
Neural networks
Noise
Robustness
Uncertainty
uncertainty-aware fusion
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Summary:We propose a deep neural architecture that performs uncertainty-aware multi-view classification of arrhythmia from ECG. Our method learns two different views (1D and 2D) of single-lead ECG to capture different types of information. We use a fusion technique to reduce the conflict between the different views caused by noise and artifacts in ECG data, thus incorporating uncertainty to obtain stronger final predictions. Our framework contains the following three modules (1) a time-series module to learn the morphological features from ECG; (2) an image-space learning module to learn the spatiotemporal features; and (3) the uncertainty-aware fusion module to fuse the information from the two different views. Experimental results on two real-world datasets demonstrate that our framework not only improves the performance on arrhythmia classification compared to the state-of-the-art but also shows better robustness to noise and artifacts present in ECG.
ISSN:2161-4407
DOI:10.1109/IJCNN60899.2024.10650766