Uncertainty-Aware Multi-view Arrhythmia Classification from ECG

TL;DR

This paper introduces an uncertainty-aware multi-view deep learning framework for ECG arrhythmia classification, combining 1D and 2D data views to improve accuracy and robustness against noise.

Contribution

The novel multi-view architecture integrates uncertainty modeling and fusion techniques to enhance arrhythmia classification from ECG signals.

Findings

Improved classification accuracy over state-of-the-art methods.

Enhanced robustness to noise and artifacts in ECG data.

Effective fusion of morphological and spatiotemporal features.

Abstract

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.