Reversal No Longer Matters: Attention-Based Arrhythmia Detection with Lead-Reversal ECG Data
Abstract: In this paper, we propose an attention-based multi-scale neural network for arrhythmia detection with lead-reversal electrocardiogram data. Electrocardiogram with a set of 12 waveforms(known as 12-lead ECG) measures myocardial electro-physiological activity, which is important in clinical diagnosis of arrhythmia. However, lead reversals caused by electrode interchange may cause great interference to the interpretation, leading to significant accuracy decline of automated algorithms and possible faulty diagnosis by cardiologists. To address this problem, we design a multi-scale neural network using attention mechanism to reduce the influence of lead reversals. The proposed model is evaluated on a dataset which consists of ECG data from 3,658 patients. In experiments, the proposed method shows high performance on both lead-reversal data and normal data, which proves great robustness of the method.
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