A proposed multi-task deep neural network with a Contextual Transformer block achieved an average F1 score of 0.827 on the CPSC2018 dataset and 0.833 on the PTB-XL dataset for ECG classification.
Does a multi-task deep neural network with a Contextual Transformer attention mechanism accurately classify arrhythmias on ECG?
A novel multi-task deep neural network incorporating a Contextual Transformer block demonstrates high accuracy for automated ECG arrhythmia classification on standard public datasets.
Electrocardiogram (ECG) is an efficient and simple method for the diagnosis of cardiovascular diseases and has been widely used in clinical practice. Because of the shortage of professional cardiologists and the popularity of electrocardiograms, accurate and efficient arrhythmia detection has become a hot research topic. In this paper, we propose a new multi-task deep neural network, which includes a shared low-level feature extraction module (i.e., SE-ResNet) and a task-specific classification module. Contextual Transformer (CoT) block is introduced in the classification module to dynamically model the local and global information of ECG feature sequence. The proposed method was evaluated on public CPSC2018 and PTB-XL datasets and achieved an average F1 score of 0.827 on the CPSC2018 dataset and an average F1 score of 0.833 on the PTB-XL dataset.
Geng et al. (Fri,) conducted a other in Arrhythmia. Multi-task deep neural network with Contextual Transformer (CoT) block was evaluated on Average F1 score. A proposed multi-task deep neural network with a Contextual Transformer block achieved an average F1 score of 0.827 on the CPSC2018 dataset and 0.833 on the PTB-XL dataset for ECG classification.