SE-ECGNet: A Multi-scale Deep Residual Network with Squeeze-and-Excitation Module for ECG Signal Classification

TL;DR

This paper introduces SE-ECGNet, a multi-scale deep residual network with squeeze-and-excitation modules, designed to improve ECG signal classification by effectively capturing long-term dependencies and multi-lead information, achieving state-of-the-art results.

Contribution

The paper presents the first multi-scale deep residual network that treats multi-lead ECG signals as 2D matrices, combining multi-scale 2D and 1D convolutions for enhanced feature extraction.

Findings

Achieved 99.2% F1-score on MIT-BIH dataset

Achieved 89.4% F1-score on Alibaba dataset

Outperformed existing methods by 2-3% in F1-score

Abstract

The classification of electrocardiogram (ECG) signals, which takes much time and suffers from a high rate of misjudgment, is recognized as an extremely challenging task for cardiologists. The major difficulty of the ECG signals classification is caused by the long-term sequence dependencies. Most existing approaches for ECG signal classification use Recurrent Neural Network models, e.g., LSTM and GRU, which are unable to extract accurate features for such long sequences. Other approaches utilize 1-Dimensional Convolutional Neural Network (CNN), such as ResNet or its variant, and they can not make good use of the multi-lead information from ECG signals.Based on the above observations, we develop a multi-scale deep residual network for the ECG signal classification task. We are the first to propose to treat the multi-lead signal as a 2-dimensional matrix and combines multi-scale 2-D convolution blocks with 1-D convolution blocks for feature extraction. Our proposed model achieves 99.2% F1-score in the MIT-BIH dataset and 89.4% F1-score in Alibaba dataset and outperforms the state-of-the-art performance by 2% and 3%, respectively, view related code and data at https://github.com/Amadeuszhao/SE-ECGNet