ECGMamba: Towards Efficient ECG Classification with BiSSM

TL;DR

ECGMamba introduces a novel bidirectional state-space model to improve ECG classification efficiency, addressing transformer-based models' computational limitations while maintaining high accuracy on public datasets.

Contribution

The paper presents ECGMamba, a new model using BiSSM and Mamba-based blocks to enhance ECG classification efficiency and effectiveness compared to existing transformer-based approaches.

Findings

Achieves competitive accuracy on ECG datasets

Reduces inference computational complexity

Balances performance and efficiency effectively

Abstract

Electrocardiogram (ECG) signal analysis represents a pivotal technique in the diagnosis of cardiovascular diseases. Although transformer-based models have made significant progress in ECG classification, they exhibit inefficiencies in the inference phase. The issue is primarily attributable to the secondary computational complexity of Transformer's self-attention mechanism. particularly when processing lengthy sequences. To address this issue, we propose a novel model, ECGMamba, which employs a bidirectional state-space model (BiSSM) to enhance classification efficiency. ECGMamba is based on the innovative Mamba-based block, which incorporates a range of time series modeling techniques to enhance performance while maintaining the efficiency of inference. The experimental results on two publicly available ECG datasets demonstrate that ECGMamba effectively balances the effectiveness and efficiency of classification, achieving competitive performance. This study not only contributes to the body of knowledge in the field of ECG classification but also provides a new research path for efficient and accurate ECG signal analysis. This is of guiding significance for the development of diagnostic models for cardiovascular diseases.