  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.
