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.

提出了一种新颖的模型 ECGMamba，利用双向状态空间模型 (BiSSM) 增强分类效率，通过结合一系列时间序列建模技术来提高性能并保持推断效率，实验结果表明 ECGMamba 在 ECG 分类中有效平衡了效果和效率，取得了竞争性的性能。

ECGMamba：面向高效心电图分类的 BiSSM 研究

ECGMamba: Towards Efficient ECG Classification with BiSSM

This paper study provides a novel contribution to the field of signal
processing and DL for ECG signal analysis by introducing a new feature
representation method for ECG signals. The proposed method is based on
transforming time frequency 1D vectors into 2D images using Gramian Angular
Field transform. Moving on, the classification of the transformed ECG signals
is performed using Convolutional Neural Networks (CNN). The obtained results
show a classification accuracy of 97.47% and 98.65% for anomaly detection.
Accordingly, in addition to improving the classification performance compared
to the state-of-the-art, the feature representation helps identify and
visualize temporal patterns in the ECG signal, such as changes in heart rate,
rhythm, and morphology, which may not be apparent in the original signal. This
has significant implications in the diagnosis and treatment of cardiovascular
diseases and detection of anomalies.

该研究提出了一种新的特征表示方法，将时间频率 1D 向量转换为 2D 图像，并利用卷积神经网络对转换后的心电信号进行分类，从而提高了分类性能并帮助识别和可视化心电信号的时间模式，具有诊断和治疗心血管疾病以及检测异常的重要意义。