Key result
Novel ECGMamba model demonstrates competitive classification performance and efficient inference on standard ECG databases.
Why the study?
Transformer architectures show promise for ECG classification but suffer from suboptimal inference efficiency.
ECGMamba, a novel State Space Model-based architecture, provides an efficient and accurate alternative to Transformers for ECG classification.
May support efficient ECG classification; leaves open prospective clinical validation before adoption.
Electrocardiogram (ECG) is essential for diagnosing cardiovascular diseases. The Transformer architecture, employing multi-head self-attention, has shown promise in ECG classification due to its strong sequence modeling capabilities. However, Transformers suffer from suboptimal inference efficiency. State Space Models (SSMs) have garnered attention for their efficient inference. This study introduces ECGMamba, a novel ECG classification model based on Mamba, a specialized SSM. ECGMamba comprises the ECG encoder and the Mamba layer centered on Mamba. The encoder uses three one-dimensional convolutions to extract ECG local information. The core of the Mamba layer is the Multi-Path Mamba-based block, which models global contextual semantic information. This architecture ensures efficient inference and significant performance improvements. Experimental results on PTB-XL and CPSC2018 databases demonstrate ECGMamba’s competitive performance and efficient inference capabilities. This study suggests a new approach for efficient and accurate ECG classification.
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Qiang et al. (2024) studied Cardiovascular diseases (ECG classification). ECGMamba vs. Transformers was evaluated on ECG classification performance and inference efficiency. ECGMamba, a novel ECG classification model based on State Space Models, demonstrated competitive performance and efficient inference capabilities on PTB-XL and CPSC2018 databases.
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