Abstract
Transformer-based models (i.e., Fusing-TF and LDTF) have achieved state-of-the-art performance for electrocardiogram (ECG) classification. However, these models may suffer from low training efficiency due to the high model complexity associated with the attention mechanism. In this paper, we present a multi-layer perceptron (MLP) model for ECG classification by incorporating a multi-scale sampling strategy for signal embedding, namely, MS-MLP. In this method, a novel multi-scale sampling strategy is first proposed to exploit the multi-scale characteristics while maintaining the temporal information in the corresponding dimensions. Then, an MLP-Mixer structure with token-mixer and channel-mixer is employed to capture the multi-scale feature and temporal feature from the multi-scale embedding result, respectively. Because of the mixing operation and attention-free MLP structure, our proposed MS- MLP method not only provides better classification performance, but also has a lower model complexity, as compared with transformer-based methods, in terms of experiments performed on the MIT-BIH dataset.