MS-MLP: Multi-scale Sampling MLP for ECG Classification

Wenbo Wang, Jian Guan, Xinyi Che, Wenwu Wang

Abstract: Transformer-based models (i.e., Fusing-Tf and LDTF) have achieved state-of-the-art performance for electro-cardiogram (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.