Multi-scale Progressive Gated Transformer for Physiological Signal Classification

Physiological signal classification is of great significance for health monitoring and medical diagnosis. Deep learning-based methods (e.g. RNN and CNN) have been used in this domain to obtain reliable predictions. However, the performance of existing methods is constrained by the long-term dependence and irregular vibration of the univariate physiological signal sequence. To overcome these limitations, this paper proposes a Multi-scale Progressive Gated Transformer (MPGT) model to learn multi-scale temporal representations for better physiological signal classification. The key novelties of MPGT are the proposed Multi-scale Temporal Feature extraction (MTF) and Progressive Gated Transformer (PGT). The former adopts coarse- and fine-grained feature extractors to project the input signal data into different temporal granularity embedding spaces and the latter integrates such multi-scale information for data representation. Classification task is then conducted on the learned representations. Experimental results on real-world datasets demonstrate the superiority of the proposed model.