Multi-scale Progressive Gated Transformer for Physiological Signal Classification

Wei Zhou, Hao Wang, Yiling Zhang, Cheng Long, Yan Yang, Dongjie Wang
Proceedings of The 14th Asian Conference on Machine Learning, PMLR 189:1293-1308, 2023.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v189-zhou23b, title = {Multi-scale Progressive Gated Transformer for Physiological Signal Classification}, author = {Zhou, Wei and Wang, Hao and Zhang, Yiling and Long, Cheng and Yang, Yan and Wang, Dongjie}, booktitle = {Proceedings of The 14th Asian Conference on Machine Learning}, pages = {1293--1308}, year = {2023}, editor = {Khan, Emtiyaz and Gonen, Mehmet}, volume = {189}, series = {Proceedings of Machine Learning Research}, month = {12--14 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v189/zhou23b/zhou23b.pdf}, url = {https://proceedings.mlr.press/v189/zhou23b.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Multi-scale Progressive Gated Transformer for Physiological Signal Classification %A Wei Zhou %A Hao Wang %A Yiling Zhang %A Cheng Long %A Yan Yang %A Dongjie Wang %B Proceedings of The 14th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Emtiyaz Khan %E Mehmet Gonen %F pmlr-v189-zhou23b %I PMLR %P 1293--1308 %U https://proceedings.mlr.press/v189/zhou23b.html %V 189 %X 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.
APA
Zhou, W., Wang, H., Zhang, Y., Long, C., Yang, Y. & Wang, D.. (2023). Multi-scale Progressive Gated Transformer for Physiological Signal Classification. Proceedings of The 14th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 189:1293-1308 Available from https://proceedings.mlr.press/v189/zhou23b.html.

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