Exploiting Interactivity and Heterogeneity for Sleep Stage Classification Via Heterogeneous Graph Neural Network
- Resource Type
- Conference
- Authors
- Jia, Ziyu; Lin, Youfang; Zhou, Yuhan; Cai, Xiyang; Zheng, Peng; Li, Qiang; Wang, Jing
- Source
- ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2023 - 2023 IEEE International Conference on. :1-5 Jun, 2023
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Proteins
Source coding
Message passing
Machine learning
Signal processing
Transformers
Physiology
Sleep Stage Classification
Heterogeneous Graph Neural Network
Multimodal Physiological Signal
- Language
- ISSN
- 2379-190X
Sleep stage classification based on physiological time-series is essential for sleep quality evaluation and the diagnosis of sleep disorders in clinical practice. Existing machine learning studies have achieved adequate results in sleep stage classification. However, those methods neglect the significance of simultaneously capturing the interactivity and heterogeneity of physiological signals. In this paper, we propose a novel Sleep Heterogeneous Graph Neural Network (SleepHGNN) to employ these essential features. The SleepHGNN is a deep graph network consisting of Heterogeneous Graph Transformer layers, which are composed of a Heterogeneous Message Passing module for capturing the heterogeneity and a Target-Specific Aggregation module for capturing the interactivity of physiological signals. The experiments show that the SleepHGNN outperforms the state-of-the-art models on the sleep stage classification task. The source code of SleepHGNN is available at: https://github.com/zhouyh310/SleepHGNN.