Exploiting Interactivity and Heterogeneity for Sleep Stage Classification Via Heterogeneous Graph Neural Network

IEEE International Conference on Acoustics Speech and Signal Processing, pp. 1–5

Abstract

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.

Authors 7

1. Ziyu Jia

   Beijing Jiaotong University

   Affiliation as printed

   Beijing Jiaotong University,School of Computer and Information Technology,China

   School of Computer and Information Technology, Beijing Jiaotong University, China

2. Youfang Lin

   Beijing Jiaotong University

   Affiliation as printed

   Beijing Jiaotong University,School of Computer and Information Technology,China

   School of Computer and Information Technology, Beijing Jiaotong University, China

3. Yuhan Zhou

   Beijing Jiaotong University

   Affiliation as printed

   Beijing Jiaotong University,School of Computer and Information Technology,China

   School of Computer and Information Technology, Beijing Jiaotong University, China

4. Xiyang Cai

   University of California, Los Angeles

   Affiliation as printed

   University of California,Samueli School of Engineering,Los Angeles,USA

   Samueli School of Engineering, University of California, Los Angeles, USA

5. Peng Zheng

   Beijing Jiaotong University

   Affiliation as printed

   Beijing Jiaotong University,School of Computer and Information Technology,China

   School of Computer and Information Technology, Beijing Jiaotong University, China

6. Qiang Li Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Germany

   RWTH Aachen University, Germany

7. Jing Wang

   Beijing Jiaotong University

   Affiliation as printed

   Beijing Jiaotong University,School of Computer and Information Technology,China

   School of Computer and Information Technology, Beijing Jiaotong University, China