Multivariate phase space reconstruction and Riemannian manifold for sleep stage classification

• Published in: Biomedical signal processing and control Vol. 88; p. 105572
• Main Authors: Zhou, Xueling, Wing-Kuen Ling, Bingo, Ahmed, Waqar, Zhou, Yang, Lin, Yuxin, Zhang, Hongtao
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2024
• Subjects: Covariance feature matrix; Multiple physiological signals; Multivariate phase space reconstruction; Riemannian manifold; Sleep stage classification; Covariance feature matrix; Sleep stage classification; Riemannian manifold; Multivariate phase space reconstruction; Multiple physiological signals
• ISSN: 1746-8094
• DOI: 10.1016/j.bspc.2023.105572

•A novel covariance feature matrix architecture using multivariate phase space reconstruction (MPSR) is presented, which captured the geometric properties and the hidden dynamic characteristic of multiple physiological signals.•A novel classification strategy based on Riemannian manifold is employed to further perform the sleep state classification.•Compared to the traditional covariance method using sample covariance matrix (SCM), covariance feature matrix method using MPSR can successfully capture the differences in the spatial information of various sleep states. Sleep was highly imperative in human daily life. However, an increasing number of people were undergoing sleep deprivation and sleep disorders. Sleep stage classification became a highly essential process in sleep scoring. Nevertheless, the visual scoring of arousals during sleep routinely performed by sleep specialists was a challenging exercise. This paper introduced a novel approach for sleep stage classification using covariance feature matrix architecture with multivariate phase space reconstruction (MPSR). The goal was to capture the geometric properties and the hidden dynamic characteristics of multiple physiological signals. The covariance matrices constructed through the MPSR approach were considered as symmetric and positive definite (SPD) matrices, forming a Riemannian manifold space. These SPD matrices in the Riemannian manifold were mapped to the matrices in the tangent space, allowing them to be vectorized and treated as feature vectors in Euclidean space. Finally, an ensemble learning classifier was applied to perform various sleep stage tasks. Our proposed method was evaluated on three benchmark datasets to assess its effectiveness and robustness. For the tasks of both Five class and Six class sleep stages, the proposed approach in ten-fold cross validation achieved high accuracy of 93.57% and 92.56% for the Sleep EDF dataset, 86.36% and 84.18% for the DREAMS Subjects dataset, as well as 88.93% and 88.42% for the Sleep EDF Expanded dataset (95 subjects), respectively. In leave-one-subject-out cross validation, our proposed approach for the tasks of both Five class and Six class sleep stages yielded an accuracy of 84.46% and 80.73% for the Sleep EDF dataset, 82.50% and 79.51% for the DREAMS Subjects dataset, as well as 93.25% and 92.06% for the Sleep EDF Expanded-20 dataset (20 subjects), respectively. Compared to the traditional sample covariance matrix (SCM), the covariance feature matrix using the MPSR method successfully captured the distinction of spatial information among various sleep stages. Moreover, our proposed method obtained good performance without requiring computationally large artifact suppression or a long signal decomposition process.