Dihong Jiang1, Yu Ma2, Yuanyuan Wang3. 1. Department of Electronic Engineering, Fudan University, Shanghai 200433, China. Electronic address: jdh769135520@outlook.com. 2. Department of Electronic Engineering, Fudan University, Shanghai 200433, China; Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Shanghai 200032, China. Electronic address: mayu@fudan.edu.cn. 3. Department of Electronic Engineering, Fudan University, Shanghai 200433, China; Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Shanghai 200032, China. Electronic address: yywang@fudan.edu.cn.
Abstract
BACKGROUND AND OBJECTIVE: The recognition of many sleep related pathologies highly relies on an accurate classification of sleep stages. Clinically, sleep stages are usually labelled by sleep experts through visually inspecting the whole-night polysomnography (PSG) recording of patients, wherein electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) play the dominant role. Developing an automatic sleep staging system based on multi-channel physiological signals could relieve the burden of manual labeling by experts, and obtain reliable and repeatable recognition results as well. METHODS: In this work, we find the correlation between the spatial covariance matrices of multi-channel signals and their corresponding sleep stages. Based on that, we propose two novel sleep stage classification methods based on the features extracted from the covariance matrices of multi-channel signals. Sleep stages are classified using a minimum distance classifier according to their corresponding covariance matrices mapped on Riemannian manifolds. An alternative way to classify these covariance matrices is to represent the features of covariance matrices on the tangent space of Riemannian manifolds and classify them with an ensemble learning classifier. After any of these classification methods, a rule-free refinement process is utilized to further optimize the classification results. RESULTS: On the MASS dataset that includes 61 whole-night PSG recordings, both two methods provide satisfactory classification results while the one based on tangent space projection has better performance. On average, an accuracy of 0.812 and a Cohen's Kappa coefficient of 0.722 are obtained under leave-one-subject-out cross validation, using EEG, EOG and EMG signals. Meanwhile, the most effective combinations of EEG channels for sleep staging have been found in this work. CONCLUSIONS: The correlation between spatial covariance matrices of multi-channel signals and their corresponding sleep stages have been found. Features based on that are used for sleep stage classification, and experimental results show the superior performance of proposed methods compared to state-of-the-art works. Results of this work are expected to provide a new vision for dealing with multi-channel or multi-modal signal processing tasks in various applications.
BACKGROUND AND OBJECTIVE: The recognition of many sleep related pathologies highly relies on an accurate classification of sleep stages. Clinically, sleep stages are usually labelled by sleep experts through visually inspecting the whole-night polysomnography (PSG) recording of patients, wherein electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) play the dominant role. Developing an automatic sleep staging system based on multi-channel physiological signals could relieve the burden of manual labeling by experts, and obtain reliable and repeatable recognition results as well. METHODS: In this work, we find the correlation between the spatial covariance matrices of multi-channel signals and their corresponding sleep stages. Based on that, we propose two novel sleep stage classification methods based on the features extracted from the covariance matrices of multi-channel signals. Sleep stages are classified using a minimum distance classifier according to their corresponding covariance matrices mapped on Riemannian manifolds. An alternative way to classify these covariance matrices is to represent the features of covariance matrices on the tangent space of Riemannian manifolds and classify them with an ensemble learning classifier. After any of these classification methods, a rule-free refinement process is utilized to further optimize the classification results. RESULTS: On the MASS dataset that includes 61 whole-night PSG recordings, both two methods provide satisfactory classification results while the one based on tangent space projection has better performance. On average, an accuracy of 0.812 and a Cohen's Kappa coefficient of 0.722 are obtained under leave-one-subject-out cross validation, using EEG, EOG and EMG signals. Meanwhile, the most effective combinations of EEG channels for sleep staging have been found in this work. CONCLUSIONS: The correlation between spatial covariance matrices of multi-channel signals and their corresponding sleep stages have been found. Features based on that are used for sleep stage classification, and experimental results show the superior performance of proposed methods compared to state-of-the-art works. Results of this work are expected to provide a new vision for dealing with multi-channel or multi-modal signal processing tasks in various applications.