A multi-modal assessment of sleep stages using adaptive Fourier decomposition and machine learning. (September 2022)

• Record Type: Journal Article
• Authors: Fatimah, Binish; Singhal, Amit; Singh, Pushpendra
• Is Part Of: Computers in biology and medicine. Volume 148(2022)
• Publication Date: 2022-09
• Subjects: Sleep stages -- Electroencephalogram (EEG) -- Electromyogram (EMG) -- Electrooculogram (EOG) -- Multi-modal; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.105877
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Healthy sleep is essential for the rejuvenation of the body and helps in maintaining good health. Many people suffer from sleep disorders that are characterized by abnormal sleep patterns. Automated assessment of such disorders using biomedical signals has been an active subject of research. Electroencephalogram (EEG) is a popular diagnostic used in this regard. We consider a widely-used publicly available database and process the signals using the Fourier decomposition method (FDM) to obtain narrowband signal components. Statistical features extracted from these components are passed on to machine learning classifiers to identify different stages of sleep. A novel feature measuring the non-stationarity of the signal is also used to capture salient information. It is shown that classification results can be improved by using multi-channel EEG instead of single-channel EEG data. Simultaneous utilization of multiple modalities, such as Electromyogram (EMG), Electrooculogram (EOG) along with EEG data leads to further enhancement in the obtained results. The proposed method can be efficiently implemented in real-time using fast Fourier transform (FFT), and it provides better classification results than the other algorithms existing in the literature. It can assist in the development of low-cost sensor-based setups for continuous patient monitoring and feedback. Highlights: Fourier decomposition is used for segregating signals into band-limited components. Simultaneous utilization of EEG, EOG and EMG has been studied in detail. The measure for non-stationarity (MNS) is proposed as a feature. Results are analyzed for both subject-independent and subject-dependent frameworks. The proposed algorithm can be implemented in real-time, low-cost IoT setups.