Oliver Faust1, Hajar Razaghi2, Ragab Barika2, Edward J Ciaccio3, U Rajendra Acharya4. 1. Department of Engineering and Mathematics, Sheffield Hallam University, United Kingdom. Electronic address: oliver.faust@gmail.com. 2. Department of Engineering and Mathematics, Sheffield Hallam University, United Kingdom. 3. Department of Medicine - Cardiology, Columbia University, New York, New York, USA. 4. Department of Electronic & Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore; Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.
Abstract
BACKGROUND AND OBJECTIVE: Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiological signals. Prerequisite for this interpretation is an understanding of the way in which sleep stage changes manifest themselves in the signal waveform. With that understanding it is possible to build automated sleep stage scoring systems. Apart from their practical relevance for automating sleep disorder diagnosis, these systems provide a good indication of the amount of sleep stage related information communicated by a specific physiological signal. METHODS: This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals. RESULTS: Our review shows that all of these signals contain information for sleep stage scoring. CONCLUSIONS: The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost.
BACKGROUND AND OBJECTIVE: Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiological signals. Prerequisite for this interpretation is an understanding of the way in which sleep stage changes manifest themselves in the signal waveform. With that understanding it is possible to build automated sleep stage scoring systems. Apart from their practical relevance for automating sleep disorder diagnosis, these systems provide a good indication of the amount of sleep stage related information communicated by a specific physiological signal. METHODS: This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals. RESULTS: Our review shows that all of these signals contain information for sleep stage scoring. CONCLUSIONS: The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost.
Authors: Fernando Vaquerizo-Villar; Daniel Alvarez; Leila Kheirandish-Gozal; Gonzalo C Gutierrez-Tobal; Veronica Barroso-Garcia; Eduardo Santamaria-Vazquez; Felix Del Campo; David Gozal; Roberto Hornero Journal: IEEE J Biomed Health Inform Date: 2021-08-05 Impact factor: 7.021