可靠性(半导体)
计算机科学
睡眠(系统调用)
脑电图
质量(理念)
选择(遗传算法)
睡眠阶段
人工智能
风险分析(工程)
数据科学
多导睡眠图
医学
心理学
神经科学
操作系统
物理
哲学
功率(物理)
认识论
量子力学
作者
Maksym Gaiduk,Ángel Serrano Alarcón,Ralf Seepold,Natividad Martínez Madrid
标识
DOI:10.1007/s13534-023-00299-3
摘要
The scoring of sleep stages is one of the essential tasks in sleep analysis. Since a manual procedure requires considerable human and financial resources, and incorporates some subjectivity, an automated approach could result in several advantages. There have been many developments in this area, and in order to provide a comprehensive overview, it is essential to review relevant recent works and summarise the characteristics of the approaches, which is the main aim of this article. To achieve it, we examined articles published between 2018 and 2022 that dealt with the automated scoring of sleep stages. In the final selection for in-depth analysis, 125 articles were included after reviewing a total of 515 publications. The results revealed that automatic scoring demonstrates good quality (with Cohen's kappa up to over 0.80 and accuracy up to over 90%) in analysing EEG/EEG + EOG + EMG signals. At the same time, it should be noted that there has been no breakthrough in the quality of results using these signals in recent years. Systems involving other signals that could potentially be acquired more conveniently for the user (e.g. respiratory, cardiac or movement signals) remain more challenging in the implementation with a high level of reliability but have considerable innovation capability. In general, automatic sleep stage scoring has excellent potential to assist medical professionals while providing an objective assessment.
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