{"title":"基于脑电信号的自动睡眠阶段综述","authors":"Xiaoli Zhang , Xizhen Zhang , Qiong Huang , Yang Lv , Fuming Chen","doi":"10.1016/j.bbe.2024.06.004","DOIUrl":null,"url":null,"abstract":"<div><p><span><span><span>Sleep disorders have increasingly impacted healthy lifestyles. Accurate scoring of sleep stages is crucial for diagnosing patients with sleep disorders. The precision of sleep staging differs notably between healthy individuals and those with </span>sleep apnea<span> (SA). SA disrupts the regularity of sleep stages, affecting the performance of sleep stage detection and influencing the accuracy of sleep staging, thereby impacting sleep quality assessment results. The study compares the accuracy of sleep staging between healthy individuals and SA patients using the same algorithm, revealing variations in performance based on different severities of sleep apnea. This suggests limitations in the </span></span>generalization ability<span><span> of current sleep staging methods. Accordingly, researchers are working to develop sleep staging methods that can diminish the impact of sleep apnea and exhibit better generalization capabilities. Furthermore, the study emphasizes the advantages of automated methods over manual scoring due to being less subjective and resource-intensive, making them more suitable for practical applications. The emphasis is on recent research findings on automatic sleep stage classification based on electroencephalography (EEG). The study outlines potential applications and distinctions of various algorithm models rooted in </span>machine learning and </span></span>deep learning within the context of sleep staging. These methods are applied to the well-known public EEG dataset Sleep-EDF. The study applies four widely studied algorithms to the single-channel EEG of 20 subjects, comparing the results of the models’ automatic sleep staging with the manual sleep staging annotations by clinical experts.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 3","pages":"Pages 651-673"},"PeriodicalIF":5.3000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A review of automated sleep stage based on EEG signals\",\"authors\":\"Xiaoli Zhang , Xizhen Zhang , Qiong Huang , Yang Lv , Fuming Chen\",\"doi\":\"10.1016/j.bbe.2024.06.004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><span><span><span>Sleep disorders have increasingly impacted healthy lifestyles. Accurate scoring of sleep stages is crucial for diagnosing patients with sleep disorders. The precision of sleep staging differs notably between healthy individuals and those with </span>sleep apnea<span> (SA). SA disrupts the regularity of sleep stages, affecting the performance of sleep stage detection and influencing the accuracy of sleep staging, thereby impacting sleep quality assessment results. The study compares the accuracy of sleep staging between healthy individuals and SA patients using the same algorithm, revealing variations in performance based on different severities of sleep apnea. This suggests limitations in the </span></span>generalization ability<span><span> of current sleep staging methods. Accordingly, researchers are working to develop sleep staging methods that can diminish the impact of sleep apnea and exhibit better generalization capabilities. Furthermore, the study emphasizes the advantages of automated methods over manual scoring due to being less subjective and resource-intensive, making them more suitable for practical applications. The emphasis is on recent research findings on automatic sleep stage classification based on electroencephalography (EEG). The study outlines potential applications and distinctions of various algorithm models rooted in </span>machine learning and </span></span>deep learning within the context of sleep staging. These methods are applied to the well-known public EEG dataset Sleep-EDF. 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引用次数: 0
摘要
睡眠障碍对健康生活方式的影响越来越大。对睡眠阶段进行准确评分对于诊断睡眠障碍患者至关重要。健康人和睡眠呼吸暂停(SA)患者的睡眠分期精确度明显不同。睡眠呼吸暂停会破坏睡眠阶段的规律性,影响睡眠阶段检测的性能,影响睡眠分期的准确性,从而影响睡眠质量评估结果。该研究比较了健康人和 SA 患者使用相同算法进行睡眠分期的准确性,结果显示,不同严重程度的睡眠呼吸暂停会导致性能差异。这表明目前的睡眠分期方法在推广能力方面存在局限性。因此,研究人员正在努力开发能够减少睡眠呼吸暂停影响并表现出更好的概括能力的睡眠分期方法。此外,该研究还强调了自动方法相对于人工评分的优势,因为自动方法主观性较低,且不需要大量资源,因此更适合实际应用。重点是基于脑电图(EEG)的自动睡眠阶段分类的最新研究成果。研究概述了各种算法模型在睡眠分期方面的潜在应用和区别,这些算法模型植根于机器学习和深度学习。这些方法被应用于著名的公共脑电图数据集 Sleep-EDF。研究将四种广泛研究的算法应用于 20 名受试者的单通道脑电图,并将模型的自动睡眠分期结果与临床专家的手动睡眠分期注释结果进行比较。
A review of automated sleep stage based on EEG signals
Sleep disorders have increasingly impacted healthy lifestyles. Accurate scoring of sleep stages is crucial for diagnosing patients with sleep disorders. The precision of sleep staging differs notably between healthy individuals and those with sleep apnea (SA). SA disrupts the regularity of sleep stages, affecting the performance of sleep stage detection and influencing the accuracy of sleep staging, thereby impacting sleep quality assessment results. The study compares the accuracy of sleep staging between healthy individuals and SA patients using the same algorithm, revealing variations in performance based on different severities of sleep apnea. This suggests limitations in the generalization ability of current sleep staging methods. Accordingly, researchers are working to develop sleep staging methods that can diminish the impact of sleep apnea and exhibit better generalization capabilities. Furthermore, the study emphasizes the advantages of automated methods over manual scoring due to being less subjective and resource-intensive, making them more suitable for practical applications. The emphasis is on recent research findings on automatic sleep stage classification based on electroencephalography (EEG). The study outlines potential applications and distinctions of various algorithm models rooted in machine learning and deep learning within the context of sleep staging. These methods are applied to the well-known public EEG dataset Sleep-EDF. The study applies four widely studied algorithms to the single-channel EEG of 20 subjects, comparing the results of the models’ automatic sleep staging with the manual sleep staging annotations by clinical experts.
期刊介绍:
Biocybernetics and Biomedical Engineering is a quarterly journal, founded in 1981, devoted to publishing the results of original, innovative and creative research investigations in the field of Biocybernetics and biomedical engineering, which bridges mathematical, physical, chemical and engineering methods and technology to analyse physiological processes in living organisms as well as to develop methods, devices and systems used in biology and medicine, mainly in medical diagnosis, monitoring systems and therapy. The Journal''s mission is to advance scientific discovery into new or improved standards of care, and promotion a wide-ranging exchange between science and its application to humans.