Single-channel EOG sleep staging on a heterogeneous cohort of subjects with sleep disorders.

IF 2.3 4区 医学 Q3 BIOPHYSICS
Hans van Gorp, M. V. van Gilst, S. Overeem, Sylvie Dujardin, Angelique Pijpers, Bregje van Wetten, Pedro Fonseca, Ruud J. G. van Sloun
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Abstract

OBJECTIVE Sleep staging based on full polysomnography is the gold standard in the diagnosis of many sleep disorders. It is however costly, complex, and obtrusive due to the use of multiple electrodes. Automatic sleep staging based on single-channel electro-oculography (EOG) is a promising alternative, requiring fewer electrodes which could be self-applied below the hairline. EOG sleep staging algorithms are however yet to be validated in clinical populations with sleep disorders. Approach. We utilized the SOMNIA dataset, comprising 774 recordings from subjects with various sleep disorders, including insomnia, sleep-disordered breathing, hypersomnolence, circadian rhythm disorders, parasomnias, and movement disorders. The recordings were divided into train (574), validation (100), and test (100) groups. We trained a neural network that integrated transformers within a U-Net backbone. This design facilitated learning of arbitrary-distance temporal relationships within and between the EOG and hypnogram. Main results. For 5-class sleep staging, we achieved median accuracies of 85.0% and 85.2% and Cohen's kappas of 0.781 and 0.796 for left and right EOG, respectively. The performance using the right EOG was significantly better than using the left EOG, possibly because in the recommended AASM setup, this electrode is located closer to the scalp. The proposed model is robust to the presence of a variety of sleep disorders, displaying no significant difference in performance for subjects with a certain sleep disorder compared to those without. Significance. The results show that accurate sleep staging using single-channel EOG can be done reliably for subjects with a variety of sleep disorders.
对患有睡眠障碍的异质人群进行单通道 EOG 睡眠分期。
目的基于全面多导睡眠图的睡眠分期是诊断许多睡眠障碍的黄金标准。然而,由于需要使用多个电极,这种方法成本高昂、操作复杂且有碍观瞻。基于单通道脑电图(EOG)的自动睡眠分期是一种很有前途的替代方法,所需的电极较少,可自行安装在发际线以下。不过,EOG 睡眠分期算法尚未在患有睡眠障碍的临床人群中得到验证。研究方法我们使用了 SOMNIA 数据集,该数据集由 774 条记录组成,受试者患有各种睡眠障碍,包括失眠、睡眠呼吸障碍、嗜睡、昼夜节律紊乱、寄生虫病和运动障碍。录音被分为训练组(574 份)、验证组(100 份)和测试组(100 份)。我们训练了一个神经网络,该网络在 U-Net 主干网中集成了变压器。这种设计有助于学习 EOG 和催眠图内部和之间任意距离的时间关系。主要结果在 5 级睡眠分期中,左侧和右侧 EOG 的中位准确率分别为 85.0% 和 85.2%,Cohen's kappas 分别为 0.781 和 0.796。右EOG的性能明显优于左EOG,这可能是因为在推荐的AASM设置中,右EOG电极更靠近头皮。所提出的模型对各种睡眠障碍都有很好的适应性,与没有睡眠障碍的受试者相比,患有某种睡眠障碍的受试者的表现没有明显差异。意义重大。研究结果表明,对于患有各种睡眠障碍的受试者,使用单通道 EOG 可以可靠地进行准确的睡眠分期。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Physiological measurement
Physiological measurement 生物-工程:生物医学
CiteScore
5.50
自引率
9.40%
发文量
124
审稿时长
3 months
期刊介绍: Physiological Measurement publishes papers about the quantitative assessment and visualization of physiological function in clinical research and practice, with an emphasis on the development of new methods of measurement and their validation. Papers are published on topics including: applied physiology in illness and health electrical bioimpedance, optical and acoustic measurement techniques advanced methods of time series and other data analysis biomedical and clinical engineering in-patient and ambulatory monitoring point-of-care technologies novel clinical measurements of cardiovascular, neurological, and musculoskeletal systems. measurements in molecular, cellular and organ physiology and electrophysiology physiological modeling and simulation novel biomedical sensors, instruments, devices and systems measurement standards and guidelines.
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