Performance Investigation of Somfit Sleep Staging Algorithm

IF 3 2区医学 Q2 CLINICAL NEUROLOGY

Nature and Science of Sleep Pub Date : 2024-07-22 DOI:10.2147/nss.s463026

Marcus McMahon, Jeremy Goldin, Elizabeth Susan Kealy, Darrel Joseph Wicks, Eugene Zilberg, Warwick Freeman, Behzad Aliahmad

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Abstract

Purpose: To investigate accuracy of the sleep staging algorithm in a new miniaturized home sleep monitoring device – Compumedics® Somfit. Somfit is attached to patient’s forehead and combines channels specified for a pulse arterial tonometry (PAT)-based home sleep apnea testing (HSAT) device with the neurological signals. Somfit sleep staging deep learning algorithm is based on convolutional neural network architecture.
Patients and Methods: One hundred and ten participants referred for sleep investigation with suspected or preexisting obstructive sleep apnea (OSA) in need of a review were enrolled into the study involving simultaneous recording of full overnight polysomnography (PSG) and Somfit data. The recordings were conducted at three centers in Australia. The reported statistics include standard measures of agreement between Somfit automatic hypnogram and consensus PSG hypnogram.
Results: Overall percent agreement across five sleep stages (N1, N2, N3, REM, and wake) between Somfit automatic and consensus PSG hypnograms was 76.14 (SE: 0.79). The percent agreements between different pairs of sleep technologists’ PSG hypnograms varied from 74.36 (1.93) to 85.50 (0.64), with interscorer agreement being greater for scorers from the same sleep laboratory. The estimate of kappa between Somfit and consensus PSG was 0.672 (0.002). Percent agreement for sleep/wake discrimination was 89.30 (0.37). The accuracy of Somfit sleep staging algorithm varied with increasing OSA severity – percent agreement was 79.67 (1.87) for the normal subjects, 77.38 (1.06) for mild OSA, 74.83 (1.79) for moderate OSA and 72.93 (1.68) for severe OSA.
Conclusion: Agreement between Somfit and PSG hypnograms was non-inferior to PSG interscorer agreement for a number of scorers, thus confirming acceptability of electrode placement at the center of the forehead. The directions for algorithm improvement include additional arousal detection, integration of motion and oximetry signals and separate inference models for individual sleep stages.

Keywords: home sleep apnea testing, polysomnography, forehead electroencephalography, deep learning, interscorer agreement

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Somfit 睡眠分期算法性能调查

目的：研究新型微型家用睡眠监测设备--Compumedics® Somfit--中睡眠分期算法的准确性。Somfit 安装在患者前额，将基于脉搏动脉测压计 (PAT) 的家用睡眠呼吸暂停测试 (HSAT) 设备的指定通道与神经信号相结合。Somfit 睡眠分期深度学习算法基于卷积神经网络架构：110名疑似或已存在阻塞性睡眠呼吸暂停（OSA）并需要复查的转诊患者参加了这项研究，研究涉及同时记录整夜多导睡眠图（PSG）和Somfit数据。记录在澳大利亚的三个中心进行。报告的统计数据包括 Somfit 自动催眠图与 PSG 一致催眠图之间的标准一致度：结果：在五个睡眠阶段（N1、N2、N3、REM 和觉醒）中，Somfit 自动催眠图与 PSG 一致催眠图之间的总体一致率为 76.14（SE：0.79）。不同对睡眠技术专家的 PSG 催眠图之间的一致性百分比从 74.36 (1.93) 到 85.50 (0.64) 不等，同一睡眠实验室的评分者之间的一致性更高。Somfit 和共识 PSG 之间的卡帕估计值为 0.672 (0.002)。睡眠/觉醒分辨的一致性百分比为 89.30 (0.37)。Somfit 睡眠分期算法的准确性随 OSA 严重程度的增加而变化--正常受试者的一致性百分比为 79.67 (1.87)，轻度 OSA 为 77.38 (1.06)，中度 OSA 为 74.83 (1.79)，重度 OSA 为 72.93 (1.68)：对于一些评分者来说，Somfit 和 PSG 催眠图之间的一致性并不亚于 PSG 评分者之间的一致性，从而证实了在前额中央放置电极的可接受性。改进算法的方向包括：增加唤醒检测、整合运动和血氧饱和度信号以及针对各个睡眠阶段的独立推理模型。关键词：家庭睡眠呼吸暂停测试、多导睡眠图、前额脑电图、深度学习、评分者之间的一致性

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来源期刊

Nature and Science of Sleep Neuroscience-Behavioral Neuroscience

CiteScore

5.70

自引率

5.90%

发文量

245

审稿时长

16 weeks

期刊介绍： Nature and Science of Sleep is an international, peer-reviewed, open access journal covering all aspects of sleep science and sleep medicine, including the neurophysiology and functions of sleep, the genetics of sleep, sleep and society, biological rhythms, dreaming, sleep disorders and therapy, and strategies to optimize healthy sleep. Specific topics covered in the journal include: The functions of sleep in humans and other animals Physiological and neurophysiological changes with sleep The genetics of sleep and sleep differences The neurotransmitters, receptors and pathways involved in controlling both sleep and wakefulness Behavioral and pharmacological interventions aimed at improving sleep, and improving wakefulness Sleep changes with development and with age Sleep and reproduction (e.g., changes across the menstrual cycle, with pregnancy and menopause) The science and nature of dreams Sleep disorders Impact of sleep and sleep disorders on health, daytime function and quality of life Sleep problems secondary to clinical disorders Interaction of society with sleep (e.g., consequences of shift work, occupational health, public health) The microbiome and sleep Chronotherapy Impact of circadian rhythms on sleep, physiology, cognition and health Mechanisms controlling circadian rhythms, centrally and peripherally Impact of circadian rhythm disruptions (including night shift work, jet lag and social jet lag) on sleep, physiology, cognition and health Behavioral and pharmacological interventions aimed at reducing adverse effects of circadian-related sleep disruption Assessment of technologies and biomarkers for measuring sleep and/or circadian rhythms Epigenetic markers of sleep or circadian disruption.