Automated sleep stage classification based on tracheal body sound and actigraphy.

Q1 Medicine
GMS German Medical Science Pub Date : 2019-02-22 eCollection Date: 2019-01-01 DOI:10.3205/000268
Christoph Kalkbrenner, Rainer Brucher, Tibor Kesztyüs, Manuel Eichenlaub, Wolfgang Rottbauer, Dominik Scharnbeck
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引用次数: 17

Abstract

The current gold standard for assessment of most sleep disorders is the in-laboratory polysomnography (PSG). This approach produces high costs and inconveniences for the patients. An accessible and simple preliminary screening method to diagnose the most common sleep disorders and to decide whether a PSG is necessary or not is therefore desirable. A minimalistic type-4 monitoring system which utilized tracheal body sound and actigraphy to accurately diagnose the obstructive sleep apnea syndrome was previously developed. To further improve the diagnostic ability of said system, this study aims to examine if it is possible to perform automated sleep staging utilizing body sound to extract cardiorespiratory features and actigraphy to extract movement features. A linear discriminant classifier based on those features was used for automated sleep staging using the type-4 sleep monitor. For validation 53 subjects underwent a full-night screening at Ulm University Hospital using the developed sleep monitor in addition to polysomnography. To assess sleep stages from PSG, a trained technician manually evaluated EEG, EOG, and EMG recordings. The classifier reached 86.9% accuracy and a Kappa of 0.69 for sleep/wake classification, 76.3% accuracy and a Kappa of 0.42 for Wake/REM/NREM classification, and 56.5% accuracy and a Kappa of 0.36 for Wake/REM/light sleep/deep sleep classification. For the calculation of sleep efficiency (SE), a coefficient of determination r2 of 0.78 is reached. Additionally, subjects were classified into groups of SEs (SE≥40%, SE≥60% and SE≥80%). A Cohen's Kappa >0.61 was reached for all groups, which is considered as substantial agreement. The presented method provides satisfactory performance in sleep/wake and wake/REM/NREM sleep staging while maintaining a simple setup and offering high comfort. This minimalistic approach may address the need for a simple yet reliable preliminary sleep screening in an ambulatory setting.

Abstract Image

Abstract Image

Abstract Image

基于气管身体声音和活动记录仪的自动睡眠阶段分类。
目前评估大多数睡眠障碍的金标准是实验室多导睡眠图(PSG)。这种方法成本高,给患者带来不便。因此,需要一种易于使用和简单的初步筛选方法来诊断最常见的睡眠障碍,并决定是否需要进行多导睡眠监测。先前开发了一种简约的4型监测系统,该系统利用气管体声和活动记录仪来准确诊断阻塞性睡眠呼吸暂停综合征。为了进一步提高该系统的诊断能力,本研究旨在研究是否有可能利用身体声音提取心肺特征和活动图提取运动特征来进行自动睡眠分期。基于这些特征的线性判别分类器使用4型睡眠监测仪进行自动睡眠分期。为了验证,53名受试者在乌尔姆大学医院使用开发的睡眠监测仪和多导睡眠仪进行了通宵筛查。为了从PSG中评估睡眠阶段,训练有素的技术人员手动评估EEG、EOG和EMG记录。该分类器在睡眠/清醒分类中准确率为86.9%,Kappa为0.69;在wake /REM/NREM分类中准确率为76.3%,Kappa为0.42;在wake /REM/浅睡眠/深度睡眠分类中准确率为56.5%,Kappa为0.36。对于睡眠效率(SE)的计算,决定系数r2为0.78。并按SE≥40%、SE≥60%、SE≥80%分组。所有组的科恩Kappa值均大于0.61,这被认为是实质性的一致。该方法在睡眠/清醒和清醒/快速眼动/非快速眼动睡眠阶段提供了令人满意的性能,同时保持了简单的设置和高舒适性。这种极简的方法可能解决了在门诊环境中进行简单而可靠的初步睡眠筛查的需要。
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
GMS German Medical Science
GMS German Medical Science Medicine-Medicine (all)
CiteScore
6.30
自引率
0.00%
发文量
10
审稿时长
11 weeks
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