Krauss Daniel, Richer Robert, Küderle Arne, Jukic Jelena, German Alexander, Leutheuser Heike, Regensburger Martin, Winkler Jürgen, Eskofier Bjoern M
Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Erlangen, Germany.
Department of Molecular Neurology, Universitätsklinikum Erlangen, Elangen, Germany.
Sleep. 2025 Sep 9;48(9). doi: 10.1093/sleep/zsaf091.
Insufficient sleep quality is directly linked to various diseases, making reliable sleep monitoring crucial for prevention, diagnosis, and treatment. As sleep laboratories are cost- and resource-prohibitive, wearable sensors offer a promising alternative for long-term unobtrusive sleep monitoring at home. Current unobtrusive sleep detection systems are mostly based on actigraphy (ACT) that tend to overestimate sleep due to a lack of movement in short periods of wakefulness. Previous research established sleep stage classification by combining ACT with cardiac information but has not investigated the incorporation of respiration in large-scale studies. For that reason, this work aims to systematically compare ACT-based sleep-stage classification with multimodal approaches combining ACT, heart rate variability (HRV) as well as respiration rate variability (RRV) using state-of-the-art machine- and deep learning algorithms. The evaluation is performed on a publicly available sleep dataset including more than 1000 recordings. Respiratory information is introduced through ECG-derived respiration features, which are evaluated against traditional respiration belt data. Results show that including RRV features improves the Matthews Correlation Coefficient (MCC), with long short-term memory (LSTM) algorithms performing best. For sleep staging based on AASM standards, the LSTM achieved a median MCC of 0.51 (0.16 IQR). Respiratory information enhanced classification performance, particularly in detecting wake and rapid eye movement (REM) sleep epochs. Our findings underscore the potential of including respiratory information in sleep analysis to improve sleep detection algorithms and, thus, help to transfer sleep laboratories into a home monitoring environment. The code used in this work can be found online at https://github.com/mad-lab-fau/sleep_analysis.
睡眠质量不足与多种疾病直接相关，因此可靠的睡眠监测对于预防、诊断和治疗至关重要。由于睡眠实验室成本高昂且资源有限，可穿戴传感器为在家中进行长期非侵入性睡眠监测提供了一种有前景的替代方案。当前的非侵入性睡眠检测系统大多基于活动记录仪（ACT），由于在短时间清醒期间缺乏运动，这种方法往往会高估睡眠时间。先前的研究通过将ACT与心脏信息相结合来建立睡眠阶段分类，但尚未在大规模研究中探讨纳入呼吸信息的情况。因此，这项工作旨在使用最先进的机器学习和深度学习算法，系统地比较基于ACT的睡眠阶段分类与结合ACT、心率变异性（HRV）以及呼吸率变异性（RRV）的多模态方法。评估是在一个包含1000多个记录的公开可用睡眠数据集上进行的。通过心电图衍生的呼吸特征引入呼吸信息，并与传统呼吸带数据进行对比评估。结果表明，纳入RRV特征可提高马修斯相关系数（MCC），其中长短期记忆（LSTM）算法表现最佳。对于基于美国睡眠医学学会（AASM）标准的睡眠分期，LSTM的中位数MCC为0.51（四分位距为0.16）。呼吸信息增强了分类性能，特别是在检测清醒和快速眼动（REM）睡眠阶段方面。我们的研究结果强调了在睡眠分析中纳入呼吸信息以改进睡眠检测算法的潜力，从而有助于将睡眠实验室转变为家庭监测环境。这项工作中使用的代码可在https://github.com/mad-lab-fau/sleep_analysis在线获取。
