Estimation of Sleep Stages Analyzing Respiratory and Movement Signals

Abstract

The scoring of sleep stages is an essential part of sleep studies. The main objective of this research is to provide an algorithm for the automatic classification of sleep stages using signals that may be obtained in a nonobtrusive way. After reviewing the relevant research, the authors selected a multinomial logistic regression as the basis for their approach. Several parameters were derived from movement and breathing signals, and their combinations were investigated to develop an accurate and stable algorithm. The algorithm was implemented to produce successful results: the accuracy of the recognition of Wake/NREM/REM stages is equal to 73%, with Cohen's kappa of 0.44 for the analyzed 19324 sleep epochs of 30 seconds each. This approach has the advantage of using the only movement and breathing signals, which can be recorded with less effort than heart or brainwave signals, and requiring only four derived parameters for the calculations. Therefore, the new system is a significant improvement for non-obtrusive sleep stage identification compared to existing approaches.

Monitoring of Cardiorespiratory Parameters during Sleep Using a Special Holder for the Accelerometer Sensor

“…Another opportunity for system enhancement is the implementation of different algorithms for sleep disorder detection. One of them could be the algorithm for sleep apnea detection [41].…”

Sensing, computing and intervention for sleep health

“…此外，文献 [30,31]还从EMG中提取特征，并结合EEG、EOG中的特征，融合多模态 中包含的睡眠信息，提升睡眠分期的精度. 一些研究也尝试使用其他信号用于睡眠监测，如单导 联ECG [32] 、呼吸和体动信号 [33] 等. 监督学习依赖睡眠技师对整晚睡眠数据的逐帧标注，为了将大 A c c e p t e d https://engine.scichina.com/doi/10.1360/TB-2021-0911 量未标注数据利用起来，半监督 [34] 、无监督 [35] 以及自监督 [36] 等学习方式也开始被应用于睡眠分 期.…”

Hypoxia Differentially Affects Healthy Men and Women During a Daytime Nap With a Dose-Response Relationship: a Randomized, Cross-Over Pilot Study

“…We monitored napping in a normobaric hypoxia chamber by polysomnographic recording, a standardized and clinically validated methodology, which allows to reliably assess the quality and architecture of sleep ( Silber et al, 2007 ). Although sleep architecture assessment using actimetry or cardiorespiratory monitoring has undergone a remarkable development in recent years ( Fekedulegn et al, 2020 ; Gaiduk et al, 2022 ), polysomnography remains the gold standard. Despite some limitations, its synchronous and integrated approach of cardiovascular, respiratory, and neural parameters makes it the most objective, sensitive, and specific assessment of sleep duration and its phases ( Lee et al, 2022 ).…”