Biczuk Bartosz, Żurek Sebastian, Jurga Szymon, Turska Elżbieta, Guzik Przemysław, Piskorski Jarosław
Institute of Physics, University of Zielona Góra, 65-069 Zielona Góra, Poland.
The Doctoral School of Exact and Technical Sciences, University of Zielona Góra, 65-417 Zielona Góra, Poland.
Entropy (Basel). 2024 Dec 16;26(12):1100. doi: 10.3390/e26121100.
This study investigates whether heart rate asymmetry (HRA) parameters offer insights into sleep stages beyond those provided by conventional heart rate variability (HRV) and complexity measures. Utilizing 31 polysomnographic recordings, we focused exclusively on electrocardiogram (ECG) data, specifically the RR interval time series, to explore heart rate dynamics associated with different sleep stages. Employing both statistical techniques and machine learning models, with the Generalized Estimating Equation model as the foundational approach, we assessed the effectiveness of HRA in identifying and differentiating sleep stages and transitions. The models including asymmetric variables for detecting deep sleep stages, N2 and N3, achieved AUCs of 0.85 and 0.89, respectively, those for transitions N2-R, R-N2, i.e., falling in and out of REM sleep, achieved AUCs of 0.85 and 0.80, and those for W-N1, i.e., falling asleep, an AUC of 0.83. All these models were highly statistically significant. The findings demonstrate that HRA parameters provide significant, independent information about sleep stages that is not captured by HRV and complexity measures alone. This additional insight into sleep physiology potentially leads to a better understanding of hearth rhythm during sleep and devising more precise diagnostic tools, including cheap portable devices, for identifying sleep-related disorders.
本研究调查了心率不对称性（HRA）参数是否能提供超越传统心率变异性（HRV）和复杂性测量所提供的关于睡眠阶段的见解。利用31份多导睡眠图记录，我们专门关注心电图（ECG）数据，特别是RR间期时间序列，以探索与不同睡眠阶段相关的心率动态。采用统计技术和机器学习模型，以广义估计方程模型为基础方法，我们评估了HRA在识别和区分睡眠阶段及转换方面的有效性。用于检测深度睡眠阶段N2和N3的包含不对称变量的模型，其曲线下面积（AUC）分别为0.85和0.89；用于N2 - R、R - N2转换（即进入和退出快速眼动睡眠）的模型，AUC分别为0.85和0.80；用于W - N1转换（即入睡）的模型，AUC为0.83。所有这些模型在统计学上都具有高度显著性。研究结果表明，HRA参数提供了关于睡眠阶段的重要且独立的信息，这些信息是仅靠HRV和复杂性测量无法获取的。对睡眠生理学的这一额外见解可能有助于更好地理解睡眠期间的心律，并设计出更精确的诊断工具，包括廉价的便携式设备，用于识别与睡眠相关的疾病。
