Park Jong-Uk, Erdenebayar Urtnasan, Joo Eun-Yeon, Lee Kyoung-Joung
Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju, Gangwon-do 26493, Republic of Korea.
Physiol Meas. 2017 Jun 27;38(7):1441-1455. doi: 10.1088/1361-6579/aa723e.
This paper proposes a method for classifying sleep-wakefulness and estimating sleep parameters using nasal pressure signals applicable to a continuous positive airway pressure (CPAP) device.
In order to classify the sleep-wakefulness states of patients with sleep-disordered breathing (SDB), apnea-hypopnea and snoring events are first detected. Epochs detected as SDB are classified as sleep, and time-domain- and frequency-domain-based features are extracted from the epochs that are detected as normal breathing. Subsequently, sleep-wakefulness is classified using a support vector machine (SVM) classifier in the normal breathing epoch. Finally, four sleep parameters-sleep onset, wake after sleep onset, total sleep time and sleep efficiency-are estimated based on the classified sleep-wakefulness. In order to develop and test the algorithm, 110 patients diagnosed with SDB participated in this study. Ninety of the subjects underwent full-night polysomnography (PSG) and twenty underwent split-night PSG. The subjects were divided into 50 patients of a training set (full/split: 42/8), 30 of a validation set (full/split: 24/6) and 30 of a test set (full/split: 24/6).
In the experiments conducted, sleep-wakefulness classification accuracy was found to be 83.2% in the test set, compared with the PSG scoring results of clinical experts. Furthermore, all four sleep parameters showed higher correlations than the results obtained via PSG (r ⩾ 0.84, p < 0.05). In order to determine whether the proposed method is applicable to CPAP, sleep-wakefulness classification performances were evaluated for each CPAP in the split-night PSG data. The results indicate that the accuracy and sensitivity of sleep-wakefulness classification by CPAP variation shows no statistically significant difference (p < 0.05).
The contributions made in this study are applicable to the automatic classification of sleep-wakefulness states in CPAP devices and evaluation of the quality of sleep.
本文提出一种利用适用于持续气道正压通气（CPAP）设备的鼻压力信号对睡眠-觉醒状态进行分类并估计睡眠参数的方法。
为了对睡眠呼吸障碍（SDB）患者的睡眠-觉醒状态进行分类，首先检测呼吸暂停-低通气和打鼾事件。被检测为SDB的时段被分类为睡眠，并且从被检测为正常呼吸的时段中提取基于时域和频域的特征。随后，在正常呼吸时段使用支持向量机（SVM）分类器对睡眠-觉醒进行分类。最后，基于分类后的睡眠-觉醒状态估计四个睡眠参数——入睡时间、睡眠中觉醒时间、总睡眠时间和睡眠效率。为了开发和测试该算法，110名被诊断为SDB的患者参与了本研究。其中90名受试者进行了整夜多导睡眠图（PSG）检查，20名进行了分夜PSG检查。受试者被分为训练集的50名患者（全夜/分夜：42/8）、验证集的30名患者（全夜/分夜：24/6）和测试集的30名患者（全夜/分夜：24/6）。
在进行的实验中，与临床专家的PSG评分结果相比，测试集中睡眠-觉醒分类准确率为83.2%。此外，所有四个睡眠参数显示出比通过PSG获得的结果更高的相关性（r⩾0.84，p<0.05）。为了确定所提出的方法是否适用于CPAP，在分夜PSG数据中对每个CPAP的睡眠-觉醒分类性能进行了评估。结果表明，CPAP变化导致的睡眠-觉醒分类的准确性和敏感性没有统计学上的显著差异（p<0.05）。
本研究的贡献适用于CPAP设备中睡眠-觉醒状态的自动分类以及睡眠质量评估。
