Department of Computer Engineering, Hongik University, Seoul, 04066, Republic of Korea.
Department of Neurology, Bundang Jesaeng Hospital, Seongnam, 13590, Korea.
Sci Rep. 2024 Mar 12;14(1):5983. doi: 10.1038/s41598-023-50653-9.
Arousal during sleep can result in sleep fragmentation and various physiological effects, impairing cognitive function and raising blood pressure and heart rate. However, the current definition of arousal has limitations in assessing both amplitude and duration, making it challenging to measure sleep fragmentation accurately. Moreover, there is inconsistency among inter-raters in arousal scoring, which renders it susceptible to subjective variability. Therefore, this study aims to identify a highly accurate classifier for each sleep stage by employing optimized feature selection and machine learning models. According to electroencephalography (EEG) signals during the arousal phase, the intensity level was categorized into four levels. For control, the non-arousal cases were used as level 0 and referred as sham arousal, resulting in five arousal intensity levels. Wavelet transform was applied to analyze sleep arousal to extract features from EEG. Based on these features, we classified arousal intensity levels through machine learning algorithms. Due to the different characteristics of EEG in each sleep stage, the classification model was optimized for the four sleep stages. Excluding sham arousals, a total of 13,532 arousal events were used. The lowest intensity in the entire data, level 1, was computed to be 3107, level 2 was 3384, level 3 was 3472, and the highest intensity of level 4 was 3,569. The optimized classification model for each sleep stage achieved an average sensitivity of 82.68%, specificity of 95.68%, and AUROC of 96.30%. The sensitivity of the control, arousal intensity level 0, was 83.07%, a 1.25% increase over the unoptimized model and a 14.22% increase over previous research. This study used machine learning techniques to develop classifiers for each sleep stage, improving the accuracy of arousal intensity classification. The classifiers showed high sensitivity and specificity and revealed the unique characteristics of arousal intensity during different sleep stages. These findings represent a novel approach to arousal research and have implications for developing more accurate predictive models in sleep research.
睡眠时的觉醒会导致睡眠碎片化和各种生理效应，损害认知功能并导致血压和心率升高。然而，目前的觉醒定义在评估幅度和持续时间方面存在局限性，使得准确测量睡眠碎片化变得具有挑战性。此外，觉醒评分在不同的评分者之间存在不一致性，这使得它容易受到主观变异性的影响。因此，本研究旨在通过采用优化的特征选择和机器学习模型，为每个睡眠阶段识别一个高度准确的分类器。根据觉醒阶段的脑电图 (EEG) 信号，强度水平被分为四个级别。作为对照，非觉醒病例用作级别 0，并称为假觉醒，从而产生五个觉醒强度级别。采用小波变换分析睡眠觉醒以从 EEG 中提取特征。基于这些特征，我们通过机器学习算法对觉醒强度级别进行分类。由于每个睡眠阶段的 EEG 具有不同的特征，因此针对四个睡眠阶段优化了分类模型。不包括假觉醒，总共使用了 13532 个觉醒事件。整个数据中最低强度级别 1 计算为 3107，级别 2 为 3384，级别 3 为 3472，最高强度级别 4 为 3569。针对每个睡眠阶段优化的分类模型平均灵敏度为 82.68%，特异性为 95.68%，AUROC 为 96.30%。对照（觉醒强度级别 0）的灵敏度为 83.07%，比未优化模型提高了 1.25%，比以前的研究提高了 14.22%。本研究使用机器学习技术为每个睡眠阶段开发分类器，提高了觉醒强度分类的准确性。分类器表现出高灵敏度和特异性，并揭示了不同睡眠阶段觉醒强度的独特特征。这些发现代表了一种新的觉醒研究方法，对睡眠研究中开发更准确的预测模型具有重要意义。
