Manli Wang, Junwen Guan, Tong Sun, Junjie Wang, Yikai Yuan, Yicheng Zhou, Yi Zhang, Xiaoyu Yang, Xuepei Li, Jingguo Yang, Xuebin Zhou, Hang Yu
Clinical Research Department, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
Department of Neurosurgery, West China Hospital, Sichuan University, China.
Comput Biol Med. 2025 Feb;185:109515. doi: 10.1016/j.compbiomed.2024.109515. Epub 2024 Dec 10.
Sleep disorders have become a significant health concern in modern society. To investigate and diagnose sleep disorders, sleep analysis has emerged as the primary research method. Conventional polysomnography primarily relies on cerebral electroencephalography (EEG) and electromyography (EMG) for sleep stage scoring, but manual scoring is time-consuming and subjective. This study investigated the potential application of cerebellar EEG combined with machine learning in automatic sleep stage classification. Twenty-five male mice underwent 24-h cerebral EEG/cerebellar EEG/EMG recording, and manual sleep staging was performed. Various machine learning models, including Light Gradient Boosting (LGBoost), Extreme Gradient Boosting, Categorical Boosting, Support Vector Machine, Logistic Regression, Random Forest, Long Short-Term Memory and Convolutional Neural Network, were applied for automatic sleep stage classification. The performance of different models and the efficacy of cerebellar EEG, cerebral EEG, and EMG were compared under different training:test set ratios. Cerebellar EEG exhibited significant differences in power spectral density across wakefulness, non-rapid eye movement sleep stages, and rapid eye movement sleep stages, particularly at frequencies >7 Hz. LGBoost, Extreme Gradient Boosting, and Categorical Boosting models showed comparable performance, with LGBoost being selected for further analyses due to its shorter computation time. Cerebral EEG consistently demonstrated the highest precision, recall/sensitivity, and specificity in classifying sleep stages across all training:test set ratios, followed by cerebellar EEG, which outperformed EMG. Combining the top 5 cerebellar EEG features with cerebral EEG features yielded better classification performance than combining EMG features with cerebral EEG features. Using the top 20 features, the model achieved mean area under the receiver operating characteristic curve values of 0.98 ± 0.08, 0.98 ± 0.10, and 0.99 ± 0.07 for wakefulness, non-rapid eye movement sleep stages, and rapid eye movement sleep stages, respectively. The cerebellum may play a unique and important role in sleep-wake regulation. Incorporating cerebellar EEG into polysomnography has the potential to enhance the accuracy and efficiency of sleep stage classification.
睡眠障碍已成为现代社会中一个重大的健康问题。为了研究和诊断睡眠障碍，睡眠分析已成为主要的研究方法。传统的多导睡眠图主要依靠脑电图（EEG）和肌电图（EMG）进行睡眠阶段评分，但人工评分既耗时又主观。本研究探讨了小脑脑电图结合机器学习在自动睡眠阶段分类中的潜在应用。对25只雄性小鼠进行了24小时的脑电/小脑电/肌电记录，并进行了人工睡眠分期。各种机器学习模型，包括轻梯度提升（LGBoost）、极限梯度提升、分类提升、支持向量机、逻辑回归、随机森林、长短期记忆和卷积神经网络，被用于自动睡眠阶段分类。在不同的训练集与测试集比例下，比较了不同模型的性能以及小脑电、脑电和肌电的效果。小脑电在清醒、非快速眼动睡眠阶段和快速眼动睡眠阶段的功率谱密度上表现出显著差异，特别是在频率>7Hz时。LGBoost、极限梯度提升和分类提升模型表现出可比的性能，由于计算时间较短，选择LGBoost进行进一步分析。在所有训练集与测试集比例下，脑电在睡眠阶段分类中始终表现出最高的精度、召回率/敏感性和特异性，其次是小脑电，其表现优于肌电。将前5个小脑电特征与脑电特征相结合，比将肌电特征与脑电特征相结合产生了更好的分类性能。使用前20个特征，该模型在清醒、非快速眼动睡眠阶段和快速眼动睡眠阶段的受试者工作特征曲线下面积均值分别为0.98±0.08、0.98±0.10和0.99±0.07。小脑可能在睡眠-觉醒调节中发挥独特而重要的作用。将小脑电纳入多导睡眠图有可能提高睡眠阶段分类的准确性和效率。
