Hayano Junichiro, Adachi Mine, Murakami Yutaka, Sasaki Fumihiko, Yuda Emi
Department of Research and Development, Heart Beat Science Lab Inc., Nagoya, Japan.
Takaoka Clinic, Nagoya, Japan.
Sleep Breath. 2025 Feb 1;29(1):91. doi: 10.1007/s11325-025-03255-w.
Despite increased awareness of sleep hygiene, over 80% of sleep apnea cases remain undiagnosed, underscoring the need for accessible screening methods. This study presents a method for detecting sleep apnea using data from the Apple Watch's inertial measurement unit (IMU).
An algorithm was developed to extract seismocardiographic and respiratory signals from IMU data, analyzing features such as breathing and heart rate variability, respiratory dips, and body movements. In a cohort of 61 adults undergoing polysomnography, we analyzed 52,337 30-second epochs, with 12,373 (23.6%) identified as apnea/hypopnea episodes. Machine learning models using five classifiers (Logistic Regression, Random Forest, Gradient Boosting, k-Nearest Neighbors, and Multi-layer Perceptron) were trained on data from 41 subjects and validated on 20 subjects.
The Random Forest classifier performed best in per-epoch respiratory event detection, achieving an AUC of 0.827 and an F1 score of 0.572 in the training group, and an AUC of 0.831 and an F1 score of 0.602 in the test group. The model's per-subject predictions strongly correlated with the apnea-hypopnea index (AHI) from polysomnography (r = 0.93) and identified subjects with AHI ≥ 15 with 100% sensitivity and 90% specificity.
Utilizing the widespread availability of the Apple Watch and the low power requirements of the IMU, this approach has the potential to significantly improve sleep apnea screening accessibility.
尽管人们对睡眠卫生的认识有所提高，但仍有超过80%的睡眠呼吸暂停病例未被诊断出来，这凸显了对可及的筛查方法的需求。本研究提出了一种利用苹果手表惯性测量单元（IMU）数据检测睡眠呼吸暂停的方法。
开发了一种算法，从IMU数据中提取心震图和呼吸信号，分析呼吸和心率变异性、呼吸波谷和身体运动等特征。在一组61名接受多导睡眠图检查的成年人中，我们分析了52337个30秒的时段，其中12373个（23.6%）被确定为呼吸暂停/低通气事件。使用五个分类器（逻辑回归、随机森林、梯度提升、k近邻和多层感知器）的机器学习模型在41名受试者的数据上进行训练，并在20名受试者上进行验证。
随机森林分类器在每个时段的呼吸事件检测中表现最佳，在训练组中AUC为0.827，F1分数为0.572，在测试组中AUC为0.831，F1分数为0.602。该模型对每个受试者的预测与多导睡眠图的呼吸暂停低通气指数（AHI）高度相关（r = 0.93），并以100%的灵敏度和90%的特异性识别出AHI≥15的受试者。
利用苹果手表的广泛可用性和IMU的低功耗要求，这种方法有可能显著提高睡眠呼吸暂停筛查的可及性。
