International Ph.D. Program of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
Family Medicine Training Center, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam.
J Sleep Res. 2024 May;33(3):e13991. doi: 10.1111/jsr.13991. Epub 2023 Jul 4.
Obstructive sleep apnea (OSA) has a heavy health-related burden on patients and the healthcare system. Continuous positive airway pressure (CPAP) is effective in treating OSA, but adherence to it is often inadequate. A promising solution is to detect sleep apnea events in advance, and to adjust the pressure accordingly, which could improve the long-term use of CPAP treatment. The use of CPAP titration data may reflect a similar response of patients to therapy at home. Our study aimed to develop a machine-learning algorithm using retrospective electrocardiogram (ECG) data and CPAP titration to forecast sleep apnea events before they happen. We employed a support vector machine (SVM), k-nearest neighbour (KNN), decision tree (DT), and linear discriminative analysis (LDA) to detect sleep apnea events 30-90 s in advance. Preprocessed 30 s segments were time-frequency transformed to spectrograms using continuous wavelet transform, followed by feature generation using the bag-of-features technique. Specific frequency bands of 0.5-50 Hz, 0.8-10 Hz, and 8-50 Hz were also extracted to detect the most detected band. Our results indicated that SVM outperformed KNN, LDA, and DT across frequency bands and leading time segments. The 8-50 Hz frequency band gave the best accuracy of 98.2%, and a F1-score of 0.93. Segments 60 s before sleep events seemed to exhibit better performance than other pre-OSA segments. Our findings demonstrate the feasibility of detecting sleep apnea events in advance using only a single-lead ECG signal at CPAP titration, making our proposed framework a novel and promising approach to managing obstructive sleep apnea at home.
阻塞性睡眠呼吸暂停（OSA）给患者和医疗系统带来了沉重的健康负担。持续气道正压通气（CPAP）在治疗 OSA 方面非常有效，但患者的依从性往往不足。一个有前途的解决方案是提前检测睡眠呼吸暂停事件，并相应调整压力，这可以提高 CPAP 治疗的长期使用效果。使用 CPAP 滴定数据可以反映患者在家中对治疗的类似反应。我们的研究旨在使用回顾性心电图（ECG）数据和 CPAP 滴定数据开发一种机器学习算法，以便在睡眠呼吸暂停事件发生之前进行预测。我们采用支持向量机（SVM）、k-最近邻（KNN）、决策树（DT）和线性判别分析（LDA）来提前 30-90s 检测睡眠呼吸暂停事件。预处理的 30s 段使用连续小波变换转换为时频变换的频谱图，然后使用特征生成袋式特征技术。还提取了 0.5-50Hz、0.8-10Hz 和 8-50Hz 的特定频带，以检测最常检测到的频带。我们的结果表明，SVM 在各个频带和领先时间段均优于 KNN、LDA 和 DT。8-50Hz 频带的准确率最高为 98.2%，F1 得分为 0.93。在睡眠事件发生前 60s 的段似乎比其他预 OSA 段表现更好。我们的研究结果表明，仅使用 CPAP 滴定时的单导联 ECG 信号就可以提前检测睡眠呼吸暂停事件，这使得我们提出的框架成为一种新颖且有前途的在家中管理阻塞性睡眠呼吸暂停的方法。
