Setiawan Febryan, Lin Cheng-Yu, Lin Che-Wei
Department of Biomedical Engineering, College of Engineering, National Cheng Kung University, Tainan, Taiwan.
Department of Otolaryngology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Sleep. 2025 Aug 8. doi: 10.1093/sleep/zsaf226.
Atrial fibrillation (AF) and obstructive sleep apnea (OSA) are interrelated conditions that substantially increase the risk of cardiovascular complications. However, concurrent detection of these conditions remains a critical unmet need in clinical practice. Current home sleep apnea test (HSAT) devices often fail to detect arrhythmias essential for diagnosing OSA-associated AF due to limited ECG monitoring capabilities, and their integration with continuous positive airway pressure (CPAP) data for treatment optimization remains underutilized.
This study introduces SHHDeepNet, an advanced deep learning-based framework designed for the detection of OSA in patients with AF, leveraging enhanced features extracted from single-lead electrocardiogram (ECG) signals. The ECG signals were preprocessed and refined using reconstruction independent component analysis (RICA), which isolates statistically independent features for improved data representation. These features were subsequently classified using the customized SHHDeepNet architecture. SHHDeepNet utilizes advanced signal processing and deep learning techniques to enhance ECG-based detection of AF-associated OSA.
The framework was validated using overnight ECG recordings from 101 subjects derived from the Sleep Heart Health Study Visit 1 (SHHS1) database, encompassing 36 prevalent AF (PAF) cases, 25 incident AF (IAF) cases, and 40 OSA cases. Detection performance was evaluated through binary classification (AF AH vs. AF non-AH) and multi-class classification (AF AH, AF non-AH, non-AF AH, and non-AF non-AH). During 5-fold cross-validation (5fold-CV), the framework achieved a binary classification accuracy of 98.22%, sensitivity of 96.8%, specificity of 99%, and an area under the curve (AUC) of 0.9981. For multi-class classification, 5fold-CV yielded 98.36% accuracy, 97.14% sensitivity, 98.77% specificity, and an AUC of 0.9975. Validation using leave-one-subject-out cross-validation (LOSO-CV) achieved a binary classification accuracy of 86.42%, sensitivity of 79.4%, specificity of 90.2%, and an AUC of 0.9372. For multi-class classification under LOSO-CV, the average accuracy, sensitivity, and F1-score were 86.7%, 72.6%, and 0.7224, respectively. External validation was performed on a cohort of 123 subjects from the Osteoporotic Fractures in Men (MrOS) database, comprising 68 cases of PAF and 55 cases of OSA. The proposed method achieved a multi-class classification accuracy of 88.51%, sensitivity of 73.50%, specificity of 91.34%, and an AUC of 0.9363.
These findings underscore the significance of simultaneous detection of AF and OSA, providing a more comprehensive evaluation of cardiovascular health. The proposed SHHDeepNet framework offers a promising tool to support clinical decision-making, enhance management strategies, and improve patient outcomes by mitigating the risks associated with these conditions.
心房颤动（AF）和阻塞性睡眠呼吸暂停（OSA）是相互关联的病症，会大幅增加心血管并发症的风险。然而，在临床实践中，同时检测这两种病症仍然是一项关键的未满足需求。由于心电图监测能力有限，当前的家庭睡眠呼吸暂停测试（HSAT）设备常常无法检测到诊断OSA相关AF所必需的心律失常，而且它们与持续气道正压通气（CPAP）数据结合以优化治疗的应用仍未得到充分利用。
本研究引入了SHHDeepNet，这是一个基于深度学习的先进框架，旨在利用从单导联心电图（ECG）信号中提取的增强特征来检测AF患者的OSA。ECG信号使用重建独立成分分析（RICA）进行预处理和优化，该方法分离出统计独立的特征以改善数据表示。随后使用定制的SHHDeepNet架构对这些特征进行分类。SHHDeepNet利用先进的信号处理和深度学习技术来增强基于ECG的AF相关OSA检测。
该框架使用来自睡眠心脏健康研究首次访问（SHHS1）数据库的101名受试者的夜间ECG记录进行了验证，其中包括36例持续性AF（PAF）病例、25例新发AF（IAF）病例和40例OSA病例。通过二元分类（AF AH与AF非AH）和多类分类（AF AH、AF非AH、非AF AH和非AF非AH）评估检测性能。在5折交叉验证（5折-CV）期间，该框架实现了二元分类准确率为98.22%，灵敏度为96.8%，特异性为99%，曲线下面积（AUC）为0.9981。对于多类分类，5折-CV的准确率为98.36%，灵敏度为97.14%，特异性为98.77%，AUC为0.9975。使用留一受试者交叉验证（LOSO-CV）进行验证时，二元分类准确率为86.42%，灵敏度为79.4%，特异性为90.2%，AUC为0.9372。对于LOSO-CV下的多类分类，平均准确率、灵敏度和F1分数分别为86.7%、72.6%和0.7224。对来自男性骨质疏松性骨折（MrOS）数据库的123名受试者进行了外部验证，其中包括68例PAF病例和55例OSA病例。所提出的方法实现了多类分类准确率为88.51%，灵敏度为73.50%，特异性为91.34%，AUC为0.9363。
这些发现强调了同时检测AF和OSA的重要性，为心血管健康提供了更全面的评估。所提出的SHHDeepNet框架提供了一个有前景的工具，通过降低与这些病症相关的风险来支持临床决策、加强管理策略并改善患者预后。
