Zhang Biyong, Peng Zheng, Dong Chunjiao, Hu Jun, Long Xi, Lyu Tan, Lu Peilin
Eindhoven University of Technology, Eindhoven, Netherlands.
Bobo Technology Ltd., Zhejiang, China.
Front Neurosci. 2025 Mar 20;19:1549783. doi: 10.3389/fnins.2025.1549783. eCollection 2025.
Obstructive sleep apnea syndrome (OSAS) degrades sleep quality and is associated with serious health conditions. Instead of the gold-standard polysomnography requiring complex equipment and expertise, a non-obtrusive device such as ballistocardiography (BCG) is more suitable for home-based continuous monitoring of OSAS, which has shown promising results in previous studies. However, often due to the limited storage and computing resource, also preferred by venders, the high computational cost in many existing BCG-based methods would practically limit the deployment for home monitoring.
In this preliminary study, we propose an approach for OSAS monitoring using BCG signals. Applying fast change-point detection to first isolate apnea-suspected episodes would allow for processing only those suspected episodes for further feature extraction and OSAS severity classification. This can reduce both the data to be stored or transmitted and the computational load. Furthermore, our approach directly extracts features from BCG signals without employing a complex algorithm to derive respiratory and heart rate signals as often done in literature, further simplifying the algorithm pipeline. Apnea-hypopnea index (AHI) is then computed based on the detected apnea events (using a random forest classifier) from the identified apnea-suspected episodes. To deal with the expected underestimated AHI due to missing true apnea events during change-point detection, we apply boundary adjustment on AHI when classifying severity.
Cross-validated on 32 subjects, the proposed approach achieved an accuracy of 71.9% for four-class severity classification and 87.5% for binary classification (AHI less than 15 or not).
These findings highlight the potential of our proposed BCG-based approach as an effective and accessible alternative for continuous OSAS monitoring.
阻塞性睡眠呼吸暂停综合征（OSAS）会降低睡眠质量，并与严重的健康状况相关。与需要复杂设备和专业知识的金标准多导睡眠图不同，诸如心冲击图（BCG）之类的非侵入式设备更适合于在家中对OSAS进行连续监测，先前的研究已显示出其有前景的结果。然而，由于存储和计算资源有限（这也是供应商所青睐的），许多现有基于BCG的方法中高昂的计算成本实际上会限制其在家用监测中的部署。
在这项初步研究中，我们提出了一种使用BCG信号进行OSAS监测的方法。应用快速变化点检测首先分离出疑似呼吸暂停的发作，这将允许仅处理那些疑似发作以进行进一步的特征提取和OSAS严重程度分类。这可以减少要存储或传输的数据以及计算负荷。此外，我们的方法直接从BCG信号中提取特征，而无需像文献中通常那样采用复杂算法来推导呼吸和心率信号，从而进一步简化了算法流程。然后根据从识别出的疑似呼吸暂停发作中检测到的呼吸暂停事件（使用随机森林分类器）计算呼吸暂停低通气指数（AHI）。为了处理由于在变化点检测期间遗漏真正的呼吸暂停事件而导致的预期AHI低估问题，我们在对严重程度进行分类时对AHI应用边界调整。
在32名受试者上进行交叉验证，所提出的方法在四类严重程度分类中准确率达到71.9%，在二元分类（AHI小于15或不小于15）中准确率达到87.5%。
这些发现突出了我们所提出的基于BCG的方法作为连续OSAS监测的一种有效且可及的替代方法的潜力。
