Xie Jiali, Fonseca Pedro, van Dijk Johannes P, Long Xi, Overeem Sebastiaan
Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands.
Philips Research, High Tech Campus, 5656 AE Eindhoven, The Netherlands.
Diagnostics (Basel). 2023 Jun 23;13(13):2146. doi: 10.3390/diagnostics13132146.
Sleep apnea is a prevalent sleep-disordered breathing (SDB) condition that affects a large population worldwide. Research has demonstrated the potential of using electrocardiographic (ECG) signals (heart rate and ECG-derived respiration, EDR) to detect SDB. However, EDR may be a suboptimal replacement for respiration signals.
We evaluated a previously described ECG-based deep learning algorithm in an independent dataset including 198 patients and compared performance for SDB event detection using thoracic respiratory effort versus EDR. We also evaluated the algorithm in terms of apnea-hypopnea index (AHI) estimation performance, and SDB severity classification based on the estimated AHI.
Using respiratory effort instead of EDR, we achieved an improved performance in SDB event detection (F1 score = 0.708), AHI estimation (Spearman's correlation = 0.922), and SDB severity classification (Cohen's kappa of 0.62 was obtained based on AHI).
Respiratory effort is superior to EDR to assess SDB. Using respiratory effort and ECG, the previously described algorithm achieves good performance in a new dataset from an independent laboratory confirming its adequacy for this task.
睡眠呼吸暂停是一种普遍存在的睡眠呼吸障碍（SDB）疾病，影响着全球大量人群。研究表明，利用心电图（ECG）信号（心率和基于ECG的呼吸，EDR）检测SDB具有潜力。然而，EDR可能并非呼吸信号的理想替代物。
我们在一个包含198名患者的独立数据集中评估了一种先前描述的基于ECG的深度学习算法，并比较了使用胸壁呼吸努力与EDR进行SDB事件检测的性能。我们还从呼吸暂停低通气指数（AHI）估计性能以及基于估计的AHI进行SDB严重程度分类方面评估了该算法。
使用呼吸努力而非EDR，我们在SDB事件检测（F1分数 = 0.708）、AHI估计（斯皮尔曼相关性 = 0.922）以及SDB严重程度分类（基于AHI获得的科恩kappa系数为0.62）方面取得了更好的性能。
在评估SDB方面，呼吸努力优于EDR。利用呼吸努力和ECG，先前描述的算法在来自独立实验室的新数据集中表现良好，证实了其适用于这项任务。
