Lung Center, Cantonal Hospital St. Gallen, Rorschacherstrasse 95, 9007 St. Gallen, Switzerland.
Faculty of Medicine, University of Basel, Petersplatz 1, 4001 Basel, Switzerland.
Sensors (Basel). 2024 Sep 26;24(19):6229. doi: 10.3390/s24196229.
Sleep apnea (SA) is a prevalent disorder characterized by recurrent events of nocturnal apnea. Polysomnography (PSG) represents the gold standard for SA diagnosis. This laboratory-based procedure is complex and costly, and less cumbersome wearable devices have been proposed for SA detection and monitoring. A novel textile multi-sensor monitoring belt recording electrocardiogram (ECG) and breathing frequency (BF) measured by thorax excursion was developed and tested in a sleep laboratory for validation purposes. The aim of the current study was to evaluate the diagnostic performance of ECG-derived heart rate variability and BF-derived breathing rate variability and their combination for the detection of sleep apnea in a population of patients with a suspicion of SA. Fifty-one patients with a suspicion of SA were recruited in the sleep laboratory of the Cantonal Hospital St. Gallen. Patients were equipped with the monitoring belt and underwent a single overnight laboratory-based PSG. In addition, some patients further tested the monitoring belt at home. The ECG and BF signals from the belt were compared to PSG signals using the Bland-Altman methodology. Heart rate and breathing rate variability analyses were performed. Features derived from these analyses were used to build a support vector machine (SVM) classifier for the prediction of SA severity. Model performance was assessed using receiver operating characteristics (ROC) curves. Patients included 35 males and 16 females with a median age of 49 years (range: 21 to 65) and a median apnea-hypopnea index (AHI) of 33 (IQR: 16 to 58). Belt-derived data provided ECG and BF signals with a low bias and in good agreement with PSG-derived signals. The combined ECG and BF signals improved the classification accuracy for SA (area under the ROC curve: 0.98; sensitivity and specificity greater than 90%) compared to single parameter classification based on either ECG or BF alone. This novel wearable device combining ECG and BF provided accurate signals in good agreement with the gold standard PSG. Due to its unobtrusive nature, it is potentially interesting for multi-night assessments and home-based patient follow-up.
睡眠呼吸暂停(SA)是一种普遍存在的疾病,其特征是夜间反复出现呼吸暂停事件。多导睡眠图(PSG)是 SA 诊断的金标准。这种基于实验室的程序复杂且昂贵,因此已经提出了更繁琐的可穿戴设备来检测和监测 SA。我们开发并在睡眠实验室中对一种新型纺织多传感器监测带进行了测试,该监测带记录心电图(ECG)和通过胸廓运动测量的呼吸频率(BF)。本研究旨在评估 ECG 衍生的心率变异性和 BF 衍生的呼吸率变异性及其组合在疑似 SA 患者人群中检测睡眠呼吸暂停的诊断性能。在圣加仑州立医院的睡眠实验室中招募了 51 名疑似 SA 的患者。患者佩戴监测带并接受单次夜间实验室 PSG 检查。此外,一些患者在家中进一步测试了监测带。使用 Bland-Altman 方法将带式的 ECG 和 BF 信号与 PSG 信号进行比较。对心率和呼吸率变异性进行分析。使用这些分析得出的特征来构建支持向量机(SVM)分类器,以预测 SA 严重程度。使用接收者操作特征(ROC)曲线评估模型性能。患者包括 35 名男性和 16 名女性,中位年龄为 49 岁(范围:21 至 65 岁),中位呼吸暂停低通气指数(AHI)为 33(IQR:16 至 58)。带式衍生数据提供了具有低偏差的 ECG 和 BF 信号,并且与 PSG 衍生信号具有良好的一致性。与基于 ECG 或 BF 单一参数的分类相比,组合 ECG 和 BF 信号可提高 SA 的分类准确性(ROC 曲线下面积:0.98;灵敏度和特异性均大于 90%)。这种新型可穿戴设备结合了 ECG 和 BF,可提供与金标准 PSG 一致的准确信号。由于其非侵入性,它对于多夜评估和家庭患者随访具有潜在的意义。