Barroso-García Verónica, Fernández-Poyatos Marta, Sahelices Benjamín, Álvarez Daniel, Gozal David, Hornero Roberto, Gutiérrez-Tobal Gonzalo C
Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain.
Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain.
Diagnostics (Basel). 2023 Oct 12;13(20):3187. doi: 10.3390/diagnostics13203187.
The high prevalence of sleep apnea and the limitations of polysomnography have prompted the investigation of strategies aimed at automated diagnosis using a restricted number of physiological measures. This study aimed to demonstrate that thoracic (THO) and abdominal (ABD) movement signals are useful for accurately estimating the severity of sleep apnea, even if central respiratory events are present. Thus, we developed 2D-convolutional neural networks (CNNs) jointly using THO and ABD to automatically estimate sleep apnea severity and evaluate the central event contribution. Our proposal achieved an intraclass correlation coefficient (ICC) = 0.75 and a root mean square error (RMSE) = 10.33 events/h when estimating the apnea-hypopnea index, and ICC = 0.83 and RMSE = 0.95 events/h when estimating the central apnea index. The CNN obtained accuracies of 94.98%, 79.82%, and 81.60% for 5, 15, and 30 events/h when evaluating the complete apnea hypopnea index. The model improved when the nature of the events was central: 98.72% and 99.74% accuracy for 5 and 15 events/h. Hence, the information extracted from these signals using CNNs could be a powerful tool to diagnose sleep apnea, especially in subjects with a high density of central apnea events.
睡眠呼吸暂停的高患病率以及多导睡眠图的局限性促使人们研究旨在使用有限数量生理指标进行自动诊断的策略。本研究旨在证明，即使存在中枢性呼吸事件，胸部（THO）和腹部（ABD）运动信号对于准确估计睡眠呼吸暂停的严重程度也很有用。因此，我们联合使用THO和ABD开发了二维卷积神经网络（CNN），以自动估计睡眠呼吸暂停的严重程度并评估中枢性事件的影响。我们的方案在估计呼吸暂停低通气指数时，组内相关系数（ICC）=0.75，均方根误差（RMSE）=10.33次/小时；在估计中枢性呼吸暂停指数时，ICC=0.83，RMSE=0.95次/小时。当评估完整的呼吸暂停低通气指数时，CNN在每小时5次、15次和30次事件时的准确率分别为94.98%、79.82%和81.60%。当事件性质为中枢性时，模型性能有所提高：每小时5次和15次事件时的准确率分别为98.72%和99.74%。因此，使用CNN从这些信号中提取的信息可能是诊断睡眠呼吸暂停的有力工具，尤其是在中枢性呼吸暂停事件高密度的受试者中。
