Grońska Gabriela, Peri Elisabetta, Long Xi, Overeem Sebastiaan, van Dijk Johannes, Mischi Massimo
Department of Electrical Engineering, Eindhoven University of Technology, De Groene Loper 19, 5612AP Eindhoven, The Netherlands.
Center for Sleep Medicine, Kempenhaeghe, Sterkselseweg 65, 5591VE Heeze, The Netherlands.
Sensors (Basel). 2025 Sep 3;25(17):5463. doi: 10.3390/s25175463.
Respiratory effort is a critical parameter for assessing respiratory function in various pathological conditions such as obstructive sleep apnea (OSA), as well as in patients undergoing respiratory ventilation. Currently, the gold-standard method for measuring it is esophageal pressure (Pes), which is obtrusive and uncomfortable for patients. An alternative approach is using diaphragmatic electromyography (dEMG), a non-obtrusive method that directly reflects the electrical drive triggering respiratory effort, holding potential for quantifying effort. Despite progress in this area, there is still no clear agreement on the best features for assessing respiratory effort from dEMG. This feasibility study considers several time, frequency, and statistical domain features, providing a comparative analysis to determine their performance in estimating respiratory effort. In particular, we evaluate the correlation of the different features with Pes using overnight recordings from 10 OSA patients and assess their robustness across different signal quality levels with the Kruskal-Wallis test. Our results support that time-domain dEMG features such as the filtered envelope, root mean square, and waveform length (WL) exhibit moderately strong correlations (R > 0.6) with respiratory effort. In terms of robustness to noise, the best features were WL, the area under the curve, and the slope sign change, demonstrating moderately strong to fair correlations (R > 0.5) even in low- to very low-quality signals. In contrast, features like skewness, the mean frequency, and the median frequency performed poorly (R < 0.3), regardless of signal quality, likely because they focus on overall signal characteristics rather than the dynamic and transient changes associated with respiratory effort by temporal features. These findings highlight the importance of selecting optimal features to obtain a reliable estimation of respiratory effort, providing a foundation for future research on non-intrusive methods.
呼吸努力是评估各种病理状况(如阻塞性睡眠呼吸暂停(OSA))以及接受呼吸通气患者呼吸功能的关键参数。目前,测量呼吸努力的金标准方法是食管压力(Pes),但该方法对患者具有侵入性且会带来不适。另一种方法是使用膈肌肌电图(dEMG),这是一种非侵入性方法,可直接反映触发呼吸努力的电驱动,具有量化呼吸努力的潜力。尽管该领域取得了进展,但对于从dEMG评估呼吸努力的最佳特征仍未达成明确共识。这项可行性研究考虑了多个时间、频率和统计域特征,进行了对比分析以确定它们在估计呼吸努力方面的性能。特别是,我们使用10名OSA患者的夜间记录评估不同特征与Pes的相关性,并通过Kruskal-Wallis检验评估它们在不同信号质量水平下的稳健性。我们的结果表明,诸如滤波包络、均方根和波形长度(WL)等时域dEMG特征与呼吸努力呈现出中等强度的相关性(R>0.6)。在抗噪声稳健性方面,最佳特征是WL、曲线下面积和斜率符号变化,即使在低至极低质量信号中也呈现出中等强度到中等偏弱的相关性(R>0.5)。相比之下,诸如偏度、平均频率和中位数频率等特征表现较差(R<0.3),无论信号质量如何,可能是因为它们关注的是整体信号特征,而非通过时间特征与呼吸努力相关的动态和瞬态变化。这些发现凸显了选择最佳特征以获得可靠的呼吸努力估计的重要性,为未来非侵入性方法的研究奠定了基础。