Sarlabous Leonardo, Estrada Luis, Cerezo-Hernández Ana, V D Leest Sietske, Torres Abel, Jané Raimon, Duiverman Marieke, Garde Ainara
Biomedical Signal Processing and Interpretation, Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology (BIST), 08028 Barcelona, Spain.
Department of Automatic Control (ESAII), Universitat Politècnica de Catalunya (UPC)-Barcelona Tech, 08028 Barcelona, Spain.
Entropy (Basel). 2019 Mar 7;21(3):258. doi: 10.3390/e21030258.
To optimize long-term nocturnal non-invasive ventilation in patients with chronic obstructive pulmonary disease, surface diaphragm electromyography (EMGdi) might be helpful to detect patient-ventilator asynchrony. However, visual analysis is labor-intensive and EMGdi is heavily corrupted by electrocardiographic (ECG) activity. Therefore, we developed an automatic method to detect inspiratory onset from EMGdi envelope using fixed sample entropy (fSE) and a dynamic threshold based on kernel density estimation (KDE). Moreover, we combined fSE with adaptive filtering techniques to reduce ECG interference and improve onset detection. The performance of EMGdi envelopes extracted by applying fSE and fSE with adaptive filtering was compared to the root mean square (RMS)-based envelope provided by the EMG acquisition device. Automatic onset detection accuracy, using these three envelopes, was evaluated through the root mean square error (RMSE) between the automatic and mean visual onsets (made by two observers). The fSE-based method provided lower RMSE, which was reduced from 298 ms to 264 ms when combined with adaptive filtering, compared to 301 ms provided by the RMS-based method. The RMSE was negatively correlated with the proposed EMGdi quality indices. Following further validation, fSE with KDE, combined with adaptive filtering when dealing with low quality EMGdi, indicates promise for detecting the neural onset of respiratory drive.
为优化慢性阻塞性肺疾病患者的长期夜间无创通气，表面膈肌肌电图（EMGdi）可能有助于检测患者-呼吸机不同步。然而，视觉分析劳动强度大，且EMGdi会受到心电图（ECG）活动的严重干扰。因此，我们开发了一种自动方法，利用固定样本熵（fSE）和基于核密度估计（KDE）的动态阈值从EMGdi包络中检测吸气起始。此外，我们将fSE与自适应滤波技术相结合，以减少ECG干扰并改善起始检测。将应用fSE和带自适应滤波的fSE提取的EMGdi包络的性能与EMG采集设备提供的基于均方根（RMS）的包络进行比较。通过自动检测和平均视觉起始（由两名观察者做出）之间的均方根误差（RMSE）评估使用这三种包络的自动起始检测准确性。基于fSE的方法提供了更低的RMSE，与基于RMS的方法提供的301 ms相比，与自适应滤波相结合时，RMSE从298 ms降至264 ms。RMSE与所提出的EMGdi质量指标呈负相关。经过进一步验证，在处理低质量EMGdi时，结合自适应滤波的带KDE的fSE有望检测呼吸驱动的神经起始。
