2026-04-17T17:25:37Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/1767532026-01-30T02:07:53Zcom_2072_1033col_2072_452950

00925njm 22002777a 4500 dc Sarlabous Uranga, Leonardo author Estrada Petrocelli, Luis Carlos author Cerezo Hernández, Ana author Leest, Sietske V. D. author Torres Cebrián, Abel author Jané Campos, Raimon author Duiverman, Marieke author Garde Martínez, Ainara author 2019-03-07 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. Peer Reviewed Postprint (published version) Àrees temàtiques de la UPC::Enginyeria biomèdica Electromyography Fixed sample entropy Adaptive filtering Root mean square Diaphragm electromyography Non-invasive mechanical ventilation Chronic obstructive pulmonary disease Electromiografia Electromyography-based respiratory onset detection in COPD patients on non-invasive mechanical ventilation