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by Keyword: Chronic obstructive pulmonary disease


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Sarlabous, L., Estrada, L., Cerezo-Hernández, A., Leest, Sietske V. D., Torres, A., Jané, R., Duiverman, M., Garde, Ainara, (2019). Electromyography-based respiratory onset detection in COPD patients on non-invasive mechanical ventilation Entropy 21, (3), 258

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.

Keywords: Fixed sample entropy, Adaptive filtering, Root mean square, Diaphragm electromyography, Non-invasive mechanical ventilation, Chronic obstructive pulmonary disease


Correa, L.S., Giraldo, B., Correa, R., Arini, P.D., Laciar, E., (2014). Estudio de la pausa espiratoria en pacientes con enfermedades obstructivas en proceso de desconexión de la ventilación mecánica IFMBE Proceedings VI Latin American Congress on Biomedical Engineering (CLAIB 2014) , Springer (Paraná, Argentina) 49, 705-708

In this work, the flow signal Expiratory Pause (EP) temporal analysis is used in 18 patients with obstructive lung diseases going through spontaneous breathing trial at weaning process. The main objective was to identify the patients who were successfully disconnected (success group: 9 patients), and those who were not (failure and reintubated group: 9 patients). A variable selection stage was done by mean group comparison and step wise variable inclusion, leading to a 3 parameters set: EP time median; cycle time mean; and median absolute deviation of the EP maxima local number. Next, this set was used in a classifier based on linear discriminant analysis, which results in 17 patients (94.4%) correctly classified, with 88.9% of specificity (Sp) and 100% of sensitivity (Se). Finally, applying the leave-one-out cross validation method, results were 88.9% of correctly classified patients (Sp=77.8% and Se=100%). In conclusion, the proposed parameters showed a good performance and could be used to help therapists to wean patients with obstructive diseases.

Keywords: Chronic Obstructive Pulmonary Disease (COPD), Weaning, Mechanical ventilation, Expiratory pause