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by Keyword: Chronic obstructive pulmonary disease (copd)

Torres, A, Estrada-Petrocelli, L, Raveling, T, Duiverman, ML, (2026). Automatic Detection of Onset and Offset of Respiratory Electromyographic Activity in Severe COPD Patients on Non-Invasive Mechanical Ventilation IEEE Journal of Translational Engineering in Health and Medicine 14, 55-66

Objective: Accurate detection of inspiratory onset and offset in the diaphragm electromyographic signal (EMGdi) is clinically relevant to assess patient-ventilator interaction in COPD patients undergoing non-invasive ventilation (NIV). Manual annotations are time-consuming and subject to inter-observer variability, highlighting the need for reliable automatic methods. Method: We developed a fully automatic algorithm to detect EMGdi activity cycles and their onset/offset timing in overnight NIV recordings. Four ECG suppression approaches were combined with root mean square (RMS) and fixed sample entropy (fSE) envelopes, and a novel bias correction strategy based on inspiratory-to-basal signal-to-noise ratio (I2BSNR) was introduced. Performance was compared with double-blind annotations from two independent experts. Results: In a cohort of 10 severe COPD patients (9212 annotated cycles), the best configuration (adaptive filtering with fSE exponential envelope) achieved F $1=0.96$ , with onset bias -28 ms (SD 270 ms) and offset bias + 120 ms (SD 292 ms). We show that fSE-based envelopes consistently outperform RMS in onset/offset detection, and that I2BSNR-based correction reduces systematic bias to within accepted clinical timing windows. Conclusions: The proposed method provides accurate and robust onset/offset detection of EMGdi during NIV in COPD patients. This enables reliable quantification of patient-ventilator asynchronies such as ineffective efforts and delayed cycling, offering direct clinical value for optimizing nightly ventilator settings in severe COPD. Clinical and Impact: Reliable detection of patient inspiratory activity offers a practical tool to guide real-time ventilator adjustments and reduce patient-ventilator asynchronies

JTD Keywords: Annotations, Asynchrony, Chronic obstructive pulmonary disease, Chronic obstructive pulmonary disease (copd), Electromyography, Emg, Filtering, Fixed sample entropy (fse)., Non-invasive ventilation (niv), Patient-ventilator asynchrony (pva), Recording, Reliability, Root mean square, Surface diaphragm electromyography (emgdi), Time, Timing, Ventilation, Ventilators


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.

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