Electromyography-Based Respiratory Onset Detection in COPD Patients on Non-Invasive Mechanical Ventilation

Abstract

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.

Development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation

“…SE has proved to be an effective tool for investigating different types of time series data derived from various biological conditions in the human body. Examples of these conditions include the activation of inspiratory muscles in COPD patients 24 , 25 , the analysis of atrial fibrillation on electrocardiograms 26 , background electroencephalograms in Alzheimer’s patients 27 , heart rate variability 28 , 29 , human postural sway 29 and seizure termination during electroconvulsive therapy 30 .…”

Does the Efficacy of High Intensity Ventilation in Stable COPD Depend on the Ventilator Model? A Bench-to-Bedside Study

“…The parasternal location is the most reliable and best-known approach for surface respiratory muscle EMG assessment. EMG recordings in the second anterior parasternal intercostal space have been used as a reference for the categorization of asynchronies in NIV, 19 to identify the true start of inspiratory effort in patients with COPD, 20 and as a predictive tool for readmissions in COPD patients. 21 In a model similar to the one in the present study, Duiverman et al 22 demonstrated a decrease in the EMG signal with increasing levels of pressure support, reflecting inspiratory muscle unloading.…”

Evaluation of Respiratory Muscle Activity by Means of Concentric Ring Electrodes

“…First, the power spectral density (PSD) of each segment of all the sEMG signals was estimated using the modified periodogram method with a Hamming window and a discrete Fourier transform with 4096 points [24], [31]. Then, a weighted average PSD of all the single PSDs corresponding to segments of the same type was calculated and the spectral power (P) and the median frequency (MDF) of each type of segment were computed.…”