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

1
Biomedical Signal Processing and Interpretation, Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology (BIST), 08028 Barcelona, Spain
2
Department of Automatic Control (ESAII), Universitat Politècnica de Catalunya (UPC)—Barcelona Tech, 08028 Barcelona, Spain
3
Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 08028 Barcelona, Spain
4
Department of Pulmonology, Rio Hortega University Hospital, 47012 Valladolid, Spain
5
Department of Pulmonary Diseases/Home mechanical Ventilation, University of Groningen, University Medical Center Groningen, 9713 Groningen, The Netherlands
6
Biomedical Signals and Systems Group, Faculty of Electrical Engineering, Mathematics & Computer Science, University of Twente, 7500 Enschede, The Netherlands
7
Groningen Research Institute of Asthma and COPD (GRIAC), University of Groningen, 9712 Groningen, The Netherlands
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(3), 258; https://doi.org/10.3390/e21030258
Received: 7 January 2019 / Revised: 22 February 2019 / Accepted: 28 February 2019 / Published: 7 March 2019
(This article belongs to the Special Issue The 20th Anniversary of Entropy - Approximate and Sample Entropy)
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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. View Full-Text
Keywords: fixed sample entropy; adaptive filtering; root mean square; diaphragm electromyography; non-invasive mechanical ventilation; chronic obstructive pulmonary disease fixed sample entropy; adaptive filtering; root mean square; diaphragm electromyography; non-invasive mechanical ventilation; chronic obstructive pulmonary disease
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

Sarlabous, L.; Estrada, L.; Cerezo-Hernández, A.; V. D. Leest, S.; Torres, A.; Jané, R.; Duiverman, M.; Garde, A. Electromyography-Based Respiratory Onset Detection in COPD Patients on Non-Invasive Mechanical Ventilation. Entropy 2019, 21, 258.

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