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

SUBMITTER: Sarlabous L 

PROVIDER: S-EPMC7514739 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature