The objective of this paper is to develop a non- invasive robust indicator to the inspiratory effort of a patient under mechanical ventilation. This indicator leads to the inspiratory effort detection as well as to the estimation of its level more reliably and earlier than the classical systems based on flow signal thresholding. Hence, the present work analyses the capability of inspiratory effort level estimation by the observation of the synchronization of the Alae Nasi and the diaphragmatic muscles activations. First, an experimental protocol is suggested to simulate the patient-ventilator coupling. Then, the evolution of muscular activation timing -versus the inspiratory effort level- is studied on acquired EMG and flow signals. Finally, a multidimensional clustering approach is applied in order to separate timing features into classes indicating the different effort levels.
2nd Mosharaka International Conference on Smart Systems and Technologies (MIC-Smart 2021)
Congress
2021 Global Congress on Electrical Engineering (GC-ElecEng 2021), 10-12 December 2021, Valencia, Spain
Pages
--1
Topics
Smart Healthcare Healthcare Automation
ISSN
2227-331X
DOI
BibTeX
@inproceedings{1181ElecEng2021,
title={K-means clustering of Alae Nasi and Diaphragmatic muscles activation timing as an indicator to inspiratory effort level: A proof of concept.},
author={Enas Abdulhay, and Pierre-Yves Gumery, and Elise Aitocine},
booktitle={2021 Global Congress on Electrical Engineering (GC-ElecEng 2021)},
year={2021},
pages={--1},
doi={}},
organization={Mosharaka for Research and Studies}
}
