Literature DB >> 28237936

Onset and Offset Estimation of the Neural Inspiratory Time in Surface Diaphragm Electromyography: A Pilot Study in Healthy Subjects.

Luis Estrada, Abel Torres, Leonardo Sarlabous, Raimon Jane.   

Abstract

This study evaluates the onset and offset of neural inspiratory time estimated from surface diaphragm electromyographic (EMGdi) recordings. EMGdi and airflow signals were recorded in ten healthy subjects according to two respiratory protocols based on respiratory rate (RR) increments, from 15 to 40 breaths per minute (bpm), and fractional inspiratory time (Ti/Ttot) decrements, from 0.54 to 0.18. The analysis of EMGdi signal amplitude is an alternative approach for the quantification of neural respiratory drive. The EMGdi amplitude was estimated using the fixed sample entropy computed over a 250 ms moving window of the EMGdi signal (EMGdifse). The neural onset was detected through a dynamic threshold over the EMGdifse using the kernel density estimation method, while neural offset was detected by finding when the EMGdifse had decreased to 70% of the peak value reached during inspiration. The Bland-Altman analysis between airflow and neural onsets showed a global bias of 46 ms in the RR protocol and 22 ms in the Ti /Ttot protocol. The Bland-Altman analysis between airflow and neural offsets reveals a global bias of 11 ms in the RR protocol and -2 ms in the Ti/T tot protocol. The relationship between pairs of RR values (Pearson's correlation coefficient of 0.99, Bland-=Altman limits of -2.39 to 2.41 bpm, and mean bias of 0.01 bpm) and between pairs of Ti/Ttot values (Pearson's correlation coefficient of 0.86, Bland-Altman limits of -0.11 to 0.10, and mean bias of -0.01) showed a good agreement. In conclusion, we propose a method for determining neural onset and neural offset based on noninvasive recordings of the electrical activity of the diaphragm that requires no filtering of cardiac muscle interference.

Mesh:

Year:  2017        PMID: 28237936     DOI: 10.1109/JBHI.2017.2672800

Source DB:  PubMed          Journal:  IEEE J Biomed Health Inform        ISSN: 2168-2194            Impact factor:   5.772


  3 in total

1.  Smart Vest for Respiratory Rate Monitoring of COPD Patients Based on Non-Contact Capacitive Sensing.

Authors:  David Naranjo-Hernández; Alejandro Talaminos-Barroso; Javier Reina-Tosina; Laura M Roa; Gerardo Barbarov-Rostan; Pilar Cejudo-Ramos; Eduardo Márquez-Martín; Francisco Ortega-Ruiz
Journal:  Sensors (Basel)       Date:  2018-07-03       Impact factor: 3.576

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

Authors:  Leonardo Sarlabous; Luis Estrada; Ana Cerezo-Hernández; Sietske V D Leest; Abel Torres; Raimon Jané; Marieke Duiverman; Ainara Garde
Journal:  Entropy (Basel)       Date:  2019-03-07       Impact factor: 2.524

3.  Performance Evaluation of Fixed Sample Entropy in Myographic Signals for Inspiratory Muscle Activity Estimation.

Authors:  Manuel Lozano-García; Luis Estrada; Raimon Jané
Journal:  Entropy (Basel)       Date:  2019-02-15       Impact factor: 2.524

  3 in total

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