Feasibility and Interest of Continuous Diaphragmatic Fatigue Monitoring Using Wavelet Denoising in ICU and Anesthesia

Abstract

Measures of diaphragmatic electromyography (Edi), and respiratory mechanics, have demonstrated early changes before clinical complications. However, automatic Edi data collection is not adequate today due mainly to severe artefacts as well as to loss of signal. We thus intended to develop a new device with embedded artificial intelligence to optimize automatic Edi recordings independantly of artefacts and of probe displacement.

We first chose the best mathematical tool to denoise Edi, using an established database, giving multiresolution wavelets as the best, resulting in the permanent availability of the H/L spectral index, a recognized representative of diaphragmatic fatigue. Fatigue was simultaneously measured using the classical mechanical f/Vt index (Rapid Shallow Breathing Index, RSBI), as well as the transdiaphragmatic pressure.

We then performed a comparison of real-time H/L and RSBI in a group of seven healthy volunteers, before and during midazolam sedation infusion 0.1 mg.kg^-1, with a parallel CPAP administration (2.5, 5.0, and 10 cm H₂O) intended to compensate for airways resistance due to midazolam. Procedure was ended by delivering the antagonistic flumazenil 0.2 to 0.5 mg.kg^-1. Progressive fatigue due to midazolam, the relief due to CPAP, as well as the answer to the anatgonist flumazenil, were shown earlier by the H/L index than by the RSBI change.

Our new H/L monitoring device may greatly improve clinical follow-up of anesthetized patients as well as help to determine the optimal period for ventilatory weaning in ICU (Clinical Trials NCT00133939).