Publications

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 diaphragm electromyographic (EMGdi) signal amplitude is an alternative approach for the quantification of neural respiratory drive (NRD). 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/Ttot 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 non-invasive recordings of the electrical activity of the diaphragm that requires no filtering of cardiac muscle interference.

JTD Keywords: Kernel density estimation (KDE),, Surface diaphragm electromyographic,, (EMGdi) signal,, Inspiratory time,, Neural respiratory drive (NRD),, Neural inspiratory time,, Fixed sample entropy (fSampEn)

The gold standard for diagnosing sleep apnoea-hypopnoea syndrome (SAHS) is polysomnography (PSG), an expensive, labour-intensive and time-consuming procedure. Accordingly, it would be very useful to have a screening method to allow early assessment of the severity of a subject, prior to his/her referral for PSG. Several differences have been reported between simple snorers and SAHS patients in the acoustic characteristics of snoring and its variability. In this paper, snores are fully characterised in the time domain, by their sound intensity and pitch, and in the frequency domain, by their formant frequencies and several shape and energy ratio measurements. We show that accurate multiclass classification of snoring subjects, with three levels of SAHS, can be achieved on the basis of acoustic analysis of snoring alone, without any requiring information on the duration or the number of apnoeas. Several classification methods are examined. The best of the approaches assessed is a Bayes model using a kernel density estimation method, although good results can also be obtained by a suitable combination of two binary logistic regression models. Multiclass snore-based classification allows early stratification of subjects according to their severity. This could be the basis of a single channel, snore-based screening procedure for SAHS.

JTD Keywords: Bayes classifier, Kernel density estimation, Sleep apnoea, Snoring