Respiratory cycle related EEG changes: Modified respiratory cycle segmentation

• Published in: Biomedical signal processing and control Vol. 8; no. 6; pp. 838 - 844
• Main Authors: Motamedi-Fakhr, Shayan, Moshrefi-Torbati, Mohamed, Hill, Martyn, Simpson, David, Bucks, Romola S., Carroll, Annette, Hill, Catherine M.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2013
• Subjects: Computer assisted; Inspiratory/expiratory transition; Polysomnography; RCREC; Respiratory cycle related EEG changes; Signal processing; Signal processing; Respiratory cycle related EEG changes; Inspiratory/expiratory transition; Polysomnography; RCREC; Computer assisted
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2013.08.001

•Respiratory cycle related EEG change (RCREC) is a promising parameter in sleep analysis.•RCREC is a change parameter that looks at EEG variability within respiratory cycles.•A key step in quantification of RCREC is respiratory signal segmentation.•We have introduced a new segmentation routine that significantly accentuates RCREC.•A few technical points regarding the calculation procedure of RCREC are made. Respiratory cycle related EEG change (RCREC) is characterized by significant relative EEG power changes within different stages of respiration during sleep. RCREC has been demonstrated to predict sleepiness in patients with obstructive sleep apnoea and is hypothesized to represent microarousals. As such RCREC may provide a sensitive marker of respiratory arousals. A key step in quantification of RCREC is respiratory signal segmentation which is conventionally based on local maxima and minima of the nasal flow signal. We have investigated an alternative respiratory cycle segmentation method based on inspiratory/expiratory transitions. Sixty two healthy paediatric participants were recruited through staff of local universities in Bolivia. Subjects underwent attended polysomnography on a single night (Compumedics PS2 system). Studies were sleep staged according to standard criteria. C3/A2 EEG channel and time-locked nasal flow (thermistor) were used in RCREC quantification. Forty Seven subjects aged 7–17 (11.4±3) years (24M:23F) were found to have usable polysomnographs for the purpose of RCREC calculation. Respiratory cycles were segmented using both the conventional and novel (transition) methods and differences in RCREC derived from the two methods were compared in each frequency band. Significance of transition RCREC as measured by Fisher's F value through analysis of variance (ANOVA) was found to be significantly higher than the conventional RCREC in all frequency bands (P<0.05) but beta. This increase in statistical significance of RCREC as demonstrated with the novel transition segmentation approach suggests better alignment of the respiratory cycle segments with the underlying physiology driving RCREC.