Respiratory cycle related EEG changes: modified respiratory cycle segmentation
Respiratory cycle related EEG changes: modified respiratory cycle segmentation
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 aged 7-17 (11.6±3) years (35M:27F) 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 timelocked nasal flow (thermistor) were used in RCREC quantification. 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.
polysomnography, respiratory cycle related eeg changes, inspiratory/expiratory transition, signal processing, computer assisted, rcrec
838-844
Motamedi-Fakhr, Shayan
54878c84-9201-4562-bfbb-fb1ed8759f8b
Moshrefi-Torbati, Mohamed
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Hill, Martyn
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Simpson, David
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S Bucks, Romola S.
eeb5579d-9a2c-4120-b08a-49f6fa36c668
Paul, Annette
8db06fe6-b85a-4e5c-901e-decfe1823301
Hill, Catherine M.
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November 2013
Motamedi-Fakhr, Shayan
54878c84-9201-4562-bfbb-fb1ed8759f8b
Moshrefi-Torbati, Mohamed
65b351dc-7c2e-4a9a-83a4-df797973913b
Hill, Martyn
0cda65c8-a70f-476f-b126-d2c4460a253e
Simpson, David
53674880-f381-4cc9-8505-6a97eeac3c2a
S Bucks, Romola S.
eeb5579d-9a2c-4120-b08a-49f6fa36c668
Paul, Annette
8db06fe6-b85a-4e5c-901e-decfe1823301
Hill, Catherine M.
867cd0a0-dabc-4152-b4bf-8e9fbc0edf8d
Motamedi-Fakhr, Shayan, Moshrefi-Torbati, Mohamed, Hill, Martyn, Simpson, David, S Bucks, Romola S., Paul, Annette and Hill, Catherine M.
(2013)
Respiratory cycle related EEG changes: modified respiratory cycle segmentation.
Biomedical Signal Processing and Control, 8 (6), .
(doi:10.1016/j.bspc.2013.08.001).
Abstract
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 aged 7-17 (11.6±3) years (35M:27F) 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 timelocked nasal flow (thermistor) were used in RCREC quantification. 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.
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Published date: November 2013
Keywords:
polysomnography, respiratory cycle related eeg changes, inspiratory/expiratory transition, signal processing, computer assisted, rcrec
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Mechatronics
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Local EPrints ID: 355674
URI: http://eprints.soton.ac.uk/id/eprint/355674
ISSN: 1746-8094
PURE UUID: a8388061-23fd-44a9-9408-337b0db73d3b
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Date deposited: 03 Sep 2013 13:59
Last modified: 15 Mar 2024 03:14
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Author:
Shayan Motamedi-Fakhr
Author:
Romola S. S Bucks
Author:
Annette Paul
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