Two-group classification of patients with obstructive sleep apnea based on analysis of brain recurrence

Highlights • The severity of obstructive sleep apnea (OSA) was directly reflected in the sleep EEG. • Binary classification of patients with OSA (mild or moderate) was achieved by analyzing the EEG from one derivation. • Analysis of brain recurrence is an effective algorithmic technique for extracti...

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Published inClinical neurophysiology Vol. 125; no. 6; pp. 1174 - 1181
Main Authors Kim, Paul Y, McCarty, David E, Wang, Lei, Frilot, Clifton, Chesson, Andrew L, Marino, Andrew A
Format Journal Article
LanguageEnglish
Published Netherlands Elsevier Ireland Ltd 01.06.2014
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Abstract Highlights • The severity of obstructive sleep apnea (OSA) was directly reflected in the sleep EEG. • Binary classification of patients with OSA (mild or moderate) was achieved by analyzing the EEG from one derivation. • Analysis of brain recurrence is an effective algorithmic technique for extracting useful diagnostic information regarding OSA from the EEG.
AbstractList Highlights • The severity of obstructive sleep apnea (OSA) was directly reflected in the sleep EEG. • Binary classification of patients with OSA (mild or moderate) was achieved by analyzing the EEG from one derivation. • Analysis of brain recurrence is an effective algorithmic technique for extracting useful diagnostic information regarding OSA from the EEG.
To demonstrate that the severity of obstructive sleep apnea (OSA) could be predicted algorithmically by means of recurrence analysis of the sleep-staged electroencephalogram (EEG). A randomly selected cohort of 20 sleep-staged patients with OSA (apnea-hypopnea index (AHI) 5-30) was divided into mild and moderate sub-cohorts (AHI 5-15, 16-30, respectively), and the sleep EEG (C3) was analyzed using analysis of brain recurrence (ABR) (LSU cohort). Twenty distinct but related markers for sleep depth and fragmentation were computed from four ABR variables, and a marker function capable of classifying each patient into one of the two sub-cohorts was determined by linear discriminant analysis. Classification accuracy of individual patients was evaluated using area under the receiver operator characteristics curve (AUROC). As a control procedure, 20 additional sleep-staged patients with OSA whose polysomnographic data was obtained from an independent database were also evaluated (SHHS cohort). On average, markers for sleep depth were reduced and those for sleep fragmentation were increased in the patients with moderate OSA, as expected. All patients in both cohorts were correctly classified using as few as 5-6 markers. The degree of severity of OSA was reflected in objective changes in the sleep EEG. Recurrence analysis of the EEG potentially has uses beyond identification of the degree of OSA.
•The severity of obstructive sleep apnea (OSA) was directly reflected in the sleep EEG.•Binary classification of patients with OSA (mild or moderate) was achieved by analyzing the EEG from one derivation.•Analysis of brain recurrence is an effective algorithmic technique for extracting useful diagnostic information regarding OSA from the EEG. To demonstrate that the severity of obstructive sleep apnea (OSA) could be predicted algorithmically by means of recurrence analysis of the sleep-staged electroencephalogram (EEG). A randomly selected cohort of 20 sleep-staged patients with OSA (apnea–hypopnea index (AHI) 5–30) was divided into mild and moderate sub-cohorts (AHI 5–15, 16–30, respectively), and the sleep EEG (C3) was analyzed using analysis of brain recurrence (ABR) (LSU cohort). Twenty distinct but related markers for sleep depth and fragmentation were computed from four ABR variables, and a marker function capable of classifying each patient into one of the two sub-cohorts was determined by linear discriminant analysis. Classification accuracy of individual patients was evaluated using area under the receiver operator characteristics curve (AUROC). As a control procedure, 20 additional sleep-staged patients with OSA whose polysomnographic data was obtained from an independent database were also evaluated (SHHS cohort). On average, markers for sleep depth were reduced and those for sleep fragmentation were increased in the patients with moderate OSA, as expected. All patients in both cohorts were correctly classified using as few as 5–6 markers. The degree of severity of OSA was reflected in objective changes in the sleep EEG. Recurrence analysis of the EEG potentially has uses beyond identification of the degree of OSA.
OBJECTIVETo demonstrate that the severity of obstructive sleep apnea (OSA) could be predicted algorithmically by means of recurrence analysis of the sleep-staged electroencephalogram (EEG).METHODSA randomly selected cohort of 20 sleep-staged patients with OSA (apnea-hypopnea index (AHI) 5-30) was divided into mild and moderate sub-cohorts (AHI 5-15, 16-30, respectively), and the sleep EEG (C3) was analyzed using analysis of brain recurrence (ABR) (LSU cohort). Twenty distinct but related markers for sleep depth and fragmentation were computed from four ABR variables, and a marker function capable of classifying each patient into one of the two sub-cohorts was determined by linear discriminant analysis. Classification accuracy of individual patients was evaluated using area under the receiver operator characteristics curve (AUROC). As a control procedure, 20 additional sleep-staged patients with OSA whose polysomnographic data was obtained from an independent database were also evaluated (SHHS cohort).RESULTSOn average, markers for sleep depth were reduced and those for sleep fragmentation were increased in the patients with moderate OSA, as expected. All patients in both cohorts were correctly classified using as few as 5-6 markers.SIGNIFICANCEThe degree of severity of OSA was reflected in objective changes in the sleep EEG. Recurrence analysis of the EEG potentially has uses beyond identification of the degree of OSA.
Author Wang, Lei
Marino, Andrew A
Kim, Paul Y
Frilot, Clifton
McCarty, David E
Chesson, Andrew L
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Issue 6
Keywords Biomarkers
Sleep fragmentation
Linear discrimination analysis
Brain recurrence analysis
Obstructive sleep apnea
Sleep depth
Language English
License Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
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Snippet Highlights • The severity of obstructive sleep apnea (OSA) was directly reflected in the sleep EEG. • Binary classification of patients with OSA (mild or...
•The severity of obstructive sleep apnea (OSA) was directly reflected in the sleep EEG.•Binary classification of patients with OSA (mild or moderate) was...
To demonstrate that the severity of obstructive sleep apnea (OSA) could be predicted algorithmically by means of recurrence analysis of the sleep-staged...
OBJECTIVETo demonstrate that the severity of obstructive sleep apnea (OSA) could be predicted algorithmically by means of recurrence analysis of the...
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StartPage 1174
SubjectTerms Algorithms
Area Under Curve
Biomarkers
Brain recurrence analysis
Cohort Studies
Electroencephalography
Female
Humans
Linear discrimination analysis
Male
Middle Aged
Neurology
Obstructive sleep apnea
Polysomnography - methods
ROC Curve
Sleep Apnea, Obstructive - classification
Sleep Apnea, Obstructive - complications
Sleep Apnea, Obstructive - diagnosis
Sleep Apnea, Obstructive - physiopathology
Sleep Deprivation - complications
Sleep Deprivation - diagnosis
Sleep Deprivation - physiopathology
Sleep depth
Sleep fragmentation
Sleep Stages - physiology
Title Two-group classification of patients with obstructive sleep apnea based on analysis of brain recurrence
URI https://www.clinicalkey.es/playcontent/1-s2.0-S1388245713011711
https://dx.doi.org/10.1016/j.clinph.2013.11.002
https://www.ncbi.nlm.nih.gov/pubmed/24290851
https://search.proquest.com/docview/1524817285
Volume 125
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