title: Towards automated sleep state estimation using a Holter-oximeter.
abstract: Single-channel ECG and finger photoplethysmogram were used to estimate sleep states in obstructive sleep apnea patients. Overnight Holter-oximeter recordings from 14 subjects with suspected sleep apnea were analysed. Parameters including the RR interval, photoplethysmogram amplitude and rise time, and pulse arrival time were characterised and a Hidden Markov Model classifier used to identify Wake, REM and Sleep states. An overall accuracy of 77% and Cohen's kappa of 0.54 was achieved, establishing a baseline level of usefulness for sleep state estimation using a Holter-oximeter. Autonomic arousals detected using pulse arrival time also appeared to be strongly correlated with Epworth Sleepiness Scale scores.
authors.affiliation: School of Electrical, Electronic and Mechanical Engineering, University College Dublin, Ireland. echua@ee.ucd.ie
authors.forename: C-P
authors.initials: CP
authors.lastname: Chua
authors.forename: J
authors.initials: J
authors.lastname: Garvey
authors.forename: S
authors.initials: S
authors.lastname: Redmond
authors.forename: C
authors.initials: C
authors.lastname: Heneghan
authors.forename: W T
authors.initials: WT
authors.lastname: McNicholas
mesh_terms: D000328:Adult; D000368:Aged; D001785:Blood Gas Monitoring, Transcutaneous; D004562:Electrocardiography / Q000379:methods; D005260:Female; D006801:Humans; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D017156:Photoplethysmography / Q000379:methods; D012891:Sleep Apnea Syndromes / Q000097:blood* / Q000503:physiopathology; D012894:Sleep Stages