title: Assessment of sleep/wake patterns using a non-contact biomotion sensor.
abstract: We evaluate a contact-less continuous measuring system measuring respiration and activity patterns system for identifying sleep/wake patterns in adult humans. The system is based on the use of a novel non-contact biomotion sensor, and an automated signal analysis and classification system. The sleep/wake detection algorithm combines information from respiratory frequency, magnitude, and movement to assign 30 s epochs to either wake or sleep. Comparison to a standard polysomnogram system utilizing manual sleep stage classification indicates excellent results. It has been validated on overnight studies from 12 subjects. Wake state was correctly identified 69% and sleep with 88%. Due to its ease-of-use and good performance, the device is an excellent tool for long term monitoring of sleep patterns in the home environment in an ultraconvenient fashion.
authors.affiliation: BiancaMed, NovaUCD, University College Dublin, Belfield, Dublin 4, Ireland. philip.dechazal@biancamed.com
authors.forename: Philip
authors.initials: P
authors.lastname: de Chazal
authors.forename: Emer
authors.initials: E
authors.lastname: O'Hare
authors.forename: Niall
authors.initials: N
authors.lastname: Fox
authors.forename: Conor
authors.initials: C
authors.lastname: Heneghan
mesh_terms: D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004867:Equipment Design; D019544:Equipment Failure Analysis; D006801:Humans; D009043:Motor Activity / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D017286:Polysomnography / Q000295:instrumentation* / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014159:Transducers; D014851:Wakefulness / Q000502:physiology*