title: Sleep/wake detection based on cardiorespiratory signals and actigraphy.
abstract: We investigated the potential of adding cardiac and respiratory activity information to actigraphy for sleep-wake staging. A dataset of 35 recordings with full polysomnography and actigraphy was used to assess the performance of an automated sleep/wake Bayesian classifier using electrocardiogram, inductance plethysmogram estimate of respiratory effort and actigraphy. The best performance was achieved with the linear discriminant model that provided an agreement of Cohen's kappa=0.62 for one of the configurations of the classifier, corresponding to an accuracy of 86.8%, a sensitivity of 66.9% and a specificity of 93.1%. It shows that combining different vital signs for a home sleep-wake staging system could be a promising approach.
authors.affiliation: Philips Research Europe, Weisshausstr. 2, 52066 Aachen, Germany. sandrine.devot@philips.com
authors.forename: Sandrine
authors.initials: S
authors.lastname: Devot
authors.forename: Reimund
authors.initials: R
authors.lastname: Dratwa
authors.forename: Elke
authors.initials: E
authors.lastname: Naujokat
mesh_terms: D056044:Actigraphy / Q000379:methods*; D005260:Female; D006321:Heart / Q000502:physiology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D017286:Polysomnography / Q000379:methods*; D012119:Respiration; D012890:Sleep / Q000502:physiology*; D007319:Sleep Initiation and Maintenance Disorders / Q000503:physiopathology; D014851:Wakefulness / Q000502:physiology*