title: Activity-based sleep-wake identification in infants.
abstract: Actigraphy offers one of the best-known alternatives to polysomnography for sleep-wake identification. The advantages of actigraphy include high accuracy, simplicity of use and low intrusiveness. These features allow the use of actigraphy for determining sleep-wake states in such highly sensitive groups as infants. This study utilizes a motion sensor (accelerometer) for a dual purpose: to determine an infant's position in the crib and to identify sleep-wake states. The accelerometer was positioned over the sacral region on the infant's diaper, unlike commonly used attachment to an ankle. Opposed to broadly used discriminant analysis, this study utilized logistic regression and neural networks as predictors. The accuracy of predicted sleep-wake states was established in comparison to the sleep-wake states recorded by technicians in a polysomnograph study. Both statistical and neural predictors of this study provide an accuracy of approximately 77-92% which is comparable to similar studies achieving prediction rates of 85-95%, thus validating the suggested methodology. The results support the use of body motion as a simple and reliable method for determining sleep-wake states in infants. Nonlinear mapping capabilities of the neural network benefit the accuracy of sleep-wake state identification. Utilization of the accelerometer for the dual purpose allows us to minimize intrusiveness of home infant monitors.
authors.affiliation: Department of Electrical and Computer Engineering, Clarkson University, Potsdam, NY 13699, USA. esazonov@ieee.org
authors.forename: Edward
authors.initials: E
authors.lastname: Sazonov
authors.forename: Nadezhda
authors.initials: N
authors.lastname: Sazonova
authors.forename: Stephanie
authors.initials: S
authors.lastname: Schuckers
authors.forename: Michael
authors.initials: M
authors.lastname: Neuman
mesh_terms: D005260:Female; D006801:Humans; D007223:Infant; D018730:Infant Behavior; D008297:Male; D008991:Monitoring, Physiologic; D009068:Movement; D016571:Neural Networks, Computer; D017286:Polysomnography; D012044:Regression Analysis; D012447:Sacrum; D012680:Sensitivity and Specificity; D012890:Sleep / Q000502:physiology*