title: Automatic identification of activity-rest periods based on actigraphy.
abstract: We describe a novel algorithm for identification of activity/rest periods based on actigraphy signals designed to be used for a proper estimation of ambulatory blood pressure monitoring parameters. Automatic and accurate determination of activity/rest periods is critical in cardiovascular risk assessment applications including the evaluation of dipper versus non-dipper status. The algorithm is based on adaptive rank-order filters, rank-order decision logic, and morphological processing. The algorithm was validated on a database of 104 subjects including actigraphy signals for both the dominant and non-dominant hands (i.e., 208 actigraphy recordings). The algorithm achieved a mean performance above 94.0%, with an average number of 0.02 invalid transitions per 48 h.
authors.affiliation: EERE Department, Oregon Institute of Technology, Portland, OR 97006, USA. cristina.crespo@oit.edu
authors.forename: Cristina
authors.initials: C
authors.lastname: Crespo
authors.forename: Mateo
authors.initials: M
authors.lastname: Aboy
authors.forename: José Ramón
authors.initials: JR
authors.lastname: Fernández
authors.forename: Artemio
authors.initials: A
authors.lastname: Mojón
mesh_terms: D056044:Actigraphy / Q000379:methods*; D000465:Algorithms; D001794:Blood Pressure / Q000502:physiology; D018660:Blood Pressure Monitoring, Ambulatory / Q000379:methods; D006801:Humans; D009043:Motor Activity / Q000502:physiology*; D012146:Rest / Q000502:physiology*; D018570:Risk Assessment / Q000379:methods; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult