Automatic identification of activity-rest periods based on actigraphy.
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 asses...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 50; no. 4; pp. 329 - 341 |
|---|---|
| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Apr2012
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104545215&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104545215 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Apr2012 vid: 50 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104545215 NLM22382991 2011504883 10.1007/s11517-012-0875-y NLM22382991 104545215 ppf: 329 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Automatic identification of activity-rest periods based on actigraphy. aug: au: Crespo C Aboy M Fernández JR Mojón A Crespo, Cristina Aboy, Mateo Fernández, José Ramón Mojón, Artemio affil: EERE Department, Oregon Institute of Technology, Portland, OR 97006, USA sug: subj: Monitoring, Physiologic Methods Motor Activity Physiology Relaxation Physiology Signal Processing, Computer Assisted Algorithms Blood Pressure Monitoring, Ambulatory Methods Blood Pressure Physiology Human Risk Assessment Methods Young Adult ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|