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 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.

[1]  F. Magrini,et al.  Reduced nocturnal fall in blood pressure, assessed by two ambulatory blood pressure monitorings and cardiac alterations in early phases of untreated essential hypertension , 2003, Journal of Human Hypertension.

[2]  R. Hermida.  Ambulatory Blood Pressure Monitoring in the Prediction of Cardiovascular Events and Effects of Chronotherapy: Rationale and Design of the MAPEC Study , 2007, Chronobiology international.

[3]  Dario Floreano,et al.  Improving actigraph sleep/wake classification with cardio-respiratory signals , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  V. Natale,et al.  Comparison of Two Different Actigraphs with Polysomnography in Healthy Young Subjects , 2008, Chronobiology international.

[5]  Peter H. Veltink,et al.  Ambulatory human motion tracking by fusion of inertial and magnetic sensing with adaptive actuation , 2009, Medical & Biological Engineering & Computing.

[6]  Jung Keun.  3D spinal motion analysis during staircase walking using an ambulatory inertial and magnetic sensing system , 2011 .

[7]  Hiroshi Takahashi,et al.  Non-dipping is a potent predictor of cardiovascular mortality and is associated with autonomic dysfunction in haemodialysis patients. , 2003, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association.

[8]  A. Sadeh,et al.  The role of actigraphy in sleep medicine. , 2002, Sleep medicine reviews.

[9]  A. Sadeh,et al.  Activity-based sleep-wake identification: an empirical test of methodological issues. , 1994, Sleep.

[10]  R. Hermida,et al.  Association of metabolic syndrome and blood pressure nondipping profile in untreated hypertension. , 2009, American journal of hypertension.

[11]  C. Morin,et al.  Actigraphy in the assessment of insomnia. , 2003, Sleep.

[12]  D. J. Mullaney,et al.  Automatic sleep/wake identification from wrist activity. , 1992, Sleep.

[13]  L. Krakoff.  Ambulatory blood pressure monitoring can improve cost-effective management of hypertension. , 1993, American journal of hypertension.

[14]  Alice Stanton,et al.  Superiority of Ambulatory Over Clinic Blood Pressure Measurement in Predicting Mortality: The Dublin Outcome Study , 2005, Hypertension.

[15]  D. Skene,et al.  Comparison between subjective and actigraphic measurement of sleep and sleep rhythms , 1999, Journal of sleep research.

[16]  A. Chesson,et al.  Practice parameters for the use of actigraphy in the assessment of sleep and sleep disorders: an update for 2007. , 2007, Sleep.

[17]  H. Yarandi,et al.  Assessing Sleep Quality and Daytime Wakefulness in Asthma Using Wrist Actigraphy , 2008, The Journal of asthma : official journal of the Association for the Care of Asthma.

[18]  Margot J Davey,et al.  Validation of actigraphy for determining sleep and wake in children with sleep disordered breathing , 2007, Journal of sleep research.

[19]  Steven H. Jones,et al.  Actigraphic assessment of circadian activity and sleep patterns in bipolar disorder. , 2005, Bipolar disorders.

[20]  R. Roos,et al.  Where to record motor activity: an evaluation of commonly used sites of placement for activity monitors. , 1993, Electroencephalography and clinical neurophysiology.

[21]  R. Sack,et al.  A comparison of sleep detection by wrist actigraphy, behavioral response, and polysomnography. , 1997, Sleep.

[22]  W. Elliott.  Prognostic accuracy of day versus night ambulatory blood pressure: a cohort study , 2008 .

[23]  C. Pollak,et al.  The role of actigraphy in the study of sleep and circadian rhythms. , 2003, Sleep.

[24]  D. Kripke,et al.  The role of actigraphy in the evaluation of sleep disorders. , 1995, Sleep.

[25]  F. Magrini,et al.  Target organ damage and non-dipping pattern defined by two sessions of ambulatory blood pressure monitoring in recently diagnosed essential hypertensive patients , 2001, Journal of hypertension.

[26]  Jung-Keun Lee,et al.  Quasi real-time gait event detection using shank-attached gyroscopes , 2011, Medical & Biological Engineering & Computing.

[27]  K. Fujieda,et al.  Actigraphic assessment of sleep disorders in children with chronic fatigue syndrome , 2008, Brain and Development.

[28]  F. Magrini,et al.  Cardiovascular target organ damage in essential hypertensives with or without reproducible nocturnal fall in blood pressure , 2004, Journal of hypertension.