Sleep/wake measurement using a non‐contact biomotion sensor

We studied a novel non‐contact biomotion sensor, which has been developed for identifying sleep/wake patterns in adult humans. The biomotion sensor uses ultra low‐power reflected radiofrequency waves to determine the movement of a subject during sleep. An automated classification algorithm has been developed to recognize sleep/wake states on a 30‐s epoch basis based on the measured movement signal. The sensor and software were evaluated against gold‐standard polysomnography on a database of 113 subjects [94 male, 19 female, age 53 ± 13 years, apnoea–hypopnea index (AHI) 22 ± 24] being assessed for sleep‐disordered breathing at a hospital‐based sleep laboratory. The overall per‐subject accuracy was 78%, with a Cohen’s kappa of 0.38. Lower accuracy was seen in a high AHI group (AHI >15, 63 subjects) than in a low AHI group (74.8% versus 81.3%); however, most of the change in accuracy can be explained by the lower sleep efficiency of the high AHI group. Averaged across subjects, the overall sleep sensitivity was 87.3% and the wake sensitivity was 50.1%. The automated algorithm slightly overestimated sleep efficiency (bias of +4.8%) and total sleep time (TST; bias of +19 min on an average TST of 288 min). We conclude that the non‐contact biomotion sensor can provide a valid means of measuring sleep–wake patterns in this patient population, and also allows direct visualization of respiratory movement signals.

[1]  Ståle Pallesen,et al.  A comparison of actigraphy and polysomnography in older adults treated for chronic primary insomnia. , 2006, Sleep.

[2]  P. de Chazal,et al.  An Evaluation of a Non-contact Biomotion Sensor with Actimetry , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  J L Alexander,et al.  Derbyshire Neck. Thyroid abnormalities in the Derbyshire Peak district. , 1966, Lancet.

[4]  Conor Heneghan,et al.  SleepMinder: An innovative contact-free device for the estimation of the apnoea-hypopnoea index , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  H. Nagaraja,et al.  How accurately does wrist actigraphy identify the states of sleep and wakefulness? , 2001, Sleep.

[6]  K. Müller,et al.  Automatic sleep stage classification using two-channel electro-oculography , 2007, Journal of Neuroscience Methods.

[7]  B. Jansen,et al.  Sleep staging with movement-related signals. , 1993, International journal of bio-medical computing.

[8]  O. Boric-Lubecke,et al.  Non-contact respiratory rate measurement validation for hospitalized patients , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  C G Caro,et al.  Contactless apnoea detector based on radar. , 1971, Lancet.

[10]  Florian Chapotot,et al.  Automated sleep–wake staging combining robust feature extraction, artificial neural network classification, and flexible decision rules , 2009 .

[11]  Jennifer Caffarel,et al.  Comparison of manual sleep staging with automated neural network-based analysis in clinical practice , 2005, Medical and Biological Engineering and Computing.

[12]  Changzhi Li,et al.  Robust Overnight Monitoring of Human Vital Signs by a Non-contact Respiration and Heartbeat Detector , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[13]  Yoshua Bengio,et al.  Pattern Recognition and Neural Networks , 1995 .

[14]  C. I. Franks,et al.  Contactless respiration monitoring of infants , 1976, Medical and biological engineering.

[15]  A. Schlögl,et al.  An E-Health Solution for Automatic Sleep Classification according to Rechtschaffen and Kales: Validation Study of the Somnolyzer 24 × 7 Utilizing the Siesta Database , 2005, Neuropsychobiology.

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

[17]  Jyrki Lötjönen,et al.  Automatic sleep-wake and nap analysis with a new wrist worn online activity monitoring device vivago WristCare. , 2003, Sleep.

[18]  R. Stickgold,et al.  Nightcap: laboratory and home-based evaluation of a portable sleep monitor. , 1995, Psychophysiology.

[19]  Ståle Pallesen,et al.  Cognitive behavioral therapy vs zopiclone for treatment of chronic primary insomnia in older adults: a randomized controlled trial. , 2006, JAMA.

[20]  G. Rettinger,et al.  The use of an ambulatory, automatic sleep recording device (QUISI version 1.0) in the evaluation of primary snoring and obstructive sleep apnoea. , 2004, Clinical otolaryngology and allied sciences.

[21]  Conor Heneghan,et al.  Assessment of sleep/wake patterns using a non-contact biomotion sensor , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[22]  Robert Stickgold,et al.  Cognitive behavior therapy and pharmacotherapy for insomnia: a randomized controlled trial and direct comparison. , 2004, Archives of internal medicine.

[23]  James Taylor,et al.  The Micropower Impulse Radar , 2000 .

[24]  C. Sullivan,et al.  Respiratory and body movements as indicators of sleep stage and wakefulness in infants and young children , 1996, Journal of sleep research.

[25]  C. Heneghan,et al.  Sleep staging using cardiorespiratory signals , 2007 .

[26]  J. Alihanka,et al.  A new method for long-term monitoring of the ballistocardiogram, heart rate, and respiration. , 1981, The American journal of physiology.

[27]  J. Mattout,et al.  Automatic analysis of single-channel sleep EEG: validation in healthy individuals. , 2007, Sleep.

[28]  A. Rechtschaffen.  Techniques and Scoring System for Sleep Stages of Human Subjects , 1968 .

[29]  D. White,et al.  A novel adaptive wrist actigraphy algorithm for sleep-wake assessment in sleep apnea patients. , 2004, Sleep.

[30]  Ronald R Grunstein,et al.  The validity of wrist actimetry assessment of sleep with and without sleep apnea. , 2008, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[31]  O. Boric-Lubecke,et al.  Range correlation effect on ISM band I/Q CMOS radar for non-contact vital signs sensing , 2003, IEEE MTT-S International Microwave Symposium Digest, 2003.

[32]  James C. Lin,et al.  Microwave sensing of physiological movement and volume change: a review. , 1992, Bioelectromagnetics.

[33]  C. Guilleminault,et al.  Comparison of actigraphic, polysomnographic, and subjective assessment of sleep parameters in sleep-disordered patients. , 2001, Sleep medicine.

[34]  A. Chesson,et al.  The American Academy of Sleep Medicine (AASM) Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications , 2007 .

[35]  Reinhard Steinberg,et al.  BioSomnia—Validity of a mobile system to detect sleep and sleep quality , 2004 .

[36]  Kwang Suk Park,et al.  Non-constraining sleep/wake monitoring system using bed actigraphy , 2006, Medical & Biological Engineering & Computing.

[37]  A. Sadeh,et al.  Instability of sleep patterns in children with attention-deficit/hyperactivity disorder. , 2000, Journal of the American Academy of Child and Adolescent Psychiatry.