Accurate scoring of the apnea–hypopnea index using a simple non-contact breathing sensor

Authors

  • Zachary T. Beattie,

    Corresponding author
    1. Oregon Center for Aging and Technology, Oregon Health & Science University, Portland, OR, USA
    • Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
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  • Tamara L. Hayes,

    1. Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
    2. Oregon Center for Aging and Technology, Oregon Health & Science University, Portland, OR, USA
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  • Christian Guilleminault,

    1. Stanford Outpatient Medical Center, Stanford University Sleep Medicine Division, Redwood City, CA, USA
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  • Chad C. Hagen

    1. Pacific Sleep Program, Portland, OR, USA
    2. Sleep Disorders Program, Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
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Correspondence:

Zachary T. Beattie, BSc, Oregon Health & Science University, 3303 SW Bond Avenue, CH13B, Portland, OR 97239, USA.

Tel.: 503-418-9315;

fax: 503-418-9311;

e-mail: beattiez@ohsu.edu

Summary

Sleep apnea is a serious condition that afflicts many individuals and is associated with serious health complications. Polysomnography, the gold standard for assessing and diagnosing sleep apnea, uses breathing sensors that are intrusive and can disrupt the patient's sleep during the overnight testing. We investigated the use of breathing signals derived from non-contact force sensors (i.e. load cells) placed under the supports of the bed as an alternative to traditional polysomnography breathing sensors (e.g. nasal pressure, oral-nasal thermistor, chest belt and abdominal belt). The apnea–hypopnea index estimated using the load cells was not different than that estimated using standard polysomnography leads (t44 = 0.37, = 0.71). Overnight polysomnography sleep studies scored using load cell breathing signals had an intra-class correlation coefficient of 0.97 for the apnea–hypopnea index and an intra-class correlation coefficient of 0.85 for the respiratory disturbance index when compared with scoring using traditional polysomnography breathing sensors following American Academy of Sleep Medicine guidelines. These results demonstrate the feasibility of using unobtrusive load cells installed under the bed to measure the apnea–hypopnea index.

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