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In the fast paced world of the 21st century, sleep seems to be getting less attention from individuals. The National Sleep Foundation reports that, for adults to function properly, sleep experts recommend 7-9 hours or more of sleep per night [1]. However, 45% of Americans are voluntarily willing to reduce their amount of sleep, with 68% suffering from decreased concentration and 66% experiencing difficulty handling stress as a result of sleep deprivation [2]. Though the people who voluntarily lose sleep can resolve their problem without treatment, 10-13% of Americans (30-40 million) are suffering from clinical sleep disorders that cause them to have disrupted sleep and, hence, affect their health [3]. Literature suggests that over 1 billion people worldwide experience some kind of chronic nasal congestion or snoring and sleep apnea. Interestingly, 70% of people who have trouble sleeping don’t discuss it with their doctor. However, disorders like sleep apnea can lead to severe consequences if left untreated. Yearly, 38,000 deaths are associated with complications from sleep apnea and those that suffer from apnea are 3-6 times more likely to suffer a stroke [4].
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It is evident from these statistics that sleep problems affect a large number of people. However, according to the NIH Sleep Research Plan (June 2002 Draft), current quantification methods of breathing abnormalities predict little, do not allow for screening of large populations and are cumbersome and expensive. To further challenge sleep studies, there is no comprehensive database that defines normal sleep-wake patterns based on age or gender. As a result, the NIH identifies a need for new methods that can non-invasively monitor sleep and respiration to quantify breathing problems and their consequences. Longitudinal, non-invasive monitoring using validated assessment and screening tools is a solution to developing such characterization standards and eventually plays a role in developing a predictive model for sleep hygiene.
This need has been known for several years and attempts have been made at designing equipment to perform longitudinal monitoring. Babbs, et al. introduced a method for obtaining body position through pressure sensing. Wristwatch style accelerometer based sensors known as actigraphs are commonly used in long-term sleep studies, but this equipment only monitors activity [7]. The Static Charge Sensitive Bed (SCSB) tries to find a balance between the two extremes by recording general movement, respiratory movement and a ballistocardiogram (BCG) [8]. This design, however, does not record any airflow measurements, which are extremely helpful in detecting apneas. In addition, the SCSB has difficulty monitoring movements such as twisting motions of the upper body and various leg movements, which are helpful in detecting some behavioral sleep disorders [9]. The SleepSmart project at Stanford University is a system that uses pressure and temperature sensors laid out in a grid pattern [10]. Though relatively ignificant information can be gleaned from this design, it is analysis intensive, since it uses wavelet transformations, and it is unable to pick up movements that are not overall body shifts. Unfortunately, this low-cost system’s associated heart rate sensors must be within 2 cm of the heart to determine a signal and the analysis of the collected data is complicated and expensive [10]. Though these last two systems, SCSB and SleepSmart, represent advances in the goal of long-term sleep monitoring, they are limited use monitoring applicables, not readily available to the general public, and inappropriate for use in the general public at large.
On the other hand, polysomnography, which remains the gold standard in sleep monitoring technology, typically involves an EEG, EOG, two EMGs, ECG, oxygen saturation, nasal airflow and chest wall movements at the rib cage and abdomen [11]. Several portable systems, based on laptop computer technology, have advanced sleep-monitoring technology into dual use capacities - the home and the typical sleep lab environment. Limitations of these systems include: trained clinicians are required to prep the subjects and attach the leads; movement is limited once the equipment is fitted; subjects can be uncomfortable; and the equipment’s availability and expense limit the use to a period of one or two nights. The availability of a validated, low-cost, easy to use assessment and screening tool could increase the efficiency of current sleep lab resources while providing the public with a reliable telehealth option for an overall assessment of their sleep quality.
The NAPS system, developed by the Medical Automation Research Center (MARC) at the University of Virginia, is a low-cost, low-power, physiological sensor-suite that can passively acquire important physiological and environmental characteristics. The MARC research team has completed preliminary proof-of-concept of the NAPS system, with added value in its ability to be deployed to collect data remotely. The NAPS suite will allow subjects to simply lie on a mattress pad, embedded with sensors, to obtain multidimensional data. The data collection sets can be selected to include: body temperature, heart rate, respiration rate, positional mapping and movement; additional development work is being done to monitor airflow. Furthermore, the system can also measure environmental conditions in the immediate surroundings including ambient light level, humidity, and temperature. Once validated, the NAPS system can be used as an effective screening tool for sleep quality assessment, identifying sleep disorders that require a detailed, clinically administered sleep study, as well as an aid to clinicians in the in-home longitudinal assessment of prescribed treatments to relieve sleep problems. Furthermore, the NAPS system’s ability to accurately monitor these important physiological characteristics and sleep longitudinally will provide an individual baseline that can be utilized for assessment purposes, such as detection of trends and changes.
A Passive and Portable System for Monitoring Heart Rate and Detecting Sleep Apnea and Arousals: Preliminary Validation
David Mack, Majd Alwan, Beverely Turner, Paul Suratt, Robin Felder.
Proceedings of the Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare (D2H2), 2 - 4 April 2006, Arlington, VA.
Non-Invasive Analysis of Physiological Signals (NAPS): A Vibration Sensor that Passively Detects Heart and Respiration Rates as Part of a Sensor Suite for Medical Monitoring
Mack DC, Kell SW, Alwan M, Turner B, Felder RA. 2003 Summer Bioengineering Conference.
NAPS wins 2003 Uuniversity-wide Business Concept Competition
Non-Invasive Analysis Of Physiological Signals
Mack, Kell, Alwan, Turner, Wolfe, Felder, Skalak. CBI Steps to Success (2002)
[1] National Sleep Foundation. 2003 January. Let Sleep Work for You
[2] National Sleep Foundation. 2000 March 28. National Sleep Foundation Releases New Statistics on “Sleep in America”
[3] National Sleep Foundation. Nature of Sleep
[4] Snorenet
[5] National Center on Sleep Disorders Research - National Institutes of Health. Sleep Disorders Research Plan Revision, (Draft) June 2002.
[6] Babbs, C. F., Bourland, J. D., Graber, G. P., Jones, J. T., Schoenlein, W. E. 1990. A pressure-sensitive mat for measuring contact pressure distributions of subjects lying on hospital beds. Biomedical Instrumentation and Technology, 24(5): 363-70.
[7] Sadeh, A., Hauri, P. J., Kripke, D. F., Lavie, P. 1995. The role of actigraphy in the evaluation of sleep disorders. Sleep, 18(4): 288-302.
[8] Polo, O., Brissaud, L., Sales, B., Besset, A., Billiard, M. 1988. The validity of the static charge sensitive bed in detecting obstructive sleep apnoeas. European Respiratory Journal, 1(4): 330-6.
[9] Harada, T., Sato, T., Mori, T. 2002. Estimation of Bed-Ridden Human’s Gross and Slight Movement Based on Pressure Sensors Distribution Bed. 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS, October 2002) Conference Proceedings.
[10] Van der Loos, H. F. M., Kobayashi, H., Liu, G., Tai, Y. Y., Ford, J., Norman, J., Tabata, T., Osada, T. 2001. Unobtrusive Vital Signs Monitoring From a Mulitsensor Bed Sheet. RESNA 2001 (Reno, NV, June 2001) Conference Proceedings, pp. 218-220.
[11] Keenan, S. A. 1992. Polysomnography: technical aspects in adolescents and adults. Journal of Clinical Neurophysiology, 9(1): 21-31.