General Sleep offers Zmachine Comfort 40 sensors by the case. Each case contains 40 packages of sensors (enough for 40 nights of use), and can be obtained directly from General Sleep.

Effect of Night Float on Anesthesiology Resident Sleep Patterns: An Observational Study
International Anesthesia Research Society (IARS), 2018 Annual Meeting
Dunn L, Kleiman A., Forkin K., Quigg M., Bechtel A., Huffmyer J. and Nemergut E.
University of Virginia

Daily Covariation Of Stress and EEG-Determined Sleep
Associated Professional Sleep Societies (APSS), 2018, 32st Annual Meeting, Sleep (41)
Veeramachaneni K, Dietch JR, Slavish DC, Taylor DJ
University of North Texas

Daily Positive Affect Predicts EEG-Determined REM Duration
Associated Professional Sleep Societies (APSS), 2018, 32st Annual Meeting, Sleep (41)
Hale BS, Messman B, Brown A, Banzuelo H, Nguyen T, Slavish D, Taylor DJ, Dietch JR
University of North Texas

Characterization of the Relationships Between Sleep Duration, Quality, Architecture, and Chemosensory Function in Nonobese Females
Chemical Senses, 2018
Advanced Access Publication Date: 7 March 2018
Szczygiel, EJ., Cho, S., Tucker, RM.
Michigan State University

Sleep Devices: Wearables and Nearables, Informational and Interventional, Consumer and Clinical
Metabolism: clinical and experimental, 2018
Advanced Access Publication Date: 8 December 2017
Bianchi, MT.
Harvard Medical School/Massachusetts General Hospital

Landscape Analysis of Biometric Monitoring Devices (BMDs) Utilized in Assessing Cognition, Sleep and Mobility in Alzheimer's Disease and Other Age-Related Neurological Diseases
Alzheimer's Association International Conference (AAIC), 2017
Kern V., Rhodes J., Karlin D., Fitzer-Attas C., Ropacki M., et al
Critical Path Institute, Biogen, Pfizer, CHDI Foundation, Loma Linda University, et al

Where is your (mind)fulness? It matters for sleep.
Association for Psychological Science (APS), 2017, 29th Annual Meeting
Lipsky, J., Acero, P., Cho, L., Kreidler, P., Zhu, Z., Britton, W.
Brown University

Estimation of Sleep Stages using Cardiac and Accelerometer Data from a Wrist-Worn Device
Associated Professional Sleep Societies (APSS), 2017, 31st Annual Meeting, Sleep (40)
Beattie, Z., Pantelopoulos, A., Ghoreyshi, A., Oyang, Y., Statan, A., Heneghan, C.
Fitbit, Inc.

Advances In the Management of Chronic Insomnia
British Medical Journal (BMJ), 2016;353:i2123
Kay-Stacey M. and Attarian H.
Northwestern University

Current research impacting long-duration space flight sleep and fatigue
American Osteopathic Association (AOA), 2016, 122nd Annual Meeting
Moomaw, R.
NASA - Johnson Space Center

Twenty-four Hours of Sleep, Sedentary Behavior, and Physical Activity with Nine Wearable Devices
Medicine & Science in Sports & Exercise, 2016, Vol. 48(3), 457–465
Rosenberger, ME.¹, Buman, MP.², Haskell, WL.¹, McConnell, MV.¹, Carstensen, LL.^1
1Stanford University
²Arizona State University

Evaluation of an Automated Single-Channel Sleep Staging Algorithm
Nature and Science of Sleep, 2015, Vol. 7, 101-111
Wang, Y., Loparo, K., Kelly, M., Kaplan, R.

Performance Evaluation of an Automated Single-Channel Sleep Staging Algorithm
Associated Professional Sleep Societies (APSS), 2015, 29th Annual Meeting, Sleep (38)
Wang, Y., Loparo, K., Kaplan, R.

A Pilot Study: Delirium in the Intensive Care Unit: Utilizing the Zmachine Sleep Monitor for Assessment
UCLA Department of Nursing, 14th Annual Research and Evidence-Based Practice Conference, 2015
Also appears on HEHD/SOE Research Forum, Clemson University
Whitcomb, J. J., Edwards, S., Ethridge, E., Fogle, S.
Clemson University

Performance Evaluation of an Automated Single Channel Sleep-Wake Detection Algorithm
Nature and Science of Sleep, 2014, Vol. 6, 113-122
Kaplan, R., Wang, Y., Loparo, K., Kelly, M., Bootzin, R.

Classification of Stage-1 in a Single Channel Wake-Sleep Detection System
Associated Professional Sleep Societies (APSS), 2009, 23rd Annual Meeting, Sleep (32)
Kaplan, R. F., Wang, Y., Bootzin, R.R., Loparo, K.A.

A Single Channel Wake-Sleep Detection System
Associated Professional Sleep Societies (APSS), 2008, 22nd Annual Meeting, Sleep (31)
Wang, Y., Kaplan, R. F., Bootzin, R. R., and Loparo, K. A.

Evaluation of an automated system for in-home behavioral treatment of chronic insomnia: Part III
Associated Professional Sleep Societies (APSS), 2007, 21st Annual Meeting, Sleep (30)
Kaplan, R. F., Wang, Y., Bootzin, R.R., Loparo, K.A.

Evaluation of an automated system for in-home behavioral treatment of chronic insomnia: Part II
Associated Professional Sleep Societies (APSS), 2006, 20th Annual Meeting, Sleep (29)
Kaplan, R. F., Wang, Y., Bootzin, R.R., Loparo, K.A.

Evaluation of an automated system for in-home behavioral treatment of chronic insomnia: Part I
Associated Professional Sleep Societies (APSS), 2006, 20th Annual Meeting, Sleep (29)
Kaplan, R. F., Wang, Y., Bootzin, R.R., Loparo, K.A.

Very high frequency energy (Kappa) in human surface EEG: Correlation with behavioral state
Associated Professional Sleep Societies (APSS), 2000, 14th Annual Meeting, Sleep (23)
Richardson, G.S., Kaplan, R.F., Loparo, K.A., Johnson, S.E., Roth, T.

Enhancing fatigue assessment in aviation settings: Correlation of EEG-based DDAS with performance
Aviation, Space, and Environmental Medicine, 1998, 69(3)
Kaplan, R.F., Loparo, K. A., Nesthus, T.E., Schroeder, D.J.

Four-choice serial reaction time and visual search performance during 34 hr of sleep loss
Aviation, Space, and Environmental Medicine, 1998, 69(3)
Nesthus, T.E., Scarborough, A.L., Schroeder, D.J., Kaplan, R.F., Loparo, K.A. , and Thorne, D.

Subjective sleepiness ratings and body temperature during 34 hours of sleep deprivation
Aviation, Space, and Environmental Medicine, 1998, 69(3)
Schroeder, D.J., Nesthus, T.E., Scarborough, A., Thompson, J., Kaplan, R.F., Loparo, K.A.

The Zmachine Data Viewer software, which is available as a free download on our website, will run on any computer running the Windows 7 or later operating system, and Apple computers running Windows emulators. This software is powerful, easy-to-use, and runs locally on your PC to display, graph, analyze, and organize your Zmachine data. If you wish to read the files directly (i.e. without the data viewer), all sleep scoring reports and sleep statistic summaries are stored as ASCII .csv (comma separated variable) files. They can be opened directly using Excel, Matlab, text editors and statistics programs. The raw EEG data is stored as 2-Byte unsigned integers (little-endian), 16-bit resolution, and sampled at 2500Hz. Sample Matlab code available in the Support section for loading the raw EEG data files.

The very act of sleeping precludes conscious knowledge of time and events, and reliance on subjective self-reports of sleep, without objective data, can lead to an incomplete and potentially misleading picture. With the Zmachine Insight, you can now obtain objective sleep information based on the EEG signal. Automatically scored wake, light sleep, deep sleep and REM is recorded every 30 seconds in the patient’s home where sleep is most natural.

Not really. The Zeo was strictly a consumer product that made no medical or performance claims, whereas the Zmachine is a medical device that is cleared and regulated by the US FDA, and manufactured in FDA registered facilities. The Zmachine is intended for use by medical professionals, academic and industrial researchers.

The Zmachine Insight system uses one-time-use, self-stick, disposable EEG sensors. Sensors are located completely outside of the normal hairline (mastoids and neck/shoulder ground), and are easily self-applied by the patient. As opposed to the forehead or other more obtrusive locations, the location of the sensors does not interfere with sleep. The Zmachine Insight is small, portable, and battery operated for safe and convenient in-home use.

No. The Zmachine was specifically designed for convenient, in-home use and requires very little technical knowledge to apply. The Zmachine system includes an easy-to-read User Guide, and General Sleep has produced several short videos illustrating various aspects of setting up and using the system.

No. While the data revealed by the Zmachine is highly relevant to sleep specialists and researchers, the intimate knowledge of a patient’s sleep can be highly beneficial to healthcare providers across a wide spectrum of medical specialties. Pain Management, Neurology, Sports Medicine, Geriatrics and many others are all discovering new uses for the valuable information provided by the Zmachine system. Through its unique combination of accuracy and ease-of-use, the Zmachine is making information available to healthcare providers which was previously unavailable.

The Insight+ can display any combination of EEG-derived Total Sleep Time, Sleep Efficiency, Latency to Persistent Sleep, and Wake After Sleep Onset, or none at all, and is displayed each time the user disconnects from the Zmachine. Information is updated in real time and pertains to the current night of sleep. This feature can be especially useful in treating insomnia patients with sleep state misperception.

Yes. Data from the Zmachine microSD card can be transmitted via an e-mail attachment or uploaded directly to your web portal. This information can then be imported directly into the Zmachine data viewer. No user identifiable information is contained in the Zmachine data file. Instructions are included with the Zmachine system. Healthcare providers can use this capability to receive the data files in advance of face-to-face visits to improve work flow and/or reduce the total number of visits needed. Researchers can use this capability to work remotely with test subjects to monitor compliance and/or ongoing results.

Yes, replacement accessories for your Zmachine system can be purchased directly from General Sleep. Please contact us for more details.

Using objective EEG-derived sleep state information, the Insight+ can also be programmed to produce an alert to leave bed when sleep isn’t achieved within a pre-specified period of time. This can be helpful in assisting those patients that spend too much time in bed awake in knowing when they should leave bed and stop trying to fall asleep for a while. The parameters of this alert generation can be enabled/disabled or customized by the clinician.

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