Project 2: Dreem hardware
compared to PSG

At the Dreem company we’re constantly fine-tuning our hardware to make it as accurate as possible. Dreem recently published a clinical study testing our hardware against PSG.

How Dreem hardware compares to PSG in the tracking of sleep stages.

Sleep clinics and labs employ a sleep monitoring technology called polysomnography (PSG). Brain activity, heart rate, respiratory rate and movement are measured but it is brain activity that gives the most precise data of a person’s sleep.

Dreem hardware is equipped with sensors that measure these biomarkers, in order to monitor sleep as precisely as PSG. On the graph you’ll see how the signal coming from Dreem’s sensors compares with that of the PSG in tracking sleep stages (Wake, N1, N2, N3 and REM sleep).

* Sources: The Dreem Headband as an Alternative to Polysomnography for EEG Signal Acquisition and Sleep Staging - Pierrick J. Arnal, Valentin Thorey, Michael E. Ballard, Albert Bou Hernandez, Antoine Guillot, Hugo Jourde, Mason Harris, Mathias Guillard, Pascal Van Beers, Mounir Chennaoui, and Fabien Sauvet.

Measuring and
scoring sleep

A traditional PSG requires experts to manually score the readings. Dreem hardware’s built-in algorithms perform the same task. Here’s how Dreem compares to the experts as well as a basic sleep tracker.

* Sources: Dreem - Arnal PJ, Thorey V, Ballard ME et al. The Dreem Headband as an Alternative to Polysomnography for EEG Signal Acquisition and Sleep Staging. Biorxiv doi:

** Sources: AASM Experts - Danker-Hopfe H, Anderer P, Zeitlhofer J et al. Interrater reliability for sleep scoring according to the Rechtschaffen & Kales and the new AASM standard. J Sleep Res. 2009 Mar;18(1):74-84. doi: 10.1111/j.1365-2869.2008.00700.x.

*** Sources: Trackers - Beattie Z, Oyang Y, Statan A et al. Estimation of sleep stages in a healthy adult population from optical plethysmography and accelerometer signals. Physiol Meas. 2017 Oct 31;38(11):1968-1979. doi: 10.1088/1361-6579/aa9047.

Project 1: Dreem & CBT-I

Insomnia, that is reoccuring trouble falling asleep and staying asleep, is measured by a questionnaire assessment tool known as the Insomnia Severity Index (ISI). The final score (from 0-28) shows how severe someone’s insomnia is. Anything over 14 is considered clinical insomnia.

Cognitive Behavioral Therapy for Insomnia (CBT-I) is widely considered by the medical community, including the ACP and APA.

Ye Y, Chen N, Chen J, et al Internet-based cognitive–behavioural therapy for insomnia (ICBT-i): a meta-analysis of randomised controlled trials BMJ Open 2016;6:e010707. doi: 10.1136/bmjopen-2015-010707

* Sources: Test on 154 people.

Redefining how we treat poor sleep

At the Dreem company, we’re constantly looking for new ways to make effective sleep solutions accessible to as many people as possible. Our coaching programs help people break the cycle of difficult sleep, addressing thinking and modifying actions in order to break the cycle of bad sleep and put in place new sleep routines. The results so far have proven extremely promising.

of users no longer meet the clinical cutoff for insomnia.

Our results

In 7 years, we’ve collected more than 2M nights of sleep data and helped thousands of people improve their sleep.

Faster falling asleep

Overall impact

91^%

of users fall asleep faster and more easily.

More precisely

-21^min

less time falling asleep (Average for users who took over 30 minutes to fall asleep).

Less nocturnal awakenings

Overall impact

78^%

of users have less fragmented nights.

More precisely

-2.5

less nocturnal awakenings on average.

*Data taken from users on completion of the Dreem coaching program.

Our partnerships

University of Cambridge, UK

Decoding the subjective experience of meditative states from daily EEG recordings.

Washington University St Louis Hospital, USA

Postoperative delirium linked with cognitive and EEG markers.

Cardiff University, UK

Optimizing auditory closed-loop stimulation of slow waves.

University of Friburg, Switzerland

Targeted memory reactivation during sleep.

Bard University, US

Targeted memory reactivation during naps in healthy volunteers.

Biomedical research center of the armed force, France

Sleep recording in various protocols including subjects with sleep debt.

Human Brain Project, France

European SlowDyn Consortium (FLAGERA) on Slow Wave Dynamics.

University of Paris Sorbonne Cité, France

Optimisation of slow wave sleep by non-invasive techniques.

University of Cambridge, UK

Decoding the subjective experience of meditative states from daily EEG recordings.
Washington University St Louis Hospital, USA

Postoperative delirium linked with cognitive and EEG markers.
Cardiff University, UK

Optimizing auditory closed-loop stimulation of slow waves.
University of Friburg, Switzerland

Targeted memory reactivation during sleep.
Bard University, US

Targeted memory reactivation during naps in healthy volunteers.
Biomedical research center of the armed force, France

Sleep recording in various protocols including subjects with sleep debt.
Human Brain Project, France

European SlowDyn Consortium (FLAGERA) on Slow Wave Dynamics.
University of Paris Sorbonne Cité, France

Optimisation of slow wave sleep by non-invasive techniques.

Scientific papers & publications

Chambon, S., Thorey, V., Arnal, P. J., Mignot, E., & Gramfort, A.

DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal. Journal of Neuroscience Methods.

Arnal P. J., Thorey V., Ballard M. E., Hernandez A. B, Guillot A., Jourde H., Harris M., Guillard M., Van Beers P., Chennaoui M., Sauvet F.

The Dreem Headband as an Alternative to Polysomnography for EEG Signal Acquisition and Sleep Staging. Biorxiv

Debellemaniere E., Gomez-Merino D., Erblang M., Dorey R., Genot M., Perreaut-Pierre E., Pisani A., Rocco L., Sauvet F., Léger D., Rabat A., Chennaoui M.

Using relaxation techniques to improve sleep during naps. Ind Health, 1;56(3):220-227

Debellemaniere, E., Chambon, S., Pinaud, C., Thorey, V., Dehaene, D., Léger, D., ... & Galtier, M. N.

Performance of an ambulatory dry-EEG device for auditory closed-loop stimulation of sleep slow oscillations in the home environment. Frontiers in human neuroscience, 12, 88.

Chambon, S., Galtier, M. N., Arnal, P. J., Wainrib, G., & Gramfort, A.

A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series, IEEE Transac.

Léger, D., Debellemaniere, E., Rabat, A., Bayon, V., Benchenane, K., & Chennaoui, M.

Slow-wave sleep: From the cell to the clinic. Sleep medicine reviews, 41, 113-132.

Ask a quote for non commercial study.

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Science in action.

Project 2: Dreem hardwarecompared to PSG