Experience the power of cloud-AI

Seamless Integration

Our technology is both device and software agnostic. We seamlessly integrate into your existing workflow with Zero disruption and high level of inter-operability. We also include two free PSG viewers, one tailored for researchers and one for clinicians.

Accurate and Reliable

Current version of our scorer is built ground up from scratch using nearly 100,000 hours of PSG data collected from 6000+ subjects from 60+ centers across the World. Our system beats our previously published (Patanaik et al., 2018, Sleep doi:10.1093/sleep/zsy041) results by 30 to 40% lower error rates.

Bank Grade Security & Open Standards

Before sending it to our cloud servers, we process your raw data on-premise and ingest it using proprietary but open communication standards ensuring full anonymity and transparency. We use bank level security (128-bit SSL encryption) to ensure that your data is safe. Learn more about our REST APIs and how you can integrate them in your system here.

Improved Productivity & Throughput

One night of sleep record takes anywhere between 1.0 to 2.0 hours to score by an expert technologist. What if you could get hundreds of such records scored at expert level accuracy and higher reliability in seconds. In this world of ever increasing data - time and speed is everything! Z3Score can dramitically increase your research productivity and throughput.

Simplify Your PSG Setup

Z3Score is the only commercial package leveraging deep-learning (Luigi, et al. Sleep medicine reviews (2019)). This allows us to move beyond standard PSG channels and work with reduced and alternate channels. This includes single channel EOG, EEG or any combination of it. This can significantly reduce set-up times and scalability of your experiments.

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Real-time scoring over the cloud

Our proprietary cloud architecture allows you to score thousands of records at the click of a button even on slow internet connections. In fact, our technologies are so fast, we can sleep score your data in real-time! Unlocking applications not possible before, like real-time MSLT, neuro-feedback etc.

Reference Code

Sleep-tracking from Heart-rate variability


Measure sleep ergonomically and at scale using Z3Score-HRV. Z3Score works with heart-rate variability (HRV) measured from wearable, non-contact trackers or ECG patches. 

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what our users are saying

  • Z3Score has been a revelation to our research group. It enables previously unprecedented levels of throughput and precision.
    Oluwaseun Johnson-Akeju, M.D.
    Massachusetts General Hospital, USA
  • Z3score is easy to deploy and use. It not only shortens the time-consuming task of sleep scoring by a factor of 10, it also takes a lot of the uncertainty out of the process.
    Asst Prof Lim Ziqiang Julian
    Duke-NUS Medical School, Singapore
  • With their flexible technology and incredibly responsive support team, Z3score has enabled our Bard undergraduates to learn how to score sleep and conduct advanced targeted memory reactivation studies as though they were part of a much larger research institution. Sleep doesn't always come easily, but Z3score makes the process of scoring sleep as easy as can be.
    Asst Prof Justin Hulbert
    Memory Dynamics Lab at Bard College, New York
  • Neurobit technologies and their automated sleep scoring system are all absolutely wonderful. The services are very user-friendly and provide near-immediate results. The scoring was much better than anticipated. I fully recommend their services.
    Dr. Nicole Gervais
    University of Toronto, Canada
  • This sleep scoring system is really easy to use and is so fast to return the reliable scores. I greatly appreciate their through support.
    Asst Prof Masaki Takeda
    Kochi University of Technology, Japan
  • Z3Score has made real-time sleep staging applications simple and possible. We can precisely time stimuli or manipulate sleep length in order to uncover mechanisms behind the mystery of sleep or enhance certain sleep features in order to boost learning and memory performance.
    Dr JuLynn Ong
    National University of Singapore, Singapore


We provide flexible licensing to suite your needs. 


Per Credit

Request trial
  • One Year validity
  • 1 Credit = 1 Hour of scoring
  • Purchased in bulk of $300
  • Zero-Click™ Integration
  • Reduced channel scoring
  • (Staging/Respiration/LM/Spindle/Artifact)*
  • *Exact start and end of events reported
  • Academic and non-clinical use only

Per Month

  • Unlimited scoring
  • Access to real-time scoring
  • One Year billing cycle
  • Zero-Click™ Integration
  • Reduced channel scoring
  • (Staging/Respiration/LM/Spindle/Artifact)*
  • *Exact start and end of events reported
  • Academic and non-clinical use only

(starting price)

Contact Sales
  • Unlimited scoring
  • Access to real-time scoring
  • Device specific customization
  • Zero-Click™ Integration
  • Reduced channel scoring
  • (Staging/Respiration/LM/Spindle/Artifact)*
  • *Exact start and end of events reported
  • Commercial and non-clinical use

List of Publications using/citing our technology

  • Review Articles

    • Goldstein, Cathy A., et al. "Artificial Intelligence in Sleep Medicine: An American Academy of Sleep Medicine Position Statement."
      Journal of Clinical Sleep Medicine (2020): jcsm-8288.
    • Goldstein, Cathy A., et al. "Artificial intelligence in sleep medicine: Background and implications for clinicians."
      Journal of Clinical Sleep Medicine (2020): jcsm-8388.
    • Fallmann, Sarah, and Liming Chen. "Computational Sleep Behavior Analysis: A Survey." IEEE Access 7 (2019): 142421-142440.
    • Fiorillo, Luigi, et al. "Automated sleep scoring: A review of the latest approaches." Sleep medicine reviews (2019).
    • Roy, Yannick, et al. "Deep learning-based electroencephalography analysis: a systematic review." Journal of neural engineering (2019).
    • Nakamura, Takashi, Harry J. Davies, and Danilo P. Mandic. "Scalable automatic sleep staging in the era of Big Data." 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2019.
    • Rosen, Ilene M. "Change is the only constant in life (and in sleep medicine)." Journal of Clinical Sleep Medicine 14.06 (2018): 1025-1030.

  • Neuro-Feedback

    • Cellini, Nicola, and Sara C. Mednick. "Stimulating the sleeping brain: current approaches to modulating memory-related sleep physiology." Journal of neuroscience methods 316 (2019): 125-136.
    • Ong, Ju Lynn, et al. "Auditory stimulation of sleep slow oscillations modulates subsequent memory encoding through altered hippocampal function." Sleep 41.5 (2018): zsy031.
    • Patanaik, Amiya, et al. "An end-to-end framework for real-time automatic sleep stage classification." Sleep 41.5 (2018): zsy041.

  • General

    • Lee, Xuan Kai, et al. "Validation of a consumer sleep wearable device with actigraphy and polysomnography in adolescents across sleep opportunity manipulations." Journal of Clinical Sleep Medicine 15.09 (2019): 1337-1346.
    • Cousins, James N., et al. "Does splitting sleep improve long-term memory in chronically sleep deprived adolescents?." npj Science of Learning 4.1 (2019): 1-11.
    • Cousins, James N., et al. "A split sleep schedule rescues short-term topographical memory after multiple nights of sleep restriction." Sleep 42.4 (2019): zsz018.

Download & Get Started

If you have already received your license key, download the latest version of z3score-client and watch the getting started video.

Current version of z3score-client is 2.3.0. Older versions: (2.2.0: Windows,2.1.1: Windows, 2.0.1: Windows), (2.0.0: Windows),(1.1.0: Windows, Mac-OS), (1.0.1: Windows)

Note: The technology has not been cleared or approved by the FDA and is only available for non-clinical use

Request a Demo

Please fill in the form to send us a request for demo. You can also contact directly via email: contact@neurobit.io

Corporate HQ
Neurobit Inc.
80 Broad St., 5th Floor
New York, USA

P: +65 9062 3184

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