Clinical Grade Sleep Tracking using Z3Score-HRV API

Clinical Grade Sleep Tracking using Z3Score-HRV API

Consumer sleep trackers are woefully inaccurate and lab-based sleep measurement is prohibitively expensive and ridiculously uncomfortable. A key barrier to progress is the tech to extract high-quality sleep information from heart rate.

It has been 7 weeks since the announcement of the circuit-breaker by the Singapore government. While offices remain closed, we at Neurobit have been working tirelessly (at home), pushing the boundaries of AI in the pursuit of better sleep for everyone. We are proud to announce the release of Z3Score-HRV API. A set of tools to extract very high-quality sleep information from heart rate data that may come from an ECG chest belt, a wearable device, or a medical device with just two lines of code. Before we go into the details, let us see why this has far-reaching consequences.

As sleep clinics across the world remain closed and patients avoid Hospitals even in emergencies, the importance of remote health monitoring cannot be overstated. For a long time, consumers have tried to take their health into their own hands with wearable and fitness trackers. But when it comes to sleep, it has been a story of disappointments. As Dr. Robert S Rosenberg, a board-certified sleep physician and author of _The Doctor’s Guide to Sleep Solutions for Stress & Anxiety _puts it about current sleep trackers:

Their algorithms assume with motion you are awake and without it you are asleep. They tend to overestimate total sleep time. They are very poor at detecting true awakening during the night. They purport to be able to tell if you are in a light or deep sleep. — Dr. Robert S Rosenberg (source)

Sleep is measured from your brain activity, so EEG (brain waves) remains the gold standard of sleep measurement. Multiple scientific studies involving simultaneous measurement from EEG and sleep trackers support Dr. Rosenberg’s view. But what if there was a way to get a glimpse of your mind without actually measuring EEG?

Introducing Heart Rate (Variability)

Everyone should be familiar with the heart rate. Thanks to the rapid rise in interest in fitness trackers, commoditization of hardware and R&D into completely new ways of measuring bio-signals, heart rate measurement is slowly becoming integral to most trackers. Much more interesting than heart rate is heart rate variability or HRV. In simple terms, it is the fluctuations in your heart rate which is controlled by your autonomic nervous system (ANS). The ANS can be further split into the parasympathetic nervous system (PNS) and the sympathetic nervous system (SNS). The PNS is referred to as “rest and digest” while the SNS is referred to as “fight or flight” and reflects responds to stress and exercise. The HRV reflects the intricate dance between these two systems and truly is a gateway to your mind.

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Lower HRV is associated with both higher mortality and morbidity. It’s an excellent proxy for your fitness levels and biological age. The importance of HRV in the fitness industry is well recognized. The reason why this is relevant for sleep is because HRV might be a cost-effective and scalable way to measure sleep with very high accuracy. So what is the issue?

Measuring sleep from HRV is a challenging problem

There is an increasing trend of consumer companies trying to come towards health-care while health-care companies are trying to be more consumer-friendly. While you can get away with showing pretty but inaccurate graphs to consumers, to bridge this gap between consumer tech and clinical grade, high accuracy and reliability are a must.

The hardware gap between consumer devices and clinical devices are slowly fading away. Soon what will differentiate the two will be software — the ability to extract real value from the same measurements.

The relationship between HRV and sleep is intricate and a major data science problem. Despite combining HRV and motion, the accuracy of sleep-tracking remains poor. Solving this requires deep domain expertise, a thorough understanding of AI and machine learning technologies, and access to a massive amount of simultaneous EEG/HRV data.

This is where Neurobit’s razor-sharp focus and experience in developing state of the art sleep analytics technologies come into play. Z3Score-HRV is part of a suite of sleep analytics technologies under the Z3Score® umbrella. We have spent 6 years of R&D refining our technologies and working with our customers and partners to push the boundaries of what is possible. Let’s now move to the details of the tech.

sleep data-science machine-learning wearables api

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