Source : Mouser Electronics
AI is transforming nearly every aspect of healthcare and assists in many applications. From reducing the time physicians take to diagnose diseases to autonomously managing the medical device supply chain, AI removes the lag humans need to realize, process, and react to changes in conditions. The technology processes data increasingly fast. Coupled with the IoT and increased connectivity, AI is constantly using data to assess—and improve—the quality of the application it serves.
An emerging application for AI has been assisting with sleep tracking, monitoring, and improvement. Optimizing sleep carries many benefits, such as higher productivity, a better immune system and heart health, and overall happiness. More, a lack of proper sleep not only causes grogginess, but can also result in the development of diabetes, cause weight gain and high blood pressure, weaken the immune system, and increase the risk of heart disease, among other issues. While pursuing the best rest possible is worth the effort, there is a significant technology advancement needed to deliver that result. To understand how technology can enhance sleep, we need to review the sleep stages and how the body uses rest to determine how AI can improve them.
Stages of Sleep
Sleep, as a bodily process, is rooted in neuroscience. Though seemingly akin to autopilot, sleep is an active brain process. This is significant in that any enhancement or change to the sleep cycle must not interfere with the brain’s active work to be effective. To know the best way to engage, scientists have used EEGs, MRIs, and other tools to map out discrete sleep states and create the baseline condition.
Sleep activates a series of neural networks in unique ways over four distinct phases:
Sleep stage 1: N1 (1-5 min)
Sleep stage 2: N2 (10-60 min)
Sleep stage 3: Slow-Wave Sleep (SWS, 20-40 min)
Sleep stage 4: Rapid Eye Movement (REM) Sleep (10-60 min)
Of these stages, Slow-Wave Sleep (SWS) lasts about 20-40 minutes per cycle and is the deepest stage of sleep. SWS is followed by Stage 4, Rapid Eye Movement (REM), a reasonably active sleep period that studies show may help with memory and performance. Together, these four stages last roughly 90 minutes. The subject experiences optimum benefits when they fully complete these cycles. In other words, benefits are not achieved when sleep processes are interrupted.
The cycle definitions also explain why it is essential to get 7-8 hours of sleep. At that duration, the body processes the complete sleep cycle five total times. The problem is when those five cycles are not optimized. Unaltered, the subject having problems with sleep is not getting the full benefit of this critical bodily process. Even without a whole night’s sleep, customizing the experience makes any amount of sleep they can get more beneficial. With that in mind, how does the technology work?
How Technology Enhances Sleep
Sensors are the starting point for any AI application. Measuring everything from heart rate to motion and sound, sensors collect and feed data back to the cloud-based processor, which uses AI to monitor the data over time. This data provides insights into trends and patterns in the subject’s sleep habits, eventually enabling the technology to predict the sleep cycle timing. Like any regression-based process, the more data the processor has to work with, the more accurate its prediction. Once the AI technology processes enough sleep data, engineers establish an individual sleep cycle for the subject.
Monitoring the Initial Sleep Condition
Once the algorithms identify a unique sleep cycle, the AI can work to optimize the sleep process in various ways. One of the first approaches is to time sleep cycles to avoid waking mid-cycle, especially during slow-wave sleep. Awakening during State 3 often makes the subject feel groggy and over-tired. Here is an example of how this would change the traditional method of waking up:
Imagine you need to wake up at 7 am. With a regular alarm clock, you set it for 7 am precisely. However, if a device tracked your sleep to wake you up at the optimum time during the cycle, it would wake you up after completing the last ~90 min cycle that ends before your designated wake-up time. As a result, even though you wake up earlier, you will likely feel more rested than you would sleeping longer because you woke post-cycle. While a conventional alarm clock would not know this, AI can ensure that it syncs the alarm function to your unique cycle.
Another way AI can enhance sleep is to identify factors that may be interrupting sleep during the night. These factors can exist as environmental or potential health factors, which engineers can discover by monitoring the heart and lungs. The most important outcome of AI-enhanced sleep is to ensure that the sleep environment is conducive to remaining asleep during the night, especially during active cycling. Some control levers AI can affect are maintaining light/dark cycles with blind/shade control. This approach leverages the body’s desire to sync to a Circadian cycle with smart [Circadian] lighting, helping the body feel alert during periods of light and tired when it is dark in the sleeping room.
Additional mechanisms to enhance sleep are temperature control and noise control. The existing data, taken over a sufficiently long time, can consider the body’s response to thunder or other noise events during sleep and alter conditions accordingly. Technology can even be applied in shared-bed situations to make “smart pillows” that connect to the shared devices to vibrate as a wake-up alarm for only one partner.
AI Can Aid in Sleep Disorder Diagnosis
In addition to helping subjects improve their sleep quality, AI can help physicians and medical professionals diagnose sleep disorders. Sleep disorders are an increasingly common challenge across the world and are linked to severe illness and death. One of the principal dangers of prolonged sleep deprivation is that it can lead to sleep disorders.
The objective of a sleep analysis is to collect and process enough information through sleep-staging to uncover trends that indicate a disorder. For example, the University of Copenhagen (Denmark) has developed a neural network called U-Sleep, a publicly available sleep-staging AI platform. Scientists collected baseline information through a study with 15,000 participants, which provided a comprehensive matrix of data they can use to compare with the signatures of known sleep disorders. For example, sleep experts can compare a pattern of oxygen level changes or breathing stoppages with sleep apnea, insomnia, and other illnesses to uncover a condition. Better yet, the predictive ability of AI can extrapolate a pattern observed in advance of a presenting symptom and discover an issue before it occurs.
AI is transforming the human experience, helping to provide benefits to improve the overall quality of life. For example, it can monitor initial sleep conditions, map and audit the sleep stages, and quietly customize the local environment in the sleep location to the subject’s preference. As a result, AI can enhance the sleeping experience while preserving the sleep cycle and diagnosing and predicting sleep disorders, improving attention, alertness, and health.
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