The future of sleep health: a data-driven revolution in sleep science and medicine

Abstract

In recent years, there has been a significant expansion in the development and use of multi-modal sensors and technologies to monitor physical activity, sleep and circadian rhythms. These developments make accurate sleep monitoring at scale a possibility for the first time. Vast amounts of multi-sensor data are being generated with potential applications ranging from large-scale epidemiological research linking sleep patterns to disease, to wellness applications, including the sleep coaching of individuals with chronic conditions. However, in order to realise the full potential of these technologies for individuals, medicine and research, several significant challenges must be overcome. There are important outstanding questions regarding performance evaluation, as well as data storage, curation, processing, integration, modelling and interpretation. Here, we leverage expertise across neuroscience, clinical medicine, bioengineering, electrical engineering, epidemiology, computer science, mHealth and human-computer interaction to discuss the digitisation of sleep from a inter-disciplinary perspective. We introduce the state-of-the-art in sleep-monitoring technologies, and discuss the opportunities and challenges from data acquisition to the eventual application of insights in clinical and consumer settings. Further, we explore the strengths and limitations of current and emerging sensing methods with a particular focus on novel data-driven technologies, such as Artificial Intelligence.

Keywords: Biomedical engineering; Diagnostic markers; Predictive markers; Preventive medicine; Sleep.

Fig. 1. The digital sleep framework covers the path of sleep data from its acquisition to when its insights are used for medical or consumer applications.

The framework begins with the acquisition of sleep-related data. This can be done using a variety of sensors, ranging from polysomnography to bed sensors. This data is then stored and curated, a step that comprises privacy-aware storage, cleaning, filtering and anonymisation. Once that data has been appropriately treated, the processing step takes place whereby data is transformed and integrated based on the end-model. For example it may undergo different transformations like normalization or featurization. The next step entails modeling, which can consist of simple heuristic methods, statistical learning or deep learning methods, for example. Finally, the resulting model can be deployed for a variety either medical or consumer applications.

Fig. 2. Emerging sleep-sensing technologies.

Emerging sleep technologies range from non-contact methods like RF sensors to miniaturized, wireless or in-ear EEGs.

Fig. 3. Selected methods for the measurement of sleep and their accuracy and usability trade-off.

This chart plots the accuracy of sleep-sensing methods at infering sleep-related metrics against their ease of use. For example, while polysomnography is considered the “gold-standard” technique to measure sleep, it is cumbersome and expensive.

Fig. 4. Holistic evaluation of sleep-monitoring methods.

Some methods, such as PSG, are accurate but inappropriate for use in daily sleep monitoring, as they require professional set up and are intrusive. Other methods, such as bed sensors, are unobtrusive but more prone to noise than PSG.

Fig. 5. Overview of cloud computing-based sleep data acquisition and storage.

This illustration provides an overview of the process starting with device layer (which includes fast, real-time processing and data visualisation, embedded systems, gateways and micro data storage), followed by the fog layer (which includes local networks, virtualisation, data analysis and reduction) and finally cloud layer (which consists of data centres and big data storage and processing).

Fig. 6. Sleep classification algorithms can be based on heuristic approaches or Artificial Intelligence.

We describe machine learning/statistical learning approaches and deep-learning approaches within AI.

Fig. 7. Key areas of impact for sleep health.

Emerging sleep health technologies will have an impact on patient monitoring, clinical care, insurance, the pharmaceutical industry and health and wellness applications, as well as other impacts including on digital therapeutics and sports performance.