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

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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.

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Long Short-Term Memory

Jürgen Schmidhuber, Jürgen Schmidhuber (2002)

Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.

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High-performance medicine: the convergence of human and artificial intelligence

Eric Topol (2019)

The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.

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Physical activity in the United States measured by accelerometer.

Richard Troiano, David Berrigan, Kevin Dodd … (2008)

To describe physical activity levels of children (6-11 yr), adolescents (12-19 yr), and adults (20+ yr), using objective data obtained with accelerometers from a representative sample of the U.S. population. These results were obtained from the 2003-2004 National Health and Nutritional Examination Survey (NHANES), a cross-sectional study of a complex, multistage probability sample of the civilian, noninstitutionalized U.S. population in the United States. Data are described from 6329 participants who provided at least 1 d of accelerometer data and from 4867 participants who provided four or more days of accelerometer data. Males are more physically active than females. Physical activity declines dramatically across age groups between childhood and adolescence and continues to decline with age. For example, 42% of children ages 6-11 yr obtain the recommended 60 min x d(-1) of physical activity, whereas only 8% of adolescents achieve this goal. Among adults, adherence to the recommendation to obtain 30 min x d(-1) of physical activity is less than 5%. Objective and subjective measures of physical activity give qualitatively similar results regarding gender and age patterns of activity. However, adherence to physical activity recommendations according to accelerometer-measured activity is substantially lower than according to self-report. Great care must be taken when interpreting self-reported physical activity in clinical practice, public health program design and evaluation, and epidemiological research.