Abstract
Accurately measuring sleep and its quality with polysomnography (PSG) is an expensive task. Actigraphy, an alternative, has been proven cheap and relatively accurate. However, the largest experiments conducted to date, have had only hundreds of participants. In this work, we processed the data of the recently published Multi-Ethnic Study of Atherosclerosis (MESA) Sleep study to have both PSG and actigraphy data synchronized. We propose the adoption of this publicly available large dataset, which is at least one order of magnitude larger than any other dataset, to systematically compare existing methods for the detection of sleep-wake stages, thus fostering the creation of new algorithms. We also implemented and compared state-of-the-art methods to score sleep-wake stages, which range from the widely used traditional algorithms to recent machine learning approaches. We identified among the traditional algorithms, two approaches that perform better than the algorithm implemented by the actigraphy device used in the MESA Sleep experiments. The performance, in regards to accuracy and F1 score of the machine learning algorithms, was also superior to the device’s native algorithm and comparable to human annotation. Future research in developing new sleep-wake scoring algorithms, in particular, machine learning approaches, will be highly facilitated by the cohort used here. We exemplify this potential by showing that two particular deep-learning architectures, CNN and LSTM, among the many recently created, can achieve accuracy scores significantly higher than other methods for the same tasks.
Highlights
Short and poor quality sleep have been directly linked to a series of chronic health problems, including obesity, insulin resistance, and hypertension.[1,2,3,4] measuring sleep and its quality are increasingly important beyond the diagnosis of specific sleep disorders.While polysomnography (PSG) is the gold standard approach for diagnosing specific sleep disorders, it is impractical for use in the identification of more prevalent issues with sleep loss and sleep quality
An attractive alternative to PSG is the use of wearables, such as accelerometer-based technology (Actigraphy), which may be used as a diagnostic aid for specific sleep disorders such as circadian rhythm disorders
While the signals captured by an actigraphy device are not as detailed as the ones obtained by PSG, it allows the identification of sleep-wake states, sleep timing, and sleep quality.[5]
Summary
While polysomnography (PSG) is the gold standard approach for diagnosing specific sleep disorders, it is impractical for use in the identification of more prevalent issues with sleep loss and sleep quality. An attractive alternative to PSG is the use of wearables, such as accelerometer-based technology (Actigraphy), which may be used as a diagnostic aid for specific sleep disorders such as circadian rhythm disorders. Actigraphy devices allow several weeks of unobtrusive, continuous recording, enabling prospective, and naturalistic assessment of sleep.[5] While the signals captured by an actigraphy device are not as detailed as the ones obtained by PSG, it allows the identification of sleep-wake states, sleep timing, and sleep quality.[5]
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