Deepsleep records manual#

The workflow of DeepSleep is schematically illustrated in Fig. This approach ranked first in the 2018 “You Snooze, You Win” PhysioNet/Computing in Cardiology Challenge 16, in which computational methods were systematically evaluated for predicting non-apnea sleep arousals on a large held-out test dataset 17. Here we investigate these questions and describe a deep learning approach, DeepSleep, for automatic detection of sleep arousals. These pioneering studies have paved the way for us to answer several important questions with the development in machine learning technologies: Which types of algorithms and data processing methods are well suited for arousal detection? How does the length of context influence the prediction outcome (i.e., input length of polysomnography record)? Which types of physiological signals should be used? In particular, Fourier transform focusing on 30-second epochs has established one of the gold standard approaches in this field. Great progress has been made in developing computational methods for automatic arousal detection based on polysomnographic recordings 11, 12, 13, 14, 15. It takes hours to manually score such a large-scale sleep record. This is a laborious process, as the data is huge: an 8-h sleep record sampled at 200 Hz with 13 different physiological measurements contains a total of 75 million data points.

Deepsleep records manual#

Currently, sleep arousals are labeled through visual inspection of polysomnographic recordings according to the American Academy of Sleep Medicine (AASM) scoring manual 10. These arousals result from different types of potential stimuli, for example obstructive sleep apneas or hypopneas, snoring, or external noises. However, excessive arousals can lead to fragmented sleep or daytime sleepiness 2. Spontaneous sleep arousals, defined as brief intrusions of wakefulness into sleep 9, are a common characteristic of brain activity during sleep. It is estimated that around one-third of the general population in the United States are affected by insufficient sleep 8.

Inadequate sleep is often associated with negative outcomes, including obesity 2, irritability 2, 3, cardiovascular dysfunction 4, hypotension 5, impaired memory 6 and depression 7. Sleep is important for our health and quality of life 1.

This computational tool would greatly empower the scoring process in clinical settings and accelerate studies on the impact of arousals.

Our algorithm enables fast and accurate delineation of sleep arousal events at the speed of 10 seconds per sleep recording. We created an augmentation strategy by randomly swapping similar physiological channels, which notably improved the prediction accuracy. Leveraging a specific architecture that ‘translates’ input polysomnographic signals to sleep arousal labels, this algorithm ranked first in the “You Snooze, You Win” PhysioNet Challenge. Here we present a deep learning approach for automatically segmenting sleep arousal regions based on polysomnographic recordings. Currently, sleep arousals are mainly annotated by human experts through looking at 30-second epochs (recorded pages) manually, which requires considerable time and effort. Excessive sleep arousals are associated with symptoms such as sympathetic activation, non-restorative sleep, and daytime sleepiness. Sleep arousals are transient periods of wakefulness punctuated into sleep.