AI unlocks rhythms of 'deep sleep'

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Algorithms and deep learning has enabled Flinders University sleep researchers to dive deep into one of the mysteries of sleep health.

They have used and to develop a free online tool being used by sleep experts and researchers around the world to work out the role of the so-called K-complex, a prominent, brief up-down-up pattern of brain electro-encephalogram (EEG) electrical activity lasting around half a second during sleep.

When displayed on an EEG screen, it looks a bit like a 'K', says Bastien Lechat, lead author on a new Flinders University paper published in Sleep journal (pre-press).

"We hope this algorithm will help to fast forward new discoveries regarding the mysterious K-complex waveform and its associated health outcomes."

"A lack of K-complexes has been linked to various clinical problems, such as Alzheimer's disease and insomnia, suggesting that K-complexes are an important part of normal sleep and health."

"While the meaning and role of K-complexes is rather unclear, one of the leading theories is that they reflect low-level decision processing to either wake up or stay asleep in response to during sleep," says Mr Lechat, from the Adelaide Institute for Sleep Health at Flinders University.

K-complexes occur roughly every two minutes during sleep, so are too labour-intensive for routine sleep scoring.

If K-complexes were considered, it would take an expert sleep technician approximately 0.5 to 1.5 hours longer to score one sleep study.

Manual scoring also comes with a lot of variability, with agreement between expert scorers as low as 50%. The algorithm to automatically score K-complexes during overnight sleep studies is much faster and more reliable than with manual scoring.

The algorithm takes around 3 minutes to score an entire night of sleep and out-performs all currently available automated methods," says co-author Dr. Branko Zajamsek.

"In addition to its enhanced detection speed and accuracy, the also gives a 'confidence' or probability rating, allowing for more useful comparisons between clear versus ambiguous K-complex signals—as defined by human scoring.

"This makes the sleep scoring output comprehensive, yet very easy to understand compared to other automated methods."

More information: Bastien Lechat et al, Beyond K-complex binary scoring during sleep: probabilistic classification using deep learning, Sleep (2020). DOI: 10.1093/sleep/zsaa077

Journal information: Sleep

Citation: AI unlocks rhythms of 'deep sleep' (2020, May 18) retrieved 3 June 2023 from
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