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AAN 2023 | Exploring the potential of deep learning in sleep studies for predictive insights

Emmanuel Mignot, MD, PhD, Stanford University School of Medicine, Palo Alto, CA, discusses the potential of deep learning in the field of sleep studies. Deep learning algorithms can provide a more consistent and reliable analysis of sleep stages, which could eventually replace human annotations. Moreover, Prof. Mignot discusses an ongoing project that involves collecting 250,000 sleep studies to explore the relationship between sleep abnormalities and conditions such as stroke, heart attack, and dementia. By analyzing the comprehensive data collected, including EEG, EOG, heart rate, and leg movements, researchers aim to identify potential predictors for these disorders. Prof. Mignot explains how sleep serves as a non-contaminated and convenient window into brain activity and overall body physiology. The ultimate goal is to develop a system that enables regular sleep recordings at home, with data being automatically analyzed to aid doctors in identifying future health risks. This interview took place at the American Academy of Neurology Annual Meeting 2023 in Boston, MA.

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Dr Mignot reports research/clinical trial support as a Stanford investigator or consulting fees from Takeda, Merck, Idorsia, Jazz, Centessa, Merck, Avadel, Apnea Co, Axome, Apple, Harmony Biosciences, and Sunovion.