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Artificial intelligence predicts brain age from EEG signals recorded during sleep studies


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DARIEN, IL - A study shows that a deep neural network model can accurately predict the brain age of healthy patients based on electroencephalogram data recorded during an overnight sleep study, and EEG-predicted brain age indices display unique characteristics within populations with different diseases.
The study found that the model predicted age with a mean absolute error of only 4.6 years. There was a statistically significant relationship between the Absolute Brain Age Index and: epilepsy and seizure disorders, stroke, elevated markers of sleep-disordered breathing (i.e., apnea-hypopnea index and arousal index), and low sleep efficiency. The study also found that patients with diabetes, depression, severe excessive daytime sleepiness, hypertension, and/or memory and concentration problems showed, on average, an elevated Brain Age Index compared with the healthy population sample. ....

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Game time and direction of travel are associated with college football team performance


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DARIEN, IL - A study of NCAA Division I college football games found a significant association between the performance of away teams and both their direction of travel and the time of day when games were played.
Results show that away teams playing in the afternoon allowed 5% more points and forced 13% more opponent turnovers than those playing in the evening. Teams traveling eastward to play on the road threw 39% more interceptions than those traveling in the same time zone. There also was a significant interaction between direction of travel and time of day for points allowed, and a marginal interaction for points scored. ....

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