English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Electroencephalogram (EEG) provides clinically relevant information for personalized patient health evaluation and comprehensive assessment of sleep. EEG-based indices have been associated with neurodegenerative conditions, psychiatric disorders, and metabolic and cardiovascular disease, and hold promise as a biomarker for brain health. Methods A deep neural network (DNN) model was trained to predict the age of patients using raw EEG signals recorded during clinical polysomnography (PSG). The DNN was trained on N=126,241 PSGs, validated on N=6,638, and tested on a holdout set of N=1,172. The holdout dataset included several categories of patient demographic and diagnostic parameters, allowing us to examine the association between brain age and a variety of medical conditions. Brain age was assessed by subtracting the individual’s chronological brain age from their EEG-predicted brain age (Brain Age Index; BAI), and then taking the absolute value of this variable (Absolute Brain Age Index; ABAI). We then constructed two regression models to test the relationship between BAI/ABAI and the following list of patient parameters: sex, BMI, depression, alcohol/drug problems, memory/concentration problems, epilepsy/seizures, diabetes, stroke, severe excessive daytime sleepiness (e.g., Epworth Sleepiness Scale ≥ 16; EDS), apnea-hypopnea index (AHI), arousal index (ArI), and sleep efficiency (SE). Results The DNN brain age model produced a mean absolute error of 4.604 and a Pearson’s r value of 0.933 which surpass the performance of prior research. In our regression analyses, we found a statistically significant relationship between the ABAI and: epilepsy and seizure disorders, stroke, elevated AHI, elevated ArI, and low SE (all p&amp;lt;0.05). This demonstrates these health conditions are associated with deviations of one’s predicted brain age from their chronological brain age. We also found patients with diabetes, depression, severe EDS, hypertension, and/or memory and concentration problems showed, on average, an elevated BAI compared to the healthy population sample (all p&amp;lt;0.05). Conclusion We show DNNs can accurately predict the brain age of healthy patients based on their raw, PSG derived, EEG recordings. Furthermore, we reveal indices, such as BAI and ABAI, display unique characteristics within different diseased populations, highlighting their potential value as novel diagnostic biomarker and potential “vital sign” of brain health. Support (if any):

543 EEG-Based Deep Neural Network Model for Brain Age Prediction and Its Association with Patient Health Conditions