Machine learning identification of sleep EEG and EOG biomarkers for mortality risk.

IF 5.6 2区医学 Q1 Medicine

Sleep Pub Date : 2024-09-30 DOI:10.1093/sleep/zsae231

Wolfgang Ganglberger

引用次数: 0

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通过机器学习识别睡眠脑电图和脑电图生物标志物，确定死亡风险。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Sleep Medicine-Neurology (clinical)

CiteScore

8.70

自引率

10.70%

发文量

期刊介绍： SLEEP® publishes findings from studies conducted at any level of analysis, including: Genes Molecules Cells Physiology Neural systems and circuits Behavior and cognition Self-report SLEEP® publishes articles that use a wide variety of scientific approaches and address a broad range of topics. These may include, but are not limited to: Basic and neuroscience studies of sleep and circadian mechanisms In vitro and animal models of sleep, circadian rhythms, and human disorders Pre-clinical human investigations, including the measurement and manipulation of sleep and circadian rhythms Studies in clinical or population samples. These may address factors influencing sleep and circadian rhythms (e.g., development and aging, and social and environmental influences) and relationships between sleep, circadian rhythms, health, and disease Clinical trials, epidemiology studies, implementation, and dissemination research.