Evidence record 15406 · automatically gathered

Accuracy of Machine Learning Algorithms Based on Electroencephalogram in Sleep Apnea Detection: Systematic Review and Meta-Analysis

Background: Sleep apnea (SA) is a serious sleep disorder, and its diagnostic gold standard, polysomnography, is costly and time-consuming. Electroencephalogram (EEG) signals, due to their direct correlation with neural activity and ease of extraction, represent a promising tool. Despite increasing research on machine learning (ML) and deep learning for EEG-based SA detection, model performance has not been consistently evaluated. Objective: This systematic review evaluated the accuracy of ML in

Record details

Published: 31 July 2026
Source: JMIR (Journal of Medical Internet Research)
Category: Research
Topics: Healthcare
Retrieved: 1 August 2026

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ethics.ai (31 July 2026), “Accuracy of Machine Learning Algorithms Based on Electroencephalogram in Sleep Apnea Detection: Systematic Review and Meta-Analysis,” evidence record 15406, https://ethics.ai/record/15406 (originally published by JMIR (Journal of Medical Internet Research)).

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