{
  "id": 15406,
  "url": "https://www.jmir.org/2026/1/e93378",
  "title": "Accuracy of Machine Learning Algorithms Based on Electroencephalogram in Sleep Apnea Detection: Systematic Review and Meta-Analysis",
  "summary": "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",
  "authors": "Xiangshuo Li, Lulu Wang, Ting Tang, Yuanyuan Chen, Lan Yang, Hao Cai, Chen Wang, Shuxiao Zhang, Ning Ding, Kouying Liu",
  "category": "research",
  "topics": "healthcare",
  "orgs": "meta",
  "regions": null,
  "published_at": "2026-07-31T20:00:04.000Z",
  "fetched_at": "2026-08-01T05:10:57.676Z",
  "source_slug": "x-jmir-journal-of-medical-internet-researc",
  "source_name": "JMIR (Journal of Medical Internet Research)",
  "source_homepage": "https://www.jmir.org",
  "ethics_ai_record_url": "https://ethics.ai/record/15406",
  "original_url": "https://www.jmir.org/2026/1/e93378",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}