Evidence map›Paper›PMID 39301948›Full record

ArticleSleep2025

Mortality risk assessment using deep learning-based frequency analysis of electroencephalography and electrooculography in sleep.

Teitur Óli Kristjánsson, Katie L Stone, Helge B D Sorensen, Andreas Brink-Kjaer, Emmanuel Mignot, Poul Jennum

Abstract read
In one paragraph

Article in Sleep, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Teitur Óli KristjánssonDepartment of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.ORCID 0000-0002-0685-6809
Katie L StoneResearch Institute, California Pacific Medical Center, San Francisco, CA, USA.
Helge B D SorensenDepartment of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.
Andreas Brink-KjaerDepartment of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.ORCID 0000-0002-2872-0929
Emmanuel MignotPsychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, USA.ORCID 0000-0002-6928-5310
Poul JennumDepartment of Clinical Neurophysiology, Rigshospitalet, Copenhagen, Denmark.

Funding

Oregon Clinical and Translational Research Institute - The National COVID Cohort Collaborative (N3C)UL1TR002369 · NCATS · OREGON HEALTH & SCIENCE UNIVERSITY · PI Cynthia D Morris, Christopher G. Slatore · 2017 to 2026
$78.4M
Long term fracture risk and change in peripheral bone in the oldest old men: The MrOS studyR01AG066671 · NIA · CALIFORNIA PACIFIC MED CTR RES INSTITUTE · PI BOUXSEIN, MARY L, CAWTHON, PEGGY MANNEN · 2020 to 2024
$13.3M
Outcomes of Sleep Disorders in Older MenR01HL071194 · NHLBI · UNIVERSITY OF CALIFORNIA SAN FRANCISCO · PI STONE, KATIE L · 2003 to 2013
$12.2M
Osteoporotic Fractures in Men (MrOS) - San Francisco Coordinating CenterU01AR066160 · NIAMS · CALIFORNIA PACIFIC MED CTR RES INSTITUTE · PI CUMMINGS, STEVEN RON · 2013 to 2017
$9.3M
National Sleep Research Resource (NSRR)R24HL114473 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI REDLINE, SUSAN S., ZHANG, GUO-QIANG · 2013 to 2017
$7.5M
Osteoporotic Fractures in Men (MrOS) - MinneapolisU01AG042145 · NIA · UNIVERSITY OF MINNESOTA · PI ENSRUD, KRISTINE · 2013 to 2019
$5.3M
Osteporotic Fractures in Men (MrOS)- Admin CenterU01AG027810 · NIA · OREGON HEALTH & SCIENCE UNIVERSITY · PI ORWOLL, ERIC S. · 2006 to 2017
$5.1M
DATA COORDINATING CENTER FOR SLEEP HEART STUDYU01HL064360 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI DIENER-WEST, MARIE · 1999 to 2007
$4.4M
Osteoporotic Fractures in Men (MrOS) - PortlandU01AG042124 · NIA · OREGON HEALTH & SCIENCE UNIVERSITY · PI ORWOLL, ERIC S. · 2013 to 2019
$3.0M
Osteoporatic Fractures in Men (MrOS)U01AG042168 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI KADO, DEBORAH · 2013 to 2017
$3.0M
Osteoporotic Fractures in Men (Mr. Os) Palo AltoU01AG042143 · NIA · STANFORD UNIVERSITY · PI STEFANICK, MARCIA L. · 2013 to 2017
$3.0M
Osteoporotic Fractures in Men (Mr.OS)U01AG042139 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI CAULEY, JANE ANN · 2013 to 2017
$2.9M
NCATS NIH HHS UL1 TR002369NHLBI NIH HHS R01 HL070837NHLBI NIH HHS R01 HL070838NHLBI NIH HHS R01 HL070839NHLBI NIH HHS R01 HL070841NHLBI NIH HHS R01 HL070842NHLBI NIH HHS R01 HL070847NHLBI NIH HHS R01 HL070848NHLBI NIH HHS R01 HL071194NHLBI NIH HHS R24 HL114473NHLBI NIH HHS U01 HL053916NHLBI NIH HHS U01 HL053931NHLBI NIH HHS U01 HL053934NHLBI NIH HHS U01 HL053937NHLBI NIH HHS U01 HL053938NHLBI NIH HHS U01 HL053941NHLBI NIH HHS U01 HL063463NHLBI NIH HHS U01 HL064360NIAMS NIH HHS U01 AR066160NIA NIH HHS R01 AG066671NIA NIH HHS U01 AG027810NIA NIH HHS U01 AG042124NIA NIH HHS U01 AG042139NIA NIH HHS U01 AG042140NIA NIH HHS U01 AG042143NIA NIH HHS U01 AG042145NIA NIH HHS U01 AG042168
6 · The paper itself

Abstract

STUDY

objectivesTo assess whether the frequency content of electroencephalography (EEG) and electrooculography (EOG) during nocturnal polysomnography (PSG) can predict all-cause mortality.

methodsPower spectra from PSGs of 8716 participants, including from the MrOS Sleep Study and the Sleep Heart Health Study, were analyzed in deep learning-based survival models. The best-performing model was further examined using SHapley Additive Explanation (SHAP) for data-driven sleep-stage specific definitions of power bands, which were evaluated in predicting mortality using Cox Proportional Hazards models.

resultsSurvival analyses, adjusted for known covariates, identified multiple EEG frequency bands across all sleep stages predicting all-cause mortality. For EEG, we found an all-cause mortality hazard ratio (HR) of 0.90 (CI: 95% 0.85 to 0.96) for 12-15 Hz in N2, 0.86 (CI: 95% 0.82 to 0.91) for 0.75-1.5 Hz in N3, and 0.87 (CI: 95% 0.83 to 0.92) for 14.75-33.5 Hz in rapid-eye-movement sleep. For EOG, we found several low-frequency effects including an all-cause mortality HR of 1.19 (CI: 95% 1.11 to 1.28) for 0.25 Hz in N3, 1.11 (CI: 95% 1.03 to 1.21) for 0.75 Hz in N1, and 1.11 (CI: 95% 1.03 to 1.20) for 1.25-1.75 Hz in wake. The gain in the concordance index (C-index) for all-cause mortality is minimal, with only a 0.24% increase: The best single mortality predictor was EEG N3 (0-0.5 Hz) with a C-index of 77.78% compared to 77.54% for confounders alone.

conclusionsSpectral power features, possibly reflecting abnormal sleep microstructure, are associated with mortality risk. These findings add to a growing literature suggesting that sleep contains incipient predictors of health and mortality.

Indexed as

Deep LearningElectroencephalographyElectrooculographyMortalityAdultAgedFemaleHumansMaleMiddle AgedPolysomnographyRisk AssessmentSleep StagesCox proportional hazards modelsEEG analysisEEG spectral analysisEOG analysisEOG spectral analysismachine learningmortalitynonlinear analysissleep stagesstatistics

Identifiers

PMID39301948
PMCPMC12554981

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.