From sleep patterns to heart rhythm: Predicting atrial fibrillation from overnight polysomnograms.
Zuzana KoscovaAli Bahrami RadSamaneh NasiriMatthew A ReynaReza SameniLynn M TrottiHaoqi SunNiels TurleyKatie L StoneRobert J ThomasEmmanuel MignotBrandon WestoverGari D CliffordPublished in: Journal of electrocardiology (2024)
Our proposed ECG analysis method, utilizing overnight PSG data, shows promise in AF prediction despite modest precision, suggesting false positives. This approach could enable low-cost screening and proactive treatment for high-risk patients. Refinements, including additional physiological parameters, may reduce false positives, enhancing clinical utility and accuracy.
Keyphrases
- atrial fibrillation
- low cost
- heart failure
- end stage renal disease
- ejection fraction
- newly diagnosed
- heart rate
- big data
- catheter ablation
- left atrial appendage
- oral anticoagulants
- left atrial
- physical activity
- prognostic factors
- electronic health record
- sleep quality
- machine learning
- patient reported outcomes
- venous thromboembolism
- heart rate variability
- acute coronary syndrome