Machine learning detection of Atrial Fibrillation using wearable technology.
Mark LownMichael BrownChloë BrownArthur M YueBenoy N ShahSimon J CorbettGeorge LewithBeth StuartMichael MoorePaul LittlePublished in: PloS one (2020)
An inexpensive wearable heart rate monitor and machine learning algorithm can be used to detect AF with very high accuracy and has the capability to transmit ECG data which could be used to confirm AF. It could potentially be used for intermittent screening or continuously for prolonged periods to detect paroxysmal AF. Further work could lead to cost-effective and accurate estimation of AF burden and improved risk stratification in AF.
Keyphrases
- atrial fibrillation
- heart rate
- machine learning
- heart rate variability
- catheter ablation
- oral anticoagulants
- left atrial
- left atrial appendage
- blood pressure
- direct oral anticoagulants
- big data
- heart failure
- artificial intelligence
- percutaneous coronary intervention
- deep learning
- acute coronary syndrome
- risk factors
- electronic health record
- coronary artery disease
- high intensity
- venous thromboembolism