Machine Learning for Detecting Atrial Fibrillation from ECGs: Systematic Review and Meta-Analysis.
Chenggong XieZhao WangChenglong YangJianhe LiuHao LiangPublished in: Reviews in cardiovascular medicine (2024)
ML algorithms are effective for detecting AF from ECGs. DL algorithms, particularly those based on convolutional neural networks (CNN), demonstrate superior performance in AF detection compared to TML algorithms. The integration of ML algorithms can help wearable devices diagnose AF earlier.
Keyphrases
- machine learning
- atrial fibrillation
- convolutional neural network
- deep learning
- artificial intelligence
- big data
- left atrial
- oral anticoagulants
- catheter ablation
- direct oral anticoagulants
- heart failure
- left atrial appendage
- percutaneous coronary intervention
- acute coronary syndrome
- label free
- sensitive detection
- mitral valve