Recent Research for Unobtrusive Atrial Fibrillation Detection Methods Based on Cardiac Dynamics Signals: A Survey.
Fangfang JiangYihan ZhouTianyi LingYanbing ZhangZiyu ZhuPublished in: Sensors (Basel, Switzerland) (2021)
Atrial fibrillation (AF) is the most common cardiac arrhythmia. It tends to cause multiple cardiac conditions, such as cerebral artery blockage, stroke, and heart failure. The morbidity and mortality of AF have been progressively increasing over the past few decades, which has raised widespread concern about unobtrusive AF detection in routine life. The up-to-date non-invasive AF detection methods include electrocardiogram (ECG) signals and cardiac dynamics signals, such as the ballistocardiogram (BCG) signal, the seismocardiogram (SCG) signal and the photoplethysmogram (PPG) signal. Cardiac dynamics signals can be collected by cushions, mattresses, fabrics, or even cameras, which is more suitable for long-term monitoring. Therefore, methods for AF detection by cardiac dynamics signals bring about extensive attention for recent research. This paper reviews the current unobtrusive AF detection methods based on the three cardiac dynamics signals, summarized as data acquisition and preprocessing, feature extraction and selection, classification and diagnosis. In addition, the drawbacks and limitations of the existing methods are analyzed, and the challenges in future work are discussed.
Keyphrases
- atrial fibrillation
- left ventricular
- heart failure
- catheter ablation
- left atrial
- oral anticoagulants
- loop mediated isothermal amplification
- left atrial appendage
- label free
- direct oral anticoagulants
- machine learning
- real time pcr
- percutaneous coronary intervention
- coronary artery disease
- randomized controlled trial
- working memory
- venous thromboembolism
- heart rate