Why the study?
Increasing morbidity and mortality of AF have driven interest in unobtrusive, long-term detection in routine life using cardiac dynamics signals such as BCG, SCG, and PPG.
This survey highlights the potential and challenges of using unobtrusive cardiac dynamics signals like BCG, SCG, and PPG for long-term atrial fibrillation monitoring.
Unobtrusive sensor-based AF detection remains hypothesis-generating; prospective validation required before clinical adoption.
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.
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Jiang et al. (2021) studied this question.