Abstract
Atrial fibrillation (AF) is a cardiac rhythm disorder associated with increased morbidity and mortality. It is the leading risk factor for cardioembolic stroke and its early detection is crucial in both primary and secondary stroke prevention. Continuous monitoring of cardiac rhythm is today possible thanks to consumer-grade wearable devices, enabling transformative diagnostic and patient management tools. Such monitoring is possible using low-cost easy-to-implement optical sensors that today equip the majority of wearables. These sensors record blood volume variations—a technology known as photoplethysmography (PPG)—from which the heart rate and other physiological parameters can be extracted to inform about user activity, fitness, sleep, and health. Recently, new wearable devices were introduced as being capable of AF detection, evidenced by large prospective trials in some cases. Such devices would allow for early screening of AF and initiation of therapy to prevent stroke. This review is a summary of a body of work on AF detection using PPG. A thorough account of the signal processing, machine learning, and deep learning approaches used in these studies is presented, followed by a discussion of their limitations and challenges towards clinical applications.
Highlights
Atrial fibrillation (AF) is an abnormal cardiac rhythm characterized by a disorganized atrial activity
Respiratory rate is one of the fundamental vital signs and can be determined from the time–frequency representation of a PPG signal.[23]. Some hemodynamic parameters such as augmentation index (AIx) and pulse wave velocity (PWV) are important biomarkers of arterial stiffness, which is a direct cause of hypertension and a major risk factor for cardiovascular events such as myocardial infarction and stroke. Both AIx and PWV could be derived from PPG,[24,25] Subendocardial Viability Ratio (SEVR %) and Ejection Time Index (ETI) are two hemodynamic parameters used in the evaluation of cardiac workload that can be estimated with PPG analysis.[25]
A review of statistical and machine learning approaches applied to AF detection using PPG is presented
Summary
Atrial fibrillation (AF) is an abnormal cardiac rhythm characterized by a disorganized atrial activity. Respiratory rate is one of the fundamental vital signs and can be determined from the time–frequency representation of a PPG signal.[23] Some hemodynamic parameters such as augmentation index (AIx) and pulse wave velocity (PWV) are important biomarkers of arterial stiffness, which is a direct cause of hypertension and a major risk factor for cardiovascular events such as myocardial infarction and stroke. Both AIx and PWV could be derived from PPG,[24,25] Subendocardial Viability Ratio (SEVR %) and Ejection Time Index (ETI) are two hemodynamic parameters used in the evaluation of cardiac workload that can be estimated with PPG analysis.[25]. 74 prior and after – cardioversion + Public databases (MIT-BIH AF + MITBIH NSR + MIT-BIH Arrhythmia Database)
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