Using PPG Signals and Wearable Devices for Atrial Fibrillation Screening

Abstract

Cardiovascular diseases are the primary cause of deaths in the world. Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. Due to its high prevalence and associated risks, early detection of AF is an important objective for healthcare systems worldwide. The growing demand for medical assistance implies increased expenses, which could be limited by implementing ambulatory monitoring techniques based on wearable devices, thus, reducing the number of people requiring observation in hospitals. One of the main challenges in this context is related to the large amount of data from patients to be analyzed, which points to the suitability of using computational intelligence techniques for it. The selection of the features to be extracted from data plays a key role in order for any classifier of heart rhythm to provide good results in this regard. This paper demonstrates that it is possible to achieve an accurate detection of AF using a very low number of relatively simple features extracted from photoplethysmographic signals, enabling the use of affordable wearable devices (with scarce processing and data storage resources) with this purpose over long periods of time. This fact has been validated in experiments using data from real patients under medical supervision.