Key result
Photoplethysmographic signal analysis using a low number of simple features enabled accurate detection of atrial fibrillation, facilitating the use of affordable wearable devices for screening.
Why the study?
Early detection of AF using wearable devices is needed to reduce healthcare costs, but analyzing large volumes of data requires selecting simple features suitable for resource-constrained devices.
Does an algorithm using simple features from photoplethysmographic signals accurately detect atrial fibrillation in patients?
Does an algorithm using simple features from photoplethysmographic signals accurately detect atrial fibrillation in patients?
Accurate detection of atrial fibrillation can be achieved using simple features from photoplethysmographic signals, enabling long-term ambulatory monitoring with affordable wearable devices.
Answers are grounded in this paper and cite their sources.
Supports wearable AF screening feasibility; leaves open prospective clinical validation before adoption.
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.
No takes yet. Share an insight, caveat, or question.
Yang et al. (2019) studied Atrial fibrillation. Photoplethysmographic (PPG) signal analysis was evaluated on Detection of atrial fibrillation. Photoplethysmographic signal analysis using a low number of simple features enabled accurate detection of atrial fibrillation, facilitating the use of affordable wearable devices for screening.
Synapse has enriched 10 related papers. Consider them for comparative context: