Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study

Abstract

Atrial fibrillation (AF) is a common arrhythmia affecting 8–10% of the population older than 80 years old. The importance of early diagnosis of atrial fibrillation has been broadly recognized since arrhythmias significantly increase the risk of stroke, heart failure and tachycardia-induced cardiomyopathy with reduced cardiac function. However, the prevalence of atrial fibrillation is often underestimated due to the high frequency of clinically silent atrial fibrillation as well as paroxysmal atrial fibrillation, both of which are hard to catch by routine physical examination or 12-lead electrocardiogram (ECG). The development of wearable devices has provided a reliable way for healthcare providers to uncover undiagnosed atrial fibrillation in the population, especially those most at risk. Furthermore, with the advancement of artificial intelligence and machine learning, the technology is now able to utilize the database in assisting detection of arrhythmias from the data collected by the devices. In this review study, we compare the different wearable devices available on the market and review the current advancement in artificial intelligence in diagnosing atrial fibrillation. We believe that with the aid of the progressive development of technologies, the diagnosis of atrial fibrillation shall be made more effectively and accurately in the near future.

Practical Exploration on the Construction of Theoretical Courses of Composition Technology in the Age of Artificial Intelligence

“…Previously, researchers viewed automatic composition as a sequence prediction problem. erefore, they simply and directly use one-hot encoding to represent the sequence of notes extracted from the music to train the generated music [15]. In order to solve this problem, this paper uses the word vector model in the field of natural language processing to optimize the representation of music features.…”

Improved One-year Outcomes After Active Cooling During Left Atrial Radiofrequency Ablation

“…The increased availability of monitors available for patient purchase and use (such as Kardia, Fitbit, the Apple Watch, etc), and the increased clinical use of wearable monitors, may bias against active esophageal cooling, since an increased incidence of arrhythmia is likely to be found with this increased monitoring. [25][26][27] With an increased identi cation of arrhythmias, both symptomatic and asymptomatic, over time, the rate of arrhythmia detection in patients treated after the adoption of active esophageal cooling device is likely to have increased, and as such, the effect size found in this analysis may in fact be underestimated. Likewise, a trend in increased age and comorbidity burden among patients undergoing AF ablation exists.…”

Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis

“…While there is still limited supporting evidence for systematic screening for AF, as well as associated cost implications [26], targeted screening, systemic opportunistic screening or smartphone algorithms, may be a more cost-effective option when using AI-enhanced ECG systems. With an increasing consumer adoption of wearable healthcare technologies [27,28], the incorporation of AIenhanced algorithms for AF screening [29][30][31][32] would be expected to AF-related morbidities in the long-term [33,34].…”