A Review of Atrial Fibrillation Detection Methods as a Service

Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals

Detection of atrial fibrillation episodes in long-term heart rhythm signals using a support vector machine

Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.Web of Science203art. no. 76

The burden of proof: the current state of atrial fibrillation prevention and treatment trials

Atrial fibrillation (AF) is an age-related arrhythmia of enormous socioeconomic significance. In recent years, our understanding of the basic mechanisms that initiate and perpetuate AF has evolved rapidly, catheter ablation of AF has progressed from concept to reality, and recent studies suggest lifestyle modification may help prevent AF recurrence. Emerging developments in genetics, imaging, and informatics also present new opportunities for personalized care. However, considerable challenges remain. These include a paucity of studies examining AF prevention, modest efficacy of existing antiarrhythmic therapies, diverse ablation technologies and practice, and limited evidence to guide management of high-risk patients with multiple comorbidities. Studies examining the long-term effects of AF catheter ablation on morbidity and mortality outcomes are not yet completed. In many ways, further progress in the field is heavily contingent on the feasibility, capacity, and efficiency of clinical trials to incorporate the rapidly evolving knowledge base and to provide substantive evidence for novel AF therapeutic strategies. This review outlines the current state of AF prevention and treatment trials, including the foreseeable challenges, as discussed by a unique forum of clinical trialists, scientists, and regulatory representatives in a session endorsed by the Heart Rhythm Society at the 12th Global CardioVascular Clinical Trialists Forum in Washington, DC, December 3–5, 2015