Machine learning detection of Atrial Fibrillation using wearable technology

Abstract

BackgroundAtrial Fibrillation is the most common arrhythmia worldwide with a global age adjusted prevalence of 0.5% in 2010. Anticoagulation treatment using warfarin or direct oral anticoagulants is effective in reducing the risk of AF-related stroke by approximately two-thirds and can provide a 10% reduction in overall mortality. There has been increased interest in detecting AF due to its increased incidence and the possibility to prevent AF-related strokes. Inexpensive consumer devices which measure the ECG may have the potential to accurately detect AF but do not generally incorporate diagnostic algorithms. Machine learning algorithms have the potential to improve patient outcomes particularly where diagnoses are made from large volumes or complex patterns of data such as in AF. OPEN ACCESS Citation: Lown M, Brown M, Brown C, Yue AM, Shah BN, Corbett SJ, et al. (2020) Machine learning detection of Atrial Fibrillation using wearable technology. PLoS ONE 15(1): e0227401.

Single-Feature Method for Fast Atrial Fibrillation Detection in ECG Signals

“…We achieved sensitivity Se=96.32% and specificity Sp=98.61% on MIT-BIH Atrial Fibrillation database, Se=91.30% and Sp=90.80% on MIT-BIH Arrhythmia database, Se=95.6% and Sp=80.27% for Long Term Atrial Fibrillation database and Se=93.04% and Sp=87.30% for CinC Challenge database 2020. The achieved results of our one-feature method are comparable with other authors of more complicated and computationally expensive methods [9], [10]. Fast a simplicity of our method is useful for example in application where the time is limitation and also for mobile devices, where the low computational complexity is important.…”

“…In this work, highly efficient single feature algorithm for atrial fibrillation is proposed. The achieved results of our one-feature method are comparable with other authors of more complicated and computationally expensive methods [9], [10]. Fast a simplicity of our method is useful for example in application where the time is limitation and also for mobile devices, where the low computational complexity is important.…”

Exploration of physiological sensors, features, and machine learning models for pain intensity estimation

“…They extracted five typological network features from quantile graphs of EEG signal and presented them to SVM classifier to detect the disease using data collected from 24 healthy and 24 patient subjects. It has also been demonstrated that ECG-based SVM model have the potential to accurately detect Atrial Fibrillation arrhythmia using wearable technology to prevent AF-related stroke [ 47 ]. Moreover, studies have also proven that Biosignal-based Machine learning models are useful for automated sleep monitoring and sleep stage classification [ 48 , 49 ].…”

Using Machine Learning to Characterize Atrial Fibrotic Substrate from Intracardiac Signals using a Hybrid in silico and in vivo Dataset

“…With the increasing amount of data available, the use of machine learning for the interpretation of cardiac signals is steadily increasing. Machine learning has been extensively used in electrocardiogram analysis due to its potential to analyze big datasets and uncover mechanistic information about the cardiac electrical function [19][20][21] . Several studies aimed at quantifying AF mechanisms and automatically localize reentrant drivers using in silico or clinical electrograms 22,23 .…”