Smartphone-based paroxysmal atrial fibrillation monitoring with robust generalization

TL;DR

This paper introduces a robust smartphone-based method for detecting paroxysmal atrial fibrillation using heart beat intervals, demonstrating high accuracy and generalization on real-world low-cost hardware and large ECG datasets.

Contribution

The study presents a novel computational approach for AFib detection from short waveform data, validated on smartphone recordings and large ECG datasets, outperforming existing methods.

Findings

Achieves 93% sensitivity and 94% specificity on 30-second waveforms.

Demonstrates strong generalization across different datasets.

Validates feasibility on low-cost Android smartphones.

Abstract

Atrial fibrillation is increasingly prevalent, especially in the elderly, and challenging to detect due paroxysmal nature. Here, we propose novel computational methods based on heart beat intervals to facilitate rapid and robust discrimination between atrial fibrillation and sinus rhythm. We used low-cost Android smartphones, and recorded short, 30 second waveform data from 194 participants. In addition, we evaluated our approach on 8528 hand-held ECG recordings to show generalization. Our approach achieves a sensitivity of 93% and specificity of 94% on 30 second waveforms, significantly outperforming previously proposed heart rate variability features and smartphone-based AFib detection methods, and substantiates the feasibility of real-world application on low-cost hardware.