Atrial Fibrillation Detection System via Acoustic Sensing for Mobile Phones

TL;DR

This paper introduces MobileAF, a smartphone-based atrial fibrillation detection system utilizing acoustic sensing with speakers and microphones, achieving high accuracy and reliability for portable cardiac monitoring.

Contribution

The paper presents a novel acoustic sensing method for AF detection on smartphones, including a multi-channel pulse wave probing, a signal purification pipeline, and a ResNet-based detection model.

Findings

Achieved 97.9% accuracy in AF detection

Demonstrated robustness with 96.8% precision and 97.2% recall

Collected data from 23 participants to validate system performance

Abstract

Atrial fibrillation (AF) is characterized by irregular electrical impulses originating in the atria, which can lead to severe complications and even death. Due to the intermittent nature of the AF, early and timely monitoring of AF is critical for patients to prevent further exacerbation of the condition. Although ambulatory ECG Holter monitors provide accurate monitoring, the high cost of these devices hinders their wider adoption. Current mobile-based AF detection systems offer a portable solution, however, these systems have various applicability issues such as being easily affected by environmental factors and requiring significant user effort. To overcome the above limitations, we present MobileAF, a novel smartphone-based AF detection system using speakers and microphones. In order to capture minute cardiac activities, we propose a multi-channel pulse wave probing method. In addition, we enhance the signal quality by introducing a three-stage pulse wave purification pipeline. What's more, a ResNet-based network model is built to implement accurate and reliable AF detection. We collect data from 23 participants utilizing our data collection application on the smartphone. Extensive experimental results demonstrate the superior performance of our system, with 97.9% accuracy, 96.8% precision, 97.2% recall, 98.3% specificity, and 97.0% F1 score.