SDCEETLGASAug 9, 2024

AcousAF: Acoustic Sensing-Based Atrial Fibrillation Detection System for Mobile Phones

arXiv:2408.04912v13 citationsh-index: 2
Originality Incremental advance
AI Analysis

This work addresses the need for low-cost, portable AF monitoring for patients, offering a solution that reduces user effort and environmental sensitivity compared to existing mobile systems, though it is incremental in improving upon current mobile-based methods.

The paper tackled the problem of detecting atrial fibrillation (AF) using mobile phones by developing AcousAF, a system that uses smartphone acoustic sensors to acquire pulse waves from the wrist, achieving 92.8% accuracy and 87.1% F1 score in experiments with 20 participants.

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 AcousAF, a novel AF detection system based on acoustic sensors of smartphones. Particularly, we explore the potential of pulse wave acquisition from the wrist using smartphone speakers and microphones. In addition, we propose a well-designed framework comprised of pulse wave probing, pulse wave extraction, and AF detection to ensure accurate and reliable AF detection. We collect data from 20 participants utilizing our custom data collection application on the smartphone. Extensive experimental results demonstrate the high performance of our system, with 92.8% accuracy, 86.9% precision, 87.4% recall, and 87.1% F1 Score.

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