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

TL;DR

Atrial fibrillation (AF) detection is challenging due to intermittency and asymptomatic cases, limiting reliance on costly ECG-based screening. AcousAF introduces a smartphone-based acoustic sensing system that probes the wrist with an 18 kHz tone, extracts a CPR-based pulse wave via I/Q demodulation, and uses RR-interval and statistical features with an ML classifier (Linear SVC) to detect AF. In a 20-subject study, the approach achieves high performance ($92.8\%$ accuracy, $86.9\%$ precision, $87.4\%$ recall, $87.1\%$ F1, and $AP=0.909$), and maintains robustness under moderate background noise, illustrating the potential for low-cost, wide-access AF screening on mobile devices. The work presents a practical, privacy-conscious path toward continuous AF monitoring, while acknowledging limitations related to noise, arrhythmia differentiation, and data privacy that future work should address.

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 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.

Figures (9)

• Figure 1: Feasibility study setup and result. (a): Scenario of feasibility study. The PPG sensor is placed at the fingertip, and the smartphone is placed at the wrist above the radial artery. (b): Comparison of PPG and Acoustic CPR. The CPR waveform shows a high similarity with the PPG waveform.
• Figure 2: AcousAF system overview.
• Figure 3: Comparison of CPR results between subjects with (a) NSR and (b) AF.
• Figure 4: ECG and CPR Waveforms
• Figure 5: Comparison of ECG of AF and NSR.