用手机扬声器麦克风检测心房颤动,准确率超97%。
Atrial Fibrillation Detection System via Acoustic Sensing for Mobile Phones
- 通过多通道脉搏波探测捕捉心脏微弱活动信号。
- 在23人实验中实现97.9%准确率与98.3%特异性。
- 无需穿戴设备,适合日常移动监测,适合慢性病患者。
心房颤动(AF)由心房不规则电冲动引发,可导致严重并发症甚至死亡。由于AF具有间歇性,早期及时监测对患者至关重要。尽管动态心电图(Holter)监测准确,但设备成本高,难以普及。现有基于手机的检测系统虽便携,却易受环境干扰且需用户配合。为此,我们提出MobileAF,一种利用手机扬声器与麦克风的新型心房颤动检测系统。为捕捉细微心脏活动,我们设计了多通道脉搏波探测方法,并引入三阶段脉搏波净化流程提升信号质量。同时构建基于ResNet的网络模型实现精准可靠的AF识别。在23名参与者上通过手机应用采集数据,实验结果表明系统性能优越:准确率97.9%,精确率96.8%,召回率97.2%,特异性98.3%,F1分数97.0%。
原文摘要 · Abstract (English)
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.
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