用手机音频检测心脏杂音，轻量模型支持本地运行。
Detecting abnormal heart sound using mobile phones and on-device IConNet
- 基于手机录音和轻量IConNet模型实现心音异常检测
- 无需专业听诊器，直接分析原始音频波形
- 模型可解释性强，适合远程医疗与移动端应用
心血管疾病全球高发，亟需便捷的早期筛查手段。传统方法依赖医生听诊后进行超声心动图和心电图检查。为推动早期诊断普及，我们提出一种面向移动端的用户友好型异常心音检测方案，利用手机录音与专为设备端推理优化的轻量神经网络IConNet。不同于以往依赖专用听诊器的方法，本方案直接分析音频记录，依托新型可解释卷积神经网络IConNet，融合音频信号处理先验知识，提升从原始波形中提取神经模式的效率与透明度。该研究推动了医疗AI的可信化发展，助力远程健康监测。
原文摘要 · Abstract (English)
Given the global prevalence of cardiovascular diseases, there is a pressing need for easily accessible early screening methods. Typically, this requires medical practitioners to investigate heart auscultations for irregular sounds, followed by echocardiography and electrocardiography tests. To democratize early diagnosis, we present a user-friendly solution for abnormal heart sound detection, utilizing mobile phones and a lightweight neural network optimized for on-device inference. Unlike previous approaches reliant on specialized stethoscopes, our method directly analyzes audio recordings, facilitated by a novel architecture known as IConNet. IConNet, an Interpretable Convolutional Neural Network, harnesses insights from audio signal processing, enhancing efficiency and providing transparency in neural pattern extraction from raw waveform signals. This is a significant step towards trustworthy AI in healthcare, aiding in remote health monitoring efforts.
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