Heart murmurs are abnormal sounds caused by turbulent blood flow in the heart. Several diagnostic methods are available to detect heart murmurs and their severity, including cardiac auscultation, echocardiography, and phonocardiography (PCG). However, these methods have limitations, including the need for extensive training among healthcare providers, the cost and accessibility of echocardiography, and noise interference during PCG data processing. This study proposes an end-to-end real-time heart murmur detection approach using traditional and depthwise separable convolutional networks. We applied a Butterworth filter and Continuous Wavelet Transform (CWT) to eliminate noise and extract meaningful features from the PCG data. The proposed network consists of three parts: a Squeeze net that generates a compressed data representation, a Bottleneck layer that minimizes computational complexity using depthwise-separable convolutions, and an Expansion net that up-samples the data to capture fine details. We evaluated our model on the publicly available CirCor pediatric heart sound dataset. Using only $\sim$5.4k parameters, we achieved an accuracy of 85%, a sensitivity of 85%, and a specificity of 92%, successfully outperforming several larger models. Furthermore, we converted our network into a TinyML format and tested it on two resource-constrained devices, achieving an average real-time inference accuracy of 91% on a Raspberry Pi 4B and 80% on an Android smartphone. The proposed lightweight model offers a robust deep learning framework for accurate, real-time heart murmur detection, showing strong promise for accessible medical diagnostics in limited-resource environments. The code is publicly available at https://github.com/jobayer/FunnelNet.

翻译：心脏杂音是由心脏内湍急血流产生的异常声音。目前可用于检测心脏杂音及其严重程度的诊断方法包括心脏听诊、超声心动图和心音图（PCG），但这些方法存在局限性，例如需要医疗人员接受大量培训、超声心动图成本及可及性问题，以及PCG数据处理过程中的噪声干扰。本研究提出一种基于传统卷积网络和深度可分离卷积网络的端到端实时心脏杂音检测方法。我们采用巴特沃斯滤波器与连续小波变换（CWT）消除噪声，并从PCG数据中提取有意义的特征。所提出的网络由三部分组成：生成压缩数据表示的压缩层、利用深度可分离卷积减少计算复杂度的瓶颈层，以及对数据上采样以捕捉精细细节的扩展层。我们在公开的CirCor儿科心脏杂音数据集上评估了模型。仅使用约5.4k参数，便实现了85%的准确率、85%的灵敏度和92%的特异性，成功超越多个更大的模型。此外，我们将网络转换为TinyML格式，并在两个资源受限设备上进行测试，在树莓派4B和安卓智能手机上分别实现了91%和80%的平均实时推理准确率。该轻量化模型为准确、实时的心脏杂音检测提供了稳健的深度学习框架，在资源有限环境中展现出实现可及医疗诊断的巨大潜力。代码已公开于https://github.com/jobayer/FunnelNet。