Automated detection of pediatric congenital heart disease from phonocardiograms using deep and handcrafted feature fusion

基于深度学习与手工特征融合的心音图儿童先天性心脏病自动检测

Abstract: Congenital heart disease (CHD) is the most common type of birth defect, impacting about 1% of live births worldwide. Echocardiography, the gold-standard diagnostic method, is costly and inaccessible in low-resource settings. Diagnosis is delayed due to limited skilled experts, whose ability to interpret pathological patterns varies significantly, causing inter- and intra-clinician variability.

摘要： 先天性心脏病（CHD）是全球最常见的出生缺陷类型，影响约 1% 的新生儿。超声心动图作为诊断的“金标准”，在资源匮乏地区因成本高昂而难以普及。由于缺乏熟练的专家，诊断往往被延误；而专家对病理模式的解读能力差异巨大，导致了临床医生之间及医生内部的诊断变异性。

Therefore, we present a new method for a more accessible diagnostic modality, the digital stethoscope, to detect CHDs. Our method is based on deep feature fusion, integrating deep and handcrafted features for the automated early detection of CHDs.

因此，我们提出了一种基于数字听诊器的新型诊断方法，旨在提高先天性心脏病检测的普及性。我们的方法基于深度特征融合，通过整合深度学习特征与手工提取特征，实现先天性心脏病的自动化早期检测。

For this work, Phonocardiography (PCG) recordings were obtained from 751 pediatric subjects (Age: 1 month - 16 years) in Bangladesh, ranging from infants to adults at four auscultation locations: mitral valve (MV), aortic valve (AV), pulmonary valve (PV), and tricuspid valve (TV). These recordings were labeled based on confirmed diagnoses by cardiologists as either cases of CHD or non-CHD.

本研究采集了孟加拉国 751 名儿童受试者（年龄 1 个月至 16 岁）的心音图（PCG）记录，涵盖从婴儿到青少年的不同年龄段，听诊位置包括二尖瓣（MV）、主动脉瓣（AV）、肺动脉瓣（PV）和三尖瓣（TV）。这些记录均根据心脏病专家的确诊结果，被标注为先天性心脏病（CHD）或非先天性心脏病病例。

The results demonstrated that our proposed model achieved an accuracy of 92%, a sensitivity of 91%, and a specificity of 91%, based on a patient-wise split of 70% training, 20% validation, and 10% testing. Furthermore, the Area Under the Receiver Operating Characteristic curve (AUROC) of 96%, and an F1-score of 92%. This model promises efficient real-time remote detection of CHDs as a cost-effective screening tool for low-resource settings.

结果表明，在按患者划分的 70% 训练集、20% 验证集和 10% 测试集的基础上，我们提出的模型达到了 92% 的准确率、91% 的灵敏度和 91% 的特异性。此外，该模型的受试者工作特征曲线下面积（AUROC）为 96%，F1 分数为 92%。该模型有望作为一种经济高效的筛查工具，在资源匮乏地区实现先天性心脏病的高效实时远程检测。