Zolya Maria-Alexandra, Popa Elena-Laura, Baltag Cosmin, Bratu Dragoș-Vasile, Coman Simona, Moraru Sorin-Aurel
Department of Automatics and Information Technology, Transilvania University of Brasov, 500036 Brasov, Romania.
Sensors (Basel). 2025 Mar 8;25(6):1682. doi: 10.3390/s25061682.
Cardiovascular diseases (CVDs) are the leading cause of death worldwide, claiming over 17 million lives annually. Early detection of conditions like heart murmurs, often indicative of heart valve abnormalities, is critical for improving patient outcomes. Traditional diagnostic methods, including physical auscultation and advanced imaging techniques, are constrained by their reliance on specialized clinical expertise, inherent procedural invasiveness, substantial financial costs, and limited accessibility, particularly in resource-limited healthcare environments. This study presents a novel convolutional recurrent neural network (CRNN) model designed for the non-invasive classification of heart murmurs. The model processes heart sound recordings using advanced pre-processing techniques such as z-score normalization, band-pass filtering, and data augmentation (Gaussian noise, time shift, and pitch shift) to enhance robustness. By combining convolutional and recurrent layers, the CRNN captures spatial and temporal features in audio data, achieving an accuracy of 90.5%, precision of 89%, and recall of 87%. These results underscore the potential of machine-learning technologies to revolutionize cardiac diagnostics by offering scalable, accessible solutions for the early detection of cardiovascular conditions. This approach paves the way for broader applications of AI in healthcare, particularly in underserved regions where traditional resources are scarce.
心血管疾病（CVDs）是全球主要的死因，每年夺去超过1700万人的生命。早期检测出如心脏杂音等情况（通常表明心脏瓣膜异常）对于改善患者预后至关重要。传统的诊断方法，包括体格听诊和先进的成像技术，受到其对专业临床专业知识的依赖、固有的程序侵入性、高昂的财务成本以及有限的可及性的限制，特别是在资源有限的医疗环境中。本研究提出了一种新颖的卷积循环神经网络（CRNN）模型，用于心脏杂音的无创分类。该模型使用诸如z分数归一化、带通滤波和数据增强（高斯噪声、时间偏移和音高偏移）等先进的预处理技术来处理心音记录，以增强鲁棒性。通过结合卷积层和循环层，CRNN捕捉音频数据中的空间和时间特征，实现了90.5%的准确率、89%的精确率和87%的召回率。这些结果强调了机器学习技术通过提供可扩展、可及的解决方案来早期检测心血管疾病，从而彻底改变心脏诊断的潜力。这种方法为人工智能在医疗保健领域的更广泛应用铺平了道路，特别是在传统资源稀缺的服务不足地区。
