Cardiovascular diseases (CVD) are the leading cause of death worldwide, with coronary artery disease (CAD) comprising the largest subcategory of CVDs. Recently, there has been increased focus on detecting CAD using phonocardiogram (PCG) signals, with high success in clinical environments with low noise and optimal sensor placement. Multichannel techniques have been found to be more robust to noise; however, achieving robust performance on real-world data remains a challenge. This work utilises a novel multichannel energy-based noisy-segment rejection algorithm, using heart and noise-reference microphones, to discard audio segments with large amounts of nonstationary noise before training a deep learning classifier. This conformer-based classifier takes mel-frequency cepstral coefficients (MFCCs) from multiple channels, further helping improve the model's noise robustness. The proposed method achieved 78.4% accuracy and 78.2% balanced accuracy on 297 subjects, representing improvements of 4.1% and 4.3%, respectively, compared to training without noisy-segment rejection.

翻译：心血管疾病是全球主要的死亡原因，其中冠状动脉疾病构成心血管疾病的最大亚类。近年来，利用心音图信号检测冠状动脉疾病受到越来越多的关注，在低噪声和传感器放置理想的临床环境中取得了很高的成功率。多通道技术已被证明对噪声更具鲁棒性；然而，在真实世界数据上实现鲁棒性能仍然是一个挑战。本研究采用一种新颖的基于能量的多通道噪声段剔除算法，利用心脏麦克风和噪声参考麦克风，在训练深度学习分类器之前丢弃含有大量非平稳噪声的音频片段。这种基于Conformer的分类器从多个通道提取梅尔频率倒谱系数，进一步帮助提升模型的噪声鲁棒性。所提出的方法在297名受试者上达到了78.4%的准确率和78.2%的平衡准确率，与未进行噪声段剔除的训练相比，分别提升了4.1%和4.3%。