利用合成与增强生物信号实现多模态和多通道心音分类的规模化
Scaling to Multimodal and Multichannel Heart Sound Classification with Synthetic and Augmented Biosignals
AI总结：
针对心音分类中同步和多通道数据集有限的问题，提出结合传统信号处理与去噪扩散模型生成增强数据，微调Wav2Vec 2.0分类器，在多个数据集上取得最优性能。
摘要：
心血管疾病（CVD）是全球主要死因，每年约1790万人死亡。早期检测至关重要，因此需要准确且廉价的预筛查方法。深度学习最近被应用于使用同步心音图（PCG）和心电图（ECG）信号以及多通道PCG（mPCG）对指示CVD的异常心音进行分类。然而，由于同步和多通道数据集的有限可用性，最先进的架构仍未得到充分利用。增强数据集和预训练模型为克服这些限制提供了途径，使基于Transformer的架构能够有效训练。本工作将传统信号处理与去噪扩散模型WaveGrad和DiffWave相结合，创建增强数据集，以微调基于Wav2Vec 2.0的分类器，用于多模态和多通道心音数据集。该方法达到了最先进的性能。在单通道PCG的Computing in Cardiology (CinC) 2016数据集上，准确率、未加权平均召回率（UAR）、敏感性、特异性和马修斯相关系数（MCC）分别达到92.48%、93.05%、93.63%、92.48%、94.93%和0.8283。使用CinC的training-a数据集中的同步PCG和ECG信号，准确率、UAR、敏感性、特异性和MCC分别达到93.14%、92.21%、94.35%、90.10%、95.12%和0.8380。使用包含mPCG数据的可穿戴背心数据集，模型达到77.13%的准确率、74.25%的UAR、86.47%的敏感性、62.04%的特异性和0.5082的MCC。这些结果证明了在增强数据集支持下基于Transformer的模型在CVD检测中的有效性，突显了它们在推进多模态和多通道心音分类方面的潜力。
英文摘要：
Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for approximately 17.9 million deaths each year. Early detection is critical, creating a demand for accurate and inexpensive pre-screening methods. Deep learning has recently been applied to classify abnormal heart sounds indicative of CVDs using synchronised phonocardiogram (PCG) and electrocardiogram (ECG) signals, as well as multichannel PCG (mPCG). However, state-of-the-art architectures remain underutilised due to the limited availability of synchronised and multichannel datasets. Augmented datasets and pre-trained models provide a pathway to overcome these limitations, enabling transformer-based architectures to be trained effectively. This work combines traditional signal processing with denoising diffusion models, WaveGrad and DiffWave, to create an augmented dataset to fine-tune a Wav2Vec 2.0-based classifier on multimodal and multichannel heart sound datasets. The approach achieves state-of-the-art performance. On the Computing in Cardiology (CinC) 2016 dataset of single channel PCG, accuracy, unweighted average recall (UAR), sensitivity, specificity and Matthew's correlation coefficient (MCC) reach 92.48%, 93.05%, 93.63%, 92.48%, 94.93% and 0.8283, respectively. Using the synchronised PCG and ECG signals of the training-a dataset from CinC, 93.14%, 92.21%, 94.35%, 90.10%, 95.12% and 0.8380 are achieved for accuracy, UAR, sensitivity, specificity and MCC, respectively. Using a wearable vest dataset consisting of mPCG data, the model achieves 77.13% accuracy, 74.25% UAR, 86.47% sensitivity, 62.04% specificity, and 0.5082 MCC. These results demonstrate the effectiveness of transformer-based models for CVD detection when supported by augmented datasets, highlighting their potential to advance multimodal and multichannel heart sound classification.
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