Clinical guidelines underscore the importance of regularly monitoring and surveilling arteriovenous fistula (AVF) access in hemodialysis patients to promptly detect any dysfunction. Although phono-angiography/sound analysis overcomes the limitations of standardized AVF stenosis diagnosis tool, prior studies have depended on conventional feature extraction methods, restricting their applicability in diverse contexts. In contrast, representation learning captures fundamental underlying factors that can be readily transferred across different contexts. We propose an approach based on deep denoising autoencoders (DAEs) that perform dimensionality reduction and reconstruction tasks using the waveform obtained through one-level discrete wavelet transform, utilizing representation learning. Our results demonstrate that the latent representation generated by the DAE surpasses expectations with an accuracy of 0.93. The incorporation of noise-mixing and the utilization of a noise-to-clean scheme effectively enhance the discriminative capabilities of the latent representation. Moreover, when employed to identify patient-specific characteristics, the latent representation exhibited performance by surpassing an accuracy of 0.92. Appropriate light-weighted methods can restore the detection performance of the excessively reduced dimensionality version and enable operation on less computational devices. Our findings suggest that representation learning is a more feasible approach for extracting auscultation features in AVF, leading to improved generalization and applicability across multiple tasks. The manipulation of latent representations holds immense potential for future advancements. Further investigations in this area are promising and warrant continued exploration.

翻译：临床指南强调了在血液透析患者中定期监测和监控动静脉内瘘（AVF）通路的重要性，以便及时检测任何功能障碍。尽管声音造影/声音分析克服了标准化AVF狭窄诊断工具的局限性，但先前的研究依赖于传统的特征提取方法，限制了其在多样化场景中的适用性。相比之下，表示学习能够捕获可在不同情境中轻松迁移的基本潜在因素。我们提出了一种基于深度去噪自编码器（DAEs）的方法，该方法利用通过单级离散小波变换获得的波形执行降维和重建任务，并采用了表示学习。我们的结果表明，由DAE生成的潜在表示表现出色，准确率达到0.93。噪声混合的引入和采用噪声到干净方案有效增强了潜在表示的判别能力。此外，当用于识别患者特定特征时，潜在表示的准确率超过0.92。适当的轻量级方法可以恢复过度降维版本的检测性能，并使其能在计算能力较弱的设备上运行。我们的研究结果表明，表示学习是一种更可行的AVF听诊特征提取方法，从而提高了跨多个任务的泛化能力和适用性。对潜在表示的操控为未来的进步蕴含巨大潜力。该领域的进一步研究前景广阔，值得持续探索。