  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.
