# Deep denoising autoencoder-based non-invasive blood flow detection for
arteriovenous fistula

Li-Chin Chen  Yi-Heng Lin  Li-Ning Peng  Feng-Ming Wang  Yu-Hsin Chen  Po-Hsun
Huang  Shang-Feng Yang  Yu Tsao  Senior Member, IEEE This work was supported
by the Cheng Hsin General Hospital under Grant CY11018. Shang-Feng Yang and
Po-Hsun Huang contributed equally.Li-Chin Chen is with the Research Center for
Information Technology Innovation, Academia Sinica, Taipei, Taiwan
(li.chin@citi.sinica.edu.tw).Yi-Heng Lin is with the Department of Electrical
Engineering, National Taiwan University, Taipei, Taiwan, and the Research
Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan
(b08901161@ntu.edu.tw).Li-Ning Peng is with the Center for Geriatrics and
Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan, and the Center
for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung
University, Taipei, Taiwan (lining.peng@gmail.com).Feng-Ming Wang is with the
Dean of Kai-Yan Clinic, Taipei, Taiwan (fonming2000@gmail.com).Yu-Hsin Chen is
with the School of Medicine, National Yang Ming Chiao Tung University, Taipei,
Taiwan (yhchen17@gmail.com).Po-Hsun Huang is with the Institute of Clinical
Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, the
Division of Cardiology, Department of Medicine, Taipei Veterans General
Hospital, Taipei, Taiwan, and the Cardiovascular Research Center, Taipei
Veterans General Hospital, Taipei, Taiwan (huangbsvgh@gmail.com).Shang-Feng
Yang is with the Division of Nephrology, Department of Medicine, Cheng Hsin
General Hospital, Taipei, Taiwan, the Departmemt of Clinical Pathology, Cheng
Hsin General Hospital, Taipei, Taiwan, and the Institute of Clinical Medicine,
National Yang Ming Chiao Tung University, Taipei, Taiwan
(samyang1223@gmail.com).Yu Tsao is with the Research Center for Information
Technology Innovation, Academia Sinica, Taipei, Taiwan
(yu.tsao@citi.sinica.edu.tw).

###### Abstract

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, which are
susceptible to non-stationarity, incapable of capturing individual patient
characteristics, and unable to account for variations based on the severity
and positioning of stenosis, thereby 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.

Arteriovenous fistula, deep denoising autoencoder, latent representation,
pretrain model, representation learning, vascular access surveillance.

##  1 Introduction

To ensure optimal dialysis treatment, individuals undergoing hemodialysis (HD)
necessitate an adequate vascular access that remains stable over time. The
arteriovenous fistula (AVF), an anastomosis expertly crafted between arteries
and veins, emerges as the preferred choice due to its diminished morbidity
rate and prolonged patency [1]. Nevertheless, the occurrence of AVF stenosis
resulting from neointimal hyperplasia and subsequent reduction in blood flow
can culminate in vascular thrombosis and AVF failure [2]. Numerous
investigations have reported AVF patency rates ranging between 50% and 80% at
the conclusion of the first year following creation, with figures declining to
20% and 60% at the conclusion of the second year [3]. Consequently, the
preservation of a functional AVF persists as an obstacle for HD patients.

While angiography stands as the definitive method for diagnosing AVF stenosis,
it is burdened by invasiveness, high costs, protracted procedures, and
associated side effects [4]. On the other hand, alternative non-invasive
approaches, such as color-duplex ultrasound and physical examination (PE),
present themselves as viable options. However, color-duplex ultrasound
necessitates the availability of appropriate equipment and proficient
personnel, while PE mandates the expertise of skilled operators who employ
visual inspection, palpation, and auscultation [5, 6]. It is worth noting that
PE can be susceptible to operator-dependent variations, leading to mixed
outcomes in terms of accuracy in detecting and localizing AVF stenosis [7].

The guidelines established by the Kidney Disease Outcomes Quality Initiative
(KDOQI) emphasize the importance of regular monitoring and surveillance of
vascular access to enable the timely detection of dysfunction [8, 6, 9, 10].
Consequently, there arises a need for a straightforward, cost-effective, and
less reliant approach that minimizes the reliance on specific devices and
personnel. This would facilitate the seamless implementation of routine
auscultation for AVFs.

Phono-angiography/sound analysis, being a non-invasive method, requires
portable and cost-effective equipment, which has garnered considerable
attention in the development of diagnostic tools for stenosis and thrombosis
surveillance. The audible sound generated by turbulent blood flow and vessel
vibrations can be analyzed to indicate the state of the fistula [10, 11].
Numerous studies have explored diagnostic tools for stenosis surveillance
based on distinctive acoustic characteristics [5, 12, 13, 7, 14, 11]. However,
the extraction and transformation of feature-specific information may suffer
from limited generalizability, limiting the applicability of the results in
different contexts.

In contrast, representation learning offers a technique wherein a latent, low-
dimensional code embedding is learned, capturing the posterior distribution of
the underlying factors that explain the observed input. This code can be
easily transferred to construct a classifier for other tasks [15, 16]. The
fundamental idea of this study is to develop an end-to-end, non-invasive
technique for detecting AVF blood flow, utilizing representation learning.
Such an assistive tool simplifies and standardizes auscultation, rendering it
feasible for nephrologists, nurses, and even patients themselves.
Additionally, it enables continuous monitoring of the progression of
arteriovenous vessels in a non-invasive manner.

###  1.1 Blood flow of arteriovenous fistula

In a mature AVF, the blood flow typically ranges from 600 to 1200 ml/min [17].
Both low and high volumes can lead to undesirable outcomes. Studies have
proposed active surveillance and preemptive repair of subclinical stenosis
when the blood flow falls below 750 ml/min, aiming to reduce thrombosis rates,
costs, and prolong the functional lifespan of AVFs [18, 19]. Conversely, blood
flow exceeding 1500 ml/min has been associated with an increased risk of
distal ischemia, known as steal syndrome [20]. This phenomenon affects
approximately 1-20% of HD patients with upper-arm AVFs and is characterized by
digital coolness, pallor, mild paresthesia, and, in severe cases, tissue
necrosis [21]. Regular monitoring of blood flow enables the early detection of
AVF stenosis, which plays a crucial role in salvaging access function [22].

From an acoustical standpoint, a mature AVF exhibits a low-pitched continuous
bruit that can be perceived throughout both systole and diastole, with
heightened intensity near the arterial anastomosis. These bruits are the
audible sounds originating from the fistula, which can be discerned through a
stethoscope [11]. Conversely, in the presence of a stenosis, a high-pitched
systolic bruit manifests distal to the stenosis, followed by a normal bruit
proximally [17, 6, 8].

###  1.2 Signal characters and feature extractions for AVF

Previous research has revealed significant variations in the acoustical
characteristics of AVFs. Among the most frequently discussed acoustical
features of AVFs is the pitch resulting from the bruit within the vessel. Some
studies [10, 23, 14, 13] suggest that a higher degree of stenosis is indicated
by a high-pitched bruit. Additionally, certain research indicates that a
higher velocity of blood flow corresponds to a higher frequency [5]. Other
studies identify specific frequencies for stenosis detection, such as
frequencies above 200-300 Hz [24] or around 700-800 Hz [25]. There are
arguments proposing the combination of amplitude and frequency information for
more comprehensive analysis [11], as well as the simultaneous consideration of
time and frequency domain information [13]. Conversely, some researchers argue
that frequency analysis should differentiate between the systolic and
diastolic phases [5, 10, 26].

These varying findings align with the fact that AVF auscultations are
subjective, dependent on staff expertise, subject to non-stationarity,
specific to individual patient characteristics, and differ based on the
severity levels and positioning of stenosis [5, 11, 6, 10, 7].

####  1.2.1 Various feature extraction transformations

In the evaluation of AVFs, several feature extraction transformations have
been employed. These include the fast Fourier transform (FFT) [24], short-time
Fourier transform (STFT) [5], wavelet transform (WT) [25, 11, 27, 28], Mel
spectrograms [26], and intrinsic mode functions (IMF) [14]. Some studies
propose combining multiple coefficients, such as incorporating the ratio of
frequency power, Mel-frequency cepstral coefficients (MFCC), and normalized
cross-correlation coefficient [12], or combining power spectral density (PSD)
and wavelet decomposition [27], or utilizing the mean and variation of the
center of frequency and energy ratio within a defined frequency band [5]. Wang
et al. [13] further introduced the S-transform, which preserves information
from blood flow sounds in both the time and frequency domains simultaneously.

In line with the differences between the systolic and diastolic phases,
heartbeats peaks and periods have also been detected [11, 28], proving to be
distinguishable when multiple frequency filtering techniques are applied [5,
11, 23]. The variations observed among current studies highlight the absence
of a consensus regarding an optimal feature extraction transformation.

####  1.2.2 Diverse classification labels

Another factor contributing to the difficulty in comparing related works is
the variability in the AVF vascular access indicators targeted by each study.
Some studies classified the fistula into six staff-defined conditions, ranging
from the best to the worst condition [28, 24], while others categorized the
sounds into five types, including normal, hard, high, intermittent, and
whistling [29]. Alternatively, some studies employed a binary classification
to denote stenosis above or below 50% [26, 5, 23]. Other classifications were
based on indicators such as a resistance index (RI) above or below 60%, which
indicates the difficulty of blood flow to the distal end [12], or a luminal
diameter above or below 50% in the AVF vessel, also referred to as the size or
width of the vessel [13].

Furthermore, while guidelines recommend weekly surveillance of the vascular
access for early dysfunction detection, most studies focused on stenosis based
on significant contrast conditions, such as before and after percutaneous
transluminal angioplasty (PTA) [26, 5], or stenosis versus non-stenosis [11,
24, 14, 23, 13]. These approaches do not align with the objective of early
surveillance.

####  1.2.3 Varied puncture site measurements

The puncture location of the AVF can be categorized into different sites, such
as the site of arteriovenous anastomosis (site 1), arterial puncture site
(site 2), venous puncture site (site 3), and so on (as illustrated in Fig. 1).
The anastomotic site is located near the wrist, distal to the heart, while the
proximal end of the AVF is situated proximal to the heart [13]. Each site
exhibits distinct signal characteristics. Some studies focused on analyzing a
single site [26, 12, 28, 13], while others measured multiple sites [5, 11, 14,
23]; however, the discussions predominantly revolved around the
characteristics of each site individually. The combination or utilization of
information from different sites has not been thoroughly explored.

Figure 1: Hemodialysis process, blood flow detection, and positioning of
auscultation sites.

####  1.2.4 Limited sample size

The evaluation of AVF was typically conducted by healthcare professionals, and
often only the assessment results were recorded. The use of stethoscope
recordings was not a regular practice. Furthermore, the collection of
stethoscope recordings required a quiet room to minimize background noise. The
quality of the stethoscope and the recording equipment could also impact data
collection and study quality. As a result, the collection of stethoscope
recordings during AVF auscultation was typically done in a trial-based
scenario, which added to the practical burden and resulted in a limited number
of sampled data. The recruited patient sizes in the studies ranged from 5 to
74 [5, 11, 12, 13, 14, 26, 27, 28, 29], which is considered small sample size
for machine learning applications.

To address the aforementioned constraints in current research, this study
employs deep neural networks to overcome the challenges with the following
design:

  1. 1.

Initially, a pretrain model is trained for dimensionality reduction and
reconstruction. The latent representation learned from this model serves as
efficient acoustic features, accommodating the non-stationary and patient-
specific characteristics of auscultation recordings. The generalizability of
this representation is also assessed.

  2. 2.

Blood flow measurement, a widely recognized indicator for vascular access,
exhibits strong predictive power in early detection of dysfunction and AVF
complications [7]. Hence, it is adopted as the prediction label in this study.

  3. 3.

Information from different puncture sites is analyzed, and the combination of
latent information is explored.

  4. 4.

To mitigate the limitation of a small dataset and enhance robust
representation learning, a noise-mixing approach is employed to augment the
dataset.

  5. 5.

Considering the applicability of the proposed method to lightweight devices
with limited computational capabilities, such as stethoscopes or wearable
devices, a further dimensionality reduction technique is demonstrated while
preserving approximate predictive ability.

##  2 Methods

In this study, we propose a representation learning approach based on the
architecture of a deep denoising autoencoder (DAE), as depicted in Fig.2. The
DAE is trained to perform dimensionality reduction and reconstruction tasks
using the waveform of one-level coefficients obtained after applying discrete
wavelet transform (DWT). The latent representation generated by the DAE is
utilized for phono-angiography analysis in the downstream task.

Figure 2: Model architecture DAE. $E$: encoder; $D$: decoder; $L$: latent
representation; $\ominus$: subtraction of the two latent representation.

###  2.1 Deep autoencoder and deep denoising autoencoder

Deep autoencoder (AE) neural networks [30] are feed-forward multi-layer neural
networks that aim to reconstruct the input data itself. The AE consists of an
encoder and a decoder. The encoder, denoted as $f_{\theta}$, transforms the
input $x$ into a hidden representation $y$ through a deterministic mapping:

$f_{\theta}(x)=s(Wx+b),$ (1)

where $s(\cdot)$ is a non-linear activation function, and $\theta=\\{W,b\\}$
represents the weights and biases of the encoder. The decoder, denoted as
$g_{\theta^{\prime}}$, reconstructs the hidden representation $y$ into a
latent space $z$:

$z=g_{\theta^{\prime}}(y)=s(W^{\prime}y+b^{\prime}),$ (2)

where $\theta^{\prime}=\\{W^{\prime},b^{\prime}\\}$ represents the weights and
biases of the decoder. The goal of the autoencoder is to minimize the
discrepancy between the original input $x$ and its reconstructed output $z$
[31].

The constraint of reducing dimensionality at the bottleneck layer separates
useful information from noise and less informative details. This
dimensionality reduction helps remove irrelevant variations and focuses on the
essential aspects of the data, enhancing classification performance. By
training an AE on a reconstruction task, the bottleneck layer becomes
specialized in encoding discriminative features, which can be highly
informative for other classification tasks.

DAE takes a further approach to reconstruction by considering noisy inputs
[32, 33]. The DAE introduces a noise-adding step to the initial input $x$,
denoted as noisy input $\widetilde{x}$. The encoder then maps $\widetilde{x}$
to a hidden representation:

$y=f_{\theta}(\widetilde{x})=s(W\widetilde{x}+b).$ (3)

The decoder reconstructs the clean input $x$ from the hidden representation
$y$:

$z=g_{\theta^{\prime}}(y).$ (4)

The parameters $\theta$ and $\theta^{\prime}$ are trained to minimize the
average reconstruction error over a training set, aiming to make the
reconstructed output $z$ as close as possible to the clean input $x$.

The use of a denoising autoencoder allows the model to learn robust
representations that are less sensitive to noise and variations in the input
data. It can help to capture essential features while discarding irrelevant
details and noise, leading to improved classification performance.

###  2.2 Experimental design

####  2.2.1 Patient recruitment

Patients were recruited from the HD unit of Chung-Hsin General Hospital. The
inclusion criteria for the study were patients with functioning AVFs who were
undergoing regular HD treatment. Exclusion criteria included age younger than
20 years, unwillingness or inability to undergo scheduled exams or follow-up
regularly, and inability to provide written informed consent. After
enrollment, electronic stethoscopes were used to collect auscultation signals
at three sites of a mature AVF: arteriovenous anastomosis (site 1), arterial
puncture site (site 2), and venous puncture site (site 3). The relative
positions of these sites are shown in Fig. 1. AVF blood flows were measured
using a Transonic® Flow-QC® Hemodialysis Monitor, which introduced saline into
the venous line with the dialysis lines in a normal position. Recirculation
was calculated based on the change in blood concentration between the venous
sensor and arterial sensor [34]. The study was approved by the institutional
review board of Chung-Hsin General Hospital (817) 109A-56, and all procedures
were conducted in accordance with the principles outlined in the Declaration
of Helsinki. Informed written consent was obtained from all participants prior
to enrollment.

Since the measured blood flow indicates the vessel access between site 2 and
3, this research focused on the auscultation recordings from site 2 and 3. A
total of 199 patients were initially recruited, but patients who lacked labels
or had incomplete recordings (e.g., lack of recordings from site 2 or 3) were
excluded from the analysis. Ultimately, 171 patients were included in the
study. The blood flow detection task was designed as a three-class
classification, categorizing blood flow into $<$750 ml/min, 750-1500 ml/min,
and $>$1500 ml/min, representing different clinical requirements for HD
patients.

####  2.2.2 Data preprocessing

The audio files were saved in the waveform audio file format (.wav) and
digitized using a 16-bit analog-to-digital converter with a sample rate of 8
kHz. Preprocessing steps were applied to the audio recordings as follows: (1)
Blank gaps before and after the auscultation sounds were removed. (2) The
amplitude of the recordings was normalized. (3) The middle part of the
recordings was segmented to avoid artifacts caused by placing and removing the
stethoscope.

To extract pitch-specific acoustic features, the DWT was applied using a
biorthogonal wavelet. Three levels of coefficients were obtained after the
low-pass filter (LPF) and were denoted as $w_{L1}$, $w_{L2}$, and $w_{L3}$.
The waveform, FFT, and STFT were computed for the original signal and the
three level coefficients, as shown in Fig. 3. The FFT and STFT representations
were then normalized using the absolute values and the natural logarithm of
one plus the input ($log1p$) transformation. The $log1p$ transformation
ensures that the values are above zero and normalizes potential errors that
could be introduced by the distribution between positive and negative values
[35, 36].

Figure 3: Wavelet transform process. L: low-pass filter (LPF) + downsampling;
H: high-pass filter (HPF) + downsampling; $\downarrow$2: downsampling by the
factor of 2; $w_{L1}$: one-level low-pass filtered coefficients; $w_{L2}$:
two-level low-pass filtered coefficients; $w_{L3}$: three-level low-pass
filtered coefficients; $w_{H1}$: one-level high-pass filtered coefficients;
$w_{H2}$: two-level high-pass filtered coefficients; $w_{H3}$: three-level
high-pass filtered coefficients; FFT: fast fourier transform; STFT: short-time
fourier transform.

To overcome the limitation of a small number of samples, the auscultation
audio recordings were mixed with seven different types of noise, including
five colored noises (white, blue, violet, brown, and pink noises) and two
types of background noise with people talking. The noise was added at two
different volumes, resulting in a total of 2,394 mixed noisy auscultation
recordings for site 2 and 3, respectively.

####  2.2.3 Model design

The architecture of the encoder and decoder for one-dimensional signals, which
include waveform and FFT, consisted of three layers of fully-connected layers
with Leaky Rectified Linear Unit (LeakyReLU) activation functions between each
layer. The output sizes of the encoder layers were set to 5000, 1000, and 100,
while the decoder layers had output sizes of 100, 1000, and 5000. For two-
dimensional signals such as STFT, the architecture involved three layers of
one-dimensional Convolutional (Conv1D) layers and max-pooling layers with
Rectified Linear Unit (ReLU) activation functions between them. The encoder
layers had filter sizes of 64, 32, and 16, and the decoder layers had filter
sizes of 16, 32, and 64. The kernel sizes for the max-pooling layers were set
to 2. The model was trained using the Adam optimizer and the mean squared
error (MSE) loss function, aiming to minimize the discrepancy between the
input and output. As the downstream task involved a small number of samples,
the training was based on a Radial Basis Function (RBF)-kerneled Support
Vector Machine (SVM) [37, 38]. The cost parameter ($C$) was set to 10 for the
SVM.

####  2.2.4 Training strategies and classification metrics

The data were initially split into training and testing datasets for the
pretraining of the encoder and decoder. The latent representation generated
during the testing phase served as the dataset for training the downstream
task. This dataset was then further divided into training and testing sets
after balanced sampling. All downstream tasks were performed based on balanced
sampling. The training process is illustrated in Fig. 4.

Figure 4: Model training process. $E$: encoder; $D$: decoder; $L$: latent
representation; n: number of samples.

The quality of the learned latent representation was assessed using
discrimination analysis. The autoencoder (AE) was set as the baseline and
trained to reconstruct both the original clean signals (clean-to-clean) and
the noise-mixed signals (noisy-to-noisy). The comparison between the two
reconstructions indicated the effectiveness of enlarging the dataset.
Additionally, the DAE was compared as it reconstructed clean audio from noisy
audio, demonstrating the effectiveness of the asymmetric input and output
approach.

To conduct a comprehensive analysis, various feature extraction methods
proposed previously were tested for blood flow detection, including
S-transform [13], IMF [14, 39], and Mel spectrogram [26]. The latent
representation from different combinations of sites 2 and 3 were examined, as
well as their individual representations. Furthermore, the generalization
ability of the latent representation was assessed by predicting patient-
specific information such as gender, hypertension (HTN) diagnosis, and
diabetes mellitus (DM) diagnosis.

To achieve further dimensionality reduction on portable devices, the latent
representation was condensed using principal component analysis (PCA) to lower
dimensionalities. It was then concatenated with demographic information,
including gender, age, HTN, and DM, as shown in Fig. 5. Categorical variables
were encoded using one-hot encoding, and numeric variables were normalized
using the $log1p$ function. Three algorithms based on different mechanisms
were examined: SVM, k-nearest neighbors (KNN) [40], and Light Gradient
Boosting Machine (LightGBM) [41]. The number of cluster in KNN were set to 3;
The number of boosted trees in LightGBM was set to 100, and the learning rate
was set to 0.05.

Figure 5: Model architecture DAE multi-modality combination. $E$: encoder;
$D$: decoder; $L$: latent representation; $\ominus$: subtraction of the two
latent representation; $\oplus$: concatenation of the two latent
representation; PCA: principal component analysis.

The reported classification metrics included the area under the receiver
operating characteristic (AUROC) curve, accuracy, sensitivity, specificity,
precision, and F1 score [42]. These metrics were calculated by averaging the
performance over ten runs of the training and testing process. Taking the mean
performances of the testing samples over multiple runs was considered more
representative than a single validation result. Since all the metrics are
percentage indicators and higher scores represent better performance, a
general average score (Avg.) was calculated to provide an overall measure of
performance across all metrics. This helps assess the overall performance of
the model.

##  3 Results

Table 1 showcases the demographic profile of the recruited patients. A
majority of the recruited patients were of the male gender (59.65%), with
diagnoses of HTN (72.52%) and DM (64.91%). The average age of the patients was
67 years, with the majority falling within the range of adequate blood flow
(47.95%). Fig. 6 showcases the visualization of an audio sample in its
original form, as well as $w_{L1}$, $w_{L2}$, and $w_{L3}$, respectively. Each
level progressively filters out more information from the higher frequency
band.

Table 1: Demographic information of recruit patients Items | Values
---|---
Gender (n, %) |  Male |  102 |  (59.649)
Female |  69 |  (40.351)
Age (mean, SD) |  67.596 |  13.279
HTN (n, %) |  Yes |  124 |  (72.515)
No |  47 |  (27.485)
DM (n, %) |  Yes |  111 |  (64.912)
No |  60 |  (35.088)
Blood Flow (ml/min) (n, %) |  $<$ 750 |  45 |  (26.316)
750-1500 |  82 |  (47.953)
$>$ 1500 |  44 |  (25.731)
HTN: diagnosed with hypertension; DM: diagnosed with diabetes mellitus. n:
number of recruit patients; SD: standard deviation.

Figure 6: Wavelet transform from level 1 to 3. $w_{L1}$: one-level low-pass
filtered coefficients; $w_{L2}$: two-level low-pass filtered coefficients;
$w_{L3}$: three-level low-pass filtered coefficients.

By comparing Table 2(a) (n = 171) with (b) (n = 2,394), it becomes evident
that augmenting the dataset through the noise-mixing approach proves to be
efficient and significantly enhances the discrimination capabilities of the
latent representation. Furthermore, a comparison between Table 2(b) and (c)
highlights the impact of an asymmetric input and output. It is noteworthy that
the noise-to-clean scheme exhibits the ability to improve performance.
Employing a lower level of DWT does not necessarily lead to performance
improvement.

The most exceptional latent representation, which serves as the baseline for
subsequent comparisons, is generated by the DAE using $w_{L1}$, achieving an
average score of 0.95. The representations learned from the waveform, FFT, and
STFT of $w_{L1}$ were compared (presented in Table 3). The results indicate
that the waveform is the most viable feature for generating a discriminative
latent representation for downstream task classification.

Table 2: The effect of adding noise and different AE structure |  Original |  $w_{L1}$ |  $w_{L2}$ |  $w_{L3}$
---|---|---|---|---
|  (a) AE waveform (clean to clean)
AUROC |  0.508 |  0.498 |  0.601 |  0.655
Accuracy |  0.311 |  0.278 |  0.189 |  0.278
Sensitivity |  0.313 |  0.343 |  0.239 |  0.393
Specificity |  0.668 |  0.668 |  0.640 |  0.669
Precision |  0.304 |  0.277 |  0.233 |  0.287
F1 |  0.276 |  0.246 |  0.176 |  0.269
Avg. |  0.397 |  0.385 |  0.346 |  0.425
|  (b) AE waveform noise-mixing version (noisy to noisy)
AUROC |  0.990 |  0.977 |  0.989 |  0.976
Accuracy |  0.908 |  0.886 |  0.894 |  0.875
Sensitivity |  0.906 |  0.886 |  0.901 |  0.872
Specificity |  0.953 |  0.944 |  0.948 |  0.936
Precision |  0.910 |  0.893 |  0.905 |  0.872
F1 |  0.904 |  0.883 |  0.893 |  0.867
Avg. |  0.929 |  0.912 |  0.922 |  0.900
|  (c) DAE waveform noise-mixing version (noisy to clean)
AUROC |  0.993 |  0.987 |  0.989 |  0.990
Accuracy |  0.922 |  0.933 |  0.878 |  0.911
Sensitivity |  0.923 |  0.935 |  0.891 |  0.915
Specificity |  0.964 |  0.967 |  0.941 |  0.956
Precision |  0.915 |  0.932 |  0.884 |  0.909
F1 |  0.913 |  0.932 |  0.876 |  0.908
Avg. |  0.938 |  0.948 |  0.910 |  0.931
$w_{L1}$: one-level low-pass filtered coefficients; $w_{L2}$: two-level low-
pass filtered coefficients; $w_{L3}$: three-level low-pass filtered
coefficients. AE: autoencoder; DAE: denoising autoencoder; AUROC: area under
the receiver operating characteristic curve.
Table 3: Classification results between different features |  Original |  $w_{L1}$ |  $w_{L2}$ |  $w_{L3}$
---|---|---|---|---
|  DAE + waveform
AUROC |  0.993 |  0.987 |  0.989 |  0.990
Accuracy |  0.922 |  0.933 |  0.878 |  0.911
Sensitivity |  0.923 |  0.935 |  0.891 |  0.915
Specificity |  0.964 |  0.967 |  0.941 |  0.956
Precision |  0.915 |  0.932 |  0.884 |  0.909
F1 |  0.913 |  0.932 |  0.876 |  0.908
Avg. |  0.938 |  0.948 |  0.910 |  0.931
|  DAE + FFT
AUROC |  0.909 |  0.964 |  0.943 |  0.934
Accuracy |  0.772 |  0.864 |  0.869 |  0.839
Sensitivity |  0.772 |  0.863 |  0.869 |  0.834
Specificity |  0.888 |  0.932 |  0.935 |  0.919
Precision |  0.770 |  0.866 |  0.872 |  0.840
F1 |  0.762 |  0.860 |  0.863 |  0.832
Avg. |  0.812 |  0.892 |  0.892 |  0.866
|  DAE + STFT
AUROC |  0.923 |  0.926 |  0.904 |  0.832
Accuracy |  0.786 |  0.794 |  0.775 |  0.622
Sensitivity |  0.794 |  0.795 |  0.780 |  0.622
Specificity |  0.893 |  0.897 |  0.890 |  0.812
Precision |  0.783 |  0.791 |  0.787 |  0.618
F1 |  0.781 |  0.789 |  0.770 |  0.607
Avg. |  0.827 |  0.832 |  0.818 |  0.685
FFT: fast fourier transform; STFT: short time fourier transform; DAE:
denoising autoencoder; $w_{L1}$: one-level low-pass filtered coefficients;
$w_{L2}$: two-level low-pass filtered coefficients; $w_{L3}$: three-level low-
pass filtered coefficients. DAE: denoising autoencoder; AUROC: area under the
receiver operating characteristic curve.

Table 4(a) presents the outcomes obtained by applying previously proposed
methods in our scenario. However, none of these methods exhibited sufficient
distinctiveness for AVF blood flow detection. In Table 4(b), we showcase the
downstream task performance based on the representation of individual sites as
well as the subtraction of site 2-3. While each individual site achieved a
satisfactory accuracy of 0.90, the subtraction of site 2-3 surpassed other
combinations, such as concatenation and addition. Table 4(c) provides an
overview of the performance when patient-specific information was set as the
classification target. All approaches yielded commendable performance,
surpassing an average score of 0.93.

Utilizing the original latent representation (_dim_ = 100) as a reference,
Table 4(d) exhibits the outcomes obtained by reducing the dimensionality to 5,
4, and 2. As the dimensionality decreased, the performance also declined.
However, when the condensed representation (_dim_ = 2) is concatenated with
the demographic information, the performance can be restored to a level
approximating the best-performing version using LightGBM, as illustrated in
Table 4(e). Fig. 7 provides a visualization of the feature importance as
determined by the tree-based algorithm employed for branching. The results
indicate that the model places the highest value on the condensed
representation, followed by age, the presence of DM, gender, and HTN
diagnosis.

Table 4: Additional examinations (a) Feature extraction from previous research
(based on $w_{L1}$ of site 2).
---
|  S-transform [13] |  IMF [14] |  Mel spectrogram [26]
AUROC |  0.639 |  0.623 |  0.656
Accuracy |  0.443 |  0.365 |  0.513
Sensitivity |  0.451 |  0.385 |  0.517
Specificity |  0.725 |  0.689 |  0.761
Precision |  0.463 |  0.472 |  0.528
F1 |  0.440 |  0.360 |  0.509
Avg. |  0.527 |  0.482 |  0.581
(b) Validation results based on single and multiple sites (based on $w_{L1}$).
|  Site 2 |  Site 3 |  Site 2-3
AUROC |  0.990 |  0.981 |  0.987
Accuracy |  0.906 |  0.903 |  0.933
Sensitivity |  0.906 |  0.906 |  0.935
Specificity |  0.953 |  0.952 |  0.967
Precision |  0.910 |  0.904 |  0.932
F1 |  0.903 |  0.901 |  0.932
Avg. |  0.928 |  0.925 |  0.948
(c) Classification on patient information.
|  Gender |  HTN |  DM
AUROC |  0.990 |  0.996 |  0.993
Accuracy |  0.920 |  0.950 |  0.923
Sensitivity |  0.887 |  0.933 |  0.923
Specificity |  0.951 |  0.968 |  0.927
Precision |  0.947 |  0.966 |  0.934
F1 |  0.914 |  0.947 |  0.925
Avg. |  0.935 |  0.960 |  0.937
(d) PCA results (based on $w_{L1}$ of site 2-3)
|  _dim_ = 5 |  _dim_ = 4 |  _dim_ = 2
AUROC |  0.887 |  0.879 |  0.770
Accuracy |  0.756 |  0.747 |  0.614
Sensitivity |  0.765 |  0.747 |  0.605
Specificity |  0.880 |  0.874 |  0.805
Precision |  0.761 |  0.751 |  0.624
F1 |  0.750 |  0.740 |  0.586
Avg. |  0.800 |  0.790 |  0.667
(e) Concatenated with demographic information
|  SVM |  KNN |  LightGBM
AUROC |  0.768 |  0.928 |  0.957
Accuracy |  0.569 |  0.842 |  0.906
Sensitivity |  0.580 |  0.838 |  0.904
Specificity |  0.787 |  0.918 |  0.952
Precision |  0.592 |  0.850 |  0.915
F1 |  0.556 |  0.836 |  0.900
Avg. |  0.642 |  0.869 |  0.922
Site 1: site of arteriovenous anastamosis; Site 2: arterial puncture site;
Site 3: venous puncture site; Site 2-3: subtracting the latent representation
of site 3 from site 2. IMF: intrinsic mode function; HTN: diagnosed with
hypertension; DM: diagnosed with diabetes mellitus; _dim_ : condensed
dimensionality; SVM: support vector machine, KNN: k-nearest neighbors;
LightGBM: light gradient-boosting machine; $w_{L1}$: one-level low-pass
filtered coefficients; AUROC: area under the receiver operating characteristic
curve. Note that in (e) the concatenation was based on the latent features of
$w_{L1}$ of site 2-3 condensing to _dim_ = 2.

Figure 7: Feature importance of LightGBM. LightGBM: light gradient-boosting
machine; S1: dimensional 1 of the condensed signal; S2: dimensional 2 of the
condensed signal; HTN: diagnosed with hypertension; DM: diagnosed with
diabetes mellitus.

##  4 Discussion

Prior investigations [32] have underscored the perils of training deep neural
network models directly on the supervised target through gradient descent, as
random initialization may not yield optimal performance. Conversely,
commencing the training process with a pre-trained model has demonstrated its
efficacy in enhancing generalization. In our study, we employed DAEs to
generate a distinctive representation well-suited for detecting blood flow.
Our findings indicate that representation learning presents a more viable
approach for extracting auscultation features in AVF. Feature extraction
methods based on signal characteristics may exhibit high specificity to
particular prediction scenarios and lack ease of transferability to other
contexts. In contrast, the acquired latent representation showcases improved
generalization and applicability in non-extreme contrast scenarios (e.g.,
stenosis and non-stenosis) as well as other patient-specific characteristics.
While our study did not simultaneously collect different access indicators,
such as RI or luminal diameter, to validate transferability, our findings
suggest that a well-learned representation captures additional patient-
specific information that can be transferred to multiple tasks. Furthermore,
AVF blood flow serves as a more comprehensive measurement aligning with the
need for early surveillance and supporting the detection of stenosis and other
dysfunctions [22, 17]. Additionally, we have successfully applied the proposed
architecture to pathological voice quality detection, yielding satisfactory
outcomes [43].

Effective representation learning necessitates a sufficient amount of data.
Despite our relatively large sample size of recruited patients, the original
clean signal alone was insufficient to generate a well-learned representation.
However, augmentation methods, such as the noise-mixing approach, offer a
simple and feasible solution to overcome limitations in data size. Forcing the
model to reconstruct the clean signal from the noisy signal proves to be an
effective approach for generating a more representative representation. Our
results demonstrate that the time domain information captured in the waveform
is adequate for generating a well-learned representation, whereas the
frequency domain information and time-dependent windows converted using FFT
and STFT do not appear to be essential. Previous studies have also highlighted
the sufficiency of time domain information for turbulent sound analysis [44],
while additional information in the FFT window may introduce noise and lead to
averaging within [28]. The fixed window width of STFT may not be ideal for
accurately tracking dynamic signals [13]. Moreover, the inclusion of
additional, less informative details can impede precise reconstruction,
thereby generating a less representative representation. While a one-level DWT
discards less informative details, excessive information loss (e.g., $w_{L3}$)
hampers prediction performance.

The intensity of the bruit is most pronounced near the arterial anastomosis
(site 1), followed by the arterial and venous puncture sites (site 2 and 3).
Subtracting the latent representation of site 3 from site 2 effectively
indicates the blood flow loss in between, thus demonstrating distinguishable
results. Previous works [45, 46] have demonstrated the possibility of
reconstructing diverse images of real subjects through interpolations in
latent space, highlighting the feasibility of manipulating latent
representations to generate targeted outcomes.

Excessive dimensionality reduction at the bottleneck of AEs may hinder
reconstruction performance. Consequently, we opted to condense the
dimensionality after generating adequate representations. Our results
illustrate that prediction performance can be restored by concatenating a
vector of six elements using an appropriate machine learning method. The
concatenated vector comprises heterogeneous information, necessitating the
identification of a threshold to discriminate between different categories.
This task can be effectively handled by tree-based algorithms [47]. The
condensed representation continues to exhibit greater discriminative power
compared to other variables, as indicated by its high value in tree-based
algorithms. Furthermore, numeric variables tend to demonstrate higher
discriminative capability than categorical variables.

##  5 Conclusion

Our study showcased the effectiveness of representation learning using DAEs
for non-invasive AVF blood flow detection. This approach proved to be highly
accurate and capable of capturing patient-specific information, enabling its
application in various contexts. Furthermore, the learned representations
maintained high performance even under highly condensed conditions. The
manipulation of latent representations holds great promise for future
advancements. Further exploration of the generated latent representation can
enhance the development of smart stethoscopes and pave the way for future
applications.

## References

  * [1] R. Navuluri and S. Regalado, “The KDOQI 2006 vascular access update and fistula first program synopsis,” in _Seminars in interventional radiology_ , vol. 26, no. 02, 2009, pp. 122–124.
  * [2] M. C. Riella and P. Roy-Chaudhury, “Vascular access in haemodialysis: strengthening the achilles’ heel,” _Nature Reviews Nephrology_ , vol. 9, no. 6, pp. 348–357, 2013.
  * [3] A. A. Al-Jaishi, M. J. Oliver, S. M. Thomas, C. E. Lok, J. C. Zhang, A. X. Garg, S. D. Kosa, R. R. Quinn, and L. M. Moist, “Patency rates of the arteriovenous fistula for hemodialysis: A systematic review and meta-analysis,” _American Journal of Kidney Diseases_ , vol. 63, no. 3, pp. 464–478, 2014.
  * [4] N. Koirala, E. Anvari, and G. McLennan, “Monitoring and surveillance of hemodialysis access,” in _Seminars in interventional radiology_ , vol. 33, no. 01. Thieme Medical Publishers, 2016, pp. 025–030.
  * [5] D.-F. Yeih, Y.-S. Wang, Y.-C. Huang, M.-F. Chen, and S.-S. Lu, “Physiology-based diagnosis algorithm for arteriovenous fistula stenosis detection,” in _2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society_ , 2014, pp. 4619–4622.
  * [6] K. Abreo, B. M. Amin, and A. P. Abreo, “Physical examination of the hemodialysis arteriovenous fistula to detect early dysfunction,” _The Journal of Vascular Access_ , vol. 20, no. 1, pp. 7–11, 2019.
  * [7] R. Peralta, M. Garbelli, F. Bellocchio, P. Ponce, S. Stuard, M. Lodigiani, J. Fazendeiro Matos, R. Ribeiro, M. Nikam, M. Botler _et al._ , “Development and validation of a machine learning model predicting arteriovenous fistula failure in a large network of dialysis clinics,” _International Journal of Environmental Research and Public Health_ , vol. 18, no. 23, p. 12355, 2021.
  * [8] C. E. Lok, T. S. Huber, T. Lee, S. Shenoy, A. S. Yevzlin, K. Abreo, M. Allon, A. Asif, B. C. Astor, M. H. Glickman _et al._ , “KDOQI clinical practice guideline for vascular access: 2019 update,” _American Journal of Kidney Diseases_ , vol. 75, no. 4, pp. S1–S164, 2020.
  * [9] ——, “KDOQI clinical practice guideline for vascular access: 2019 update,” _American Journal of Kidney Diseases_ , vol. 75, no. 4, pp. S1–S164, 2020.
  * [10] A. M. Noor, “Non-imaging acoustical properties in monitoring arteriovenous hemodialysis access. a review.” _Journal of Engineering & Technological Sciences_, vol. 47, no. 6, 2015.
  * [11] P. O. Vesquez, M. M. Marco, and B. Mandersson, “Arteriovenous fistula stenosis detection using wavelets and support vector machines,” in _2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society_ , 2009, pp. 1298–1301.
  * [12] D. Higashi, K. Nishijima, K. Furuya, K. Tanaka, and S. Shin, “Classification of shunt murmurs for diagnosis of arteriovenous fistula stenosis,” in _2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)_ , 2018, pp. 665–669.
  * [13] H.-Y. Wang, C.-H. Wu, C.-Y. Chen, and B.-S. Lin, “Novel noninvasive approach for detecting arteriovenous fistula stenosis,” _IEEE Transactions on Biomedical Engineering_ , vol. 61, no. 6, pp. 1851–1857, 2014.
  * [14] P. Vásquez, M. M. Marco, E. Mattsson, and B. Mandersson, “Application of the empirical mode decomposition in the study of murmurs from arteriovenous fistula stenosis,” in _2010 Annual International Conference of the IEEE Engineering in Medicine and Biology_ , 2010, pp. 947–950.
  * [15] Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” _IEEE transactions on pattern analysis and machine intelligence_ , vol. 35, no. 8, pp. 1798–1828, 2013.
  * [16] D. Zhang, J. Yin, X. Zhu, and C. Zhang, “Network representation learning: A survey,” _IEEE transactions on Big Data_ , vol. 6, no. 1, pp. 3–28, 2018\.
  * [17] K. Konner, B. Nonnast-Daniel, and E. Ritz, “The arteriovenous fistula,” _Journal of the American Society of Nephrology_ , vol. 14, no. 6, pp. 1669–1680, 2003.
  * [18] N. Tessitore, G. Lipari, A. Poli, V. Bedogna, E. Baggio, C. Loschiavo, G. Mansueto, and A. Lupo, “Can blood flow surveillance and pre-emptive repair of subclinical stenosis prolong the useful life of arteriovenous fistulae? a randomized controlled study,” _Nephrology Dialysis Transplantation_ , vol. 19, no. 9, pp. 2325–2333, 2004.
  * [19] N. Tessitore, V. Bedogna, A. Poli, W. Mantovani, G. Lipari, E. Baggio, G. Mansueto, and A. Lupo, “Adding access blood flow surveillance to clinical monitoring reduces thrombosis rates and costs, and improves fistula patency in the short term: a controlled cohort study,” _Nephrology Dialysis Transplantation_ , vol. 23, no. 11, pp. 3578–3584, 2008.
  * [20] G. A. Miller and W. W. Hwang, “Challenges and management of high-flow arteriovenous fistulae,” in _Seminars in nephrology_ , vol. 32, no. 6. Elsevier, 2012, pp. 545–550.
  * [21] P. N. Suding and S. E. Wilson, “Strategies for management of ischemic steal syndrome,” in _Seminars in vascular surgery_ , vol. 20, no. 3. Elsevier, 2007, pp. 184–188.
  * [22] P. Mccarley, R. L. Wingard, Y. Shyr, W. Pettus, R. M. Hakim, and T. A. Ikizler, “Vascular access blood flow monitoring reduces access morbidity and costs,” _Kidney international_ , vol. 60, no. 3, pp. 1164–1172, 2001.
  * [23] W.-L. Chen, C.-D. Kan, and C.-H. Lin, “Arteriovenous shunt stenosis evaluation using a fractional-order fuzzy petri net based screening system for long-term hemodialysis patients,” _Journal of Biomedical Science and Engineering_ , vol. 2014, 2014.
  * [24] M. Grochowina and L. Leniowska, “Design and implementation of a device supporting automatic diagnosis of arteriovenous fistula,” in _2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)_ , 2018, pp. 186–190.
  * [25] Y.-N. Wang, C.-Y. Chan, and S.-J. Chou, “The detection of arteriovenous fistula stenosis for hemodialysis based on wavelet transform,” _International Journal of Advanced Computer Science_ , vol. 1, no. 1, pp. 16–22, 2011.
  * [26] J. H. Park, I. Park, K. Han, J. Yoon, Y. Sim, S. J. Kim, J. Y. Won, S. Lee, J. H. Kwon, S. Moon, G. M. Kim, and M.-D. Kim, “Feasibility of deep learning-based analysis of auscultation for screening significant stenosis of native arteriovenous fistula for hemodialysis requiring angioplasty,” _Korean J Radiol. 2022 Oct;23(10):949-958._ , vol. 10, 2022.
  * [27] M. M. Munguía and B. Mandersson, “Analysis of the vascular sounds of the arteriovenous fistula’s anastomosis,” in _2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society_ , 2011, pp. 3784–3787.
  * [28] M. Grochowina, K. Wojnar, and L. Leniowska, “An application of wavelet transform for non-invasive evaluation of arteriovenous fistula state,” in _2019 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)_ , 2019, pp. 214–217.
  * [29] K. Ota, Y. Nishiura, S. Ishihara, H. Adachi, T. Yamamoto, and T. Hamano, “Evaluation of hemodialysis arteriovenous bruit by deep learning,” _Sensors_ , vol. 20, no. 17, 2020.
  * [30] K.-H. Tsai, W.-C. Wang, C.-H. Cheng, C.-Y. Tsai, J.-K. Wang, T.-H. Lin, S.-H. Fang, L.-C. Chen, and Y. Tsao, “Blind monaural source separation on heart and lung sounds based on periodic-coded deep autoencoder,” _IEEE Journal of Biomedical and Health Informatics_ , vol. 24, no. 11, pp. 3203–3214, 2020.
  * [31] G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” _Science_ , vol. 313, no. 5786, pp. 504–507, 2006\.
  * [32] P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, P.-A. Manzagol, and L. Bottou, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion.” _Journal of machine learning research_ , vol. 11, no. 12, 2010.
  * [33] X. Lu, Y. Tsao, S. Matsuda, and C. Hori, “Speech enhancement based on deep denoising autoencoder.” in _Interspeech_ , vol. 2013, 2013, pp. 436–440.
  * [34] Transonic, “HD03 hemodialysis monitor.” [Online]. Available: https://www.transonic.com/hd03-monitor
  * [35] Y.-J. Lu, C.-F. Liao, X. Lu, J.-w. Hung, and Y. Tsao, “Incorporating broad phonetic information for speech enhancement,” _arXiv preprint arXiv:2008.07618_ , 2020.
  * [36] S.-Y. Chuang, H.-M. Wang, and Y. Tsao, “Improved lite audio-visual speech enhancement,” _IEEE/ACM Transactions on Audio, Speech, and Language Processing_ , vol. 30, pp. 1345–1359, 2022.
  * [37] B. Scholkopf, K.-K. Sung, C. Burges, F. Girosi, P. Niyogi, T. Poggio, and V. Vapnik, “Comparing support vector machines with gaussian kernels to radial basis function classifiers,” _IEEE Transactions on Signal Processing_ , vol. 45, no. 11, pp. 2758–2765, 1997.
  * [38] W. S. Noble, “What is a support vector machine?” _Nature biotechnology_ , vol. 24, no. 12, pp. 1565–1567, 2006.
  * [39] D. Laszuk, “Python implementation of empirical mode decomposition algorithm,” https://github.com/laszukdawid/PyEMD, 2017.
  * [40] M.-L. Zhang and Z.-H. Zhou, “ML-KNN: A lazy learning approach to multi-label learning,” _Pattern Recognition_ , vol. 40, no. 7, pp. 2038–2048, 2007.
  * [41] G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y. Liu, “LightGBM: A highly efficient gradient boosting decision tree,” _Advances in neural information processing systems_ , vol. 30, 2017.
  * [42] R. Parikh, A. Mathai, S. Parikh, G. C. Sekhar, and R. Thomas, “Understanding and using sensitivity, specificity and predictive values,” _Indian journal of ophthalmology_ , vol. 56, no. 1, p. 45, 2008.
  * [43] L.-C. Chen, Y.-H. Lin, W.-H. Tseng, C.-T. Tan, and Y. Tsao, “Autoencoder-based pathological voice classification,” 2023. [Online]. Available: https://ishiou.github.io/PathologyVoice/initialresults.html
  * [44] Y. Akay, M. Akay, W. Welkowitz, J. Semmlow, and J. Kostis, “Noninvasive acoustical detection of coronary artery disease: a comparative study of signal processing methods,” _IEEE Transactions on Biomedical Engineering_ , vol. 40, no. 6, pp. 571–578, 1993.
  * [45] C. Donahue, Z. C. Lipton, A. Balsubramani, and J. McAuley, “Semantically decomposing the latent spaces of generative adversarial networks,” 2018.
  * [46] T. Sainburg, M. Thielk, B. Theilman, B. Migliori, and T. Gentner, “Generative adversarial interpolative autoencoding: adversarial training on latent space interpolations encourage convex latent distributions,” 2019.
  * [47] C. Kern, T. Klausch, and F. Kreuter, “Tree-based machine learning methods for survey research,” in _Survey research methods_ , vol. 13, no. 1. NIH Public Access, 2019, p. 73.