[orcid=0000-0003-1940-9968]

[1]

1]organization=School of Computer Science, Technological University Dublin,
city=Dublin, country=Ireland

[]

2]organization=School of Computer Science, University College Dublin,
city=Dublin, country=Ireland

[]

[1]Corresponding author

# Exploring the impact of noise and degradations on heart sound classification
models

Davoud Shariat Panah<EMAIL_ADDRESS>[  Andrew Hines
<EMAIL_ADDRESS>[  Susan McKeever<EMAIL_ADDRESS>
###### Abstract

The development of data-driven heart sound classification models has been an
active area of research in recent years. To develop such data-driven models in
the first place, heart sound signals need to be captured using a signal
acquisition device. However, it is almost impossible to capture noise-free
heart sound signals due to the presence of internal and external noises in
most situations. Such noises and degradations in heart sound signals can
potentially reduce the accuracy of data-driven classification models. Although
different techniques have been proposed in the literature to address the noise
issue, how and to what extent different noise and degradations in heart sound
signals impact the accuracy of data-driven classification models remains
unexplored. To answer this question, we produced a synthetic heart sound
dataset including normal and abnormal heart sounds contaminated with a large
variety of noise and degradations. We used this dataset to investigate the
impact of noise and degradation in heart sound recordings on the performance
of different classification models. The results show different noises and
degradations affect the performance of heart sound classification models to a
different extent; some are more problematic for classification models, and
others are less destructive. Comparing the findings of this study with the
results of a survey we previously carried out with a group of clinicians shows
noise and degradations that are more detrimental to classification models are
also more disruptive to accurate auscultation. The findings of this study can
be leveraged to develop targeted heart sound quality enhancement approaches -
which adapt the type and aggressiveness of quality enhancement based on the
characteristics of noise and degradation in heart sound signals.

###### keywords:

Phonocardiogram Heart sound Heart sound classification Noise and degradation
Quality enhancement Synthetic dataset

##  1 Introduction

Cardiovascular diseases are currently the leading cause of mortality
worldwide, accounting for one-third of deaths globally [1]. Early diagnosis
through pervasive approaches can help detect heart disease in patients at
earlier stages and consequently improve the survival rate. Auscultation has
been a cost-effective approach for pre-screening heart disease for over 200
years [2]. However, auscultation is a subjective practice and requires
extensive training [3]. Therefore, automatic analysis of heart sounds has been
presented as an alternative to auscultation for early pre-screening of heart
abnormalities. In recent years, a large variety of data-driven heart disease
diagnostic systems have been developed that can distinguish normal from
abnormal heart sounds [4, 5, 6, 7, 8].

Heart sounds are generally captured using digital stethoscopes or mobile
phones. Although such devices typically benefit from noise reduction and
cancellation technologies [9, 10, 11], they can still capture a considerable
amount of noise while recording heart sounds, especially in noisy
environments. Due to the presence of internal physiological body noises and
ambient artefacts in clinical and non-clinical settings, it is almost
impossible to record noise-free heart sound signals in real-world scenarios.

It has been stated that noise and contaminations in heart sound recordings can
reduce the performance of data-driven models [12, 13, 14, 15]. Researchers
have adopted different approaches to mitigate the negative impact of noise and
degradations in captured signals on the performance of data-driven heart
disease diagnostic systems. Heart sound quality enhancement is one of the most
widely adopted approaches to reducing noise in captured signals. A whole host
of enhancement techniques have been employed in the field, such as filtering
[4, 5] and wavelet-based denoising [14, 15, 16, 17]. Heart sound quality
classification is another approach in which a data-driven model distinguishes
low-quality heart sounds from good-quality ones [18, 19, 20]. Low-quality
signals are discarded, while heart sounds with an acceptable quality will
subsequently be used in heart disease diagnostic models.

While numerous methods have been proposed in the field to enhance or classify
the quality of heart sounds, it has remained unexplored how and to what extent
noise and degradation in heart sound signals can impact the overall accuracy
of data-driven models. A deeper understanding of the impact of noise and
degradations in heart sound recordings on the performance of the data-driven
models will allow us to adapt the heart sound capture process with the aim of
minimizing the negative impact of such noise and degradations. Also, such an
understanding will enable us to adjust the quality enhancement of the captured
heart sounds based on their noise content and develop targeted heart sound
pre-processing pipelines. In this regard, this study aims to answer the
following research question: how do noise and degradations in heart sound
signals impact the overall accuracy of data-driven models?

To answer this research question, we will produce a synthetic dataset
containing normal and abnormal heart sounds with various noises and
degradations. This dataset will then be employed to train and evaluate
multiple heart sound classification models. This will enable us to
systematically investigate the impact of a large variety of noises and
degradations on the performance of different heart sound classification
models. Previously we investigated the impact of noise and degradations on
heart sound signals’ diagnosability by conducting a survey with a group of
trained clinicians [21]. In this study, we will also observe the similarities
between the impact of noise and degradations on the performance of
classification models and the results of our previous survey.

The remainder of this paper is structured as follows: Section 2 overviews the
related work on the impact of noise and the application of synthetic datasets.
Section 3 provides the details of the datasets and data-driven models employed
in this study. In Section 4, the results are given. In Section 5, results are
discussed. Conclusions and future directions are presented in Section 6.

##  2 Related Work

###  2.1 Noise Impact

Noise and degradations with internal or external sources can have a negative
impact on auscultation. Shindler [22] has stated that high levels of
environmental sounds, such as speech, can interfere significantly with
auscultation. Coviello [23] has pointed out that noises due to muscular
movements can interfere with heart sounds and make it harder for clinicians to
perceive the salient characteristics of the heart sounds. In addition to
ambient and movement noises, internal physiological noises can also be
disruptive to auscultation. Ranganathan et al. [24] have emphasized that
intense breathing noises can interfere with assessing heart sounds and reduce
the accuracy of auscultation. In our previous work [21], we investigated the
impact of noise and degradations on the diagnosability of heart sound
recordings through a survey with clinicians. The results of this survey showed
that clinicians found ambient noises more disruptive than movement or internal
noises. Also, they indicated that long-duration noises are more problematic to
accurate auscultation than short-duration noises.

Noise and degradation in heart sound recordings can also reduce the accuracy
of data-driven classification models. Paul et al. [12] have indicated that
internal or external noises can mask fundamental heart sounds and increase the
false positives of the classifiers. According to Kumar et al. [13], noise in
heart sound recordings can alter the morphological characteristics of the
heart sounds and change the features salient to accurate diagnostics.
Gradolewski et al. [14] have stated that some heart sounds, such as late-
systolic and pan-systolic murmurs, have similar characteristics to noise, and,
as a result, applying denoising algorithms can decrease the misclassification
of such signals by data-driven models. Jain et al. [15] have indicated that
noise and degradations in heart sound signals can reduce the accuracy of the
segmentation of heart sounds into heartbeat cycles, which in turn can lead to
a sub-optimal heart sound classification model. Although it has been
emphasized that noise and degradation in heart sound signals can potentially
reduce the accuracy of classification models, we could not find any
comprehensive study exploring the impact of different noises and degradations
on classification models.

###  2.2 Diagnosability of Heart Sounds

In our previous work, we surveyed a group of thirteen clinicians to understand
the characteristics of diagnosable heart sounds and the impact of noises and
degradations on the diagnosability of heart sound recordings [21]. This survey
included a subjective listening test with 20 heart sound recordings
contaminated with different noises and degradations. Analyzing the results of
this survey showed that noise and degradations in heart sound signals have a
detrimental effect on the diagnosability of heart sounds. Most of the survey’s
respondents (92%) stated that a diagnosable heart sound recording must contain
at least six heartbeat cycles. In addition, the survey results showed that
clinicians found ambient noises more disruptive than movement or internal body
noises. Also, they indicated that continuous long-duration noises have a more
destructive impact on the diagnosability of heart sound recordings than
transient short-duration noises.

###  2.3 Synthetic Data

Synthetic data have been widely employed to develop and evaluate data-driven
models in different domains. According to Jordon et al. [25], synthetic data
refers to “data generated from a set of easy-to-specify distributions that is
used to validate a machine learning model”. Synthetic data can have different
use cases. Lack of access to large, annotated datasets is still a challenge in
many fields. As a result, in some studies, synthetic datasets were generated
to increase the number of available samples used to develop data-driven
models. Li et al. [26] produced a synthetic dataset by augmenting a natural
speech dataset with synthetic speech. They used this dataset to train a neural
speech recognition model. Severini et al. [27] generated a synthetic dataset
of cry sounds through acoustic scene simulation. They used this dataset to
train a data-driven model for the automatic detection of cry sounds in
neonatal intensive care units. Synthetic datasets have also been used to
produce generalizable data-driven models in cases where real-world datasets
are biased or lack diversity. For instance, Kortylewski et al. [28] generated
a synthetic dataset of facial images that includes a much larger variety of
parameters, such as lights and head poses, compared to available real-world
datasets. Synthetic data has also been used to address class imbalance problem
in some datasets, which can lead to sub-optimal data-driven models. For
example, Perez-Porras et al. [29] have developed different techniques to
balance the available samples by generating synthetic data for wildfire
prediction. Synthetic data generation is also a common practice in situations
where sharing real-world data is severely limited due to legal considerations
[30] or privacy issues [31]. Synthetic data also allows us to evaluate
different methods and algorithms in a controlled setting. In this regard,
another important application of synthetic data is in cases where we need to
study the impact of different variations in input data on the performance of
algorithms. For instance, Bolon-Canedo et al. [32] have used eleven synthetic
datasets to compare the performance of different feature selection methods.
These synthetic datasets vary in terms of non-linearity, noise level, number
of irrelevant and redundant features, etc. Having control over these variables
in generated datasets enabled them to study their impact on the performance of
feature selection algorithms more precisely and identify the advantages and
disadvantages of each algorithm.

##  3 Method

In this section, we provide the details of the methodology for this study. The
overall approach can be summarized as follows:

  * •

A synthetic heart sound dataset including normal and abnormal heart sounds
contaminated with a large variety of noise and degradations will be generated.
To generate this dataset, clean heart sounds will be mixed with noises of
different types, durations and groupings in different SNR levels.

  * •

The synthetic dataset will be split into train and test sets.

  * •

Multiple classification models will be developed. To develop these models,
different feature representations (log-spectrogram and mel-spectrogram) and
two commonly used classifiers in heart classification (support vector machine
and convolutional neural network) will be employed.

  * •

Support vector machine models will be trained using the synthetic training
set. Convolutional neural network models will be pre-trained using a dataset
called PhysioNet and then will be fine-tuned using the synthetic training set.

  * •

After training the models, we will use the synthetic test set to evaluate the
classification models.

  * •

To investigate the impact of noise and degradations in heart sound signals on
the performance of classification models, we will report the overall
accuracies of the models across heart sounds contaminated with different noise
types, durations, groupings and SNR levels.

In Section 3.1, we describe the process of generating the synthetic heart
sound dataset. Also, we provide the details of another heart sound dataset
called PhysioNet used in our experiments. Afterwards, in Section 3.2, we
explain the stages of developing heart sound classification models, including
pre-processing, feature extraction and classification.

Table 1: Details of the clean heart sound recordings used to generate the synthetic dataset Recording # | Type | Duration (s)
---|---|---
1 | Normal | 12.0
2 | Normal | 10.1
3 | Normal | 13.8
4 | Normal | 10.0
5 | Normal | 3.0
6 | Normal | 12.8
7 | Normal | 10.2
8 | Normal | 15.0
9 | Abnormal - Aortic regurgitation | 12.0
10 | Abnormal - Aortic stenosis | 10.9
11 | Abnormal - Mitral regurgitation | 12.0
12 | Abnormal - Mitral stenosis | 11.2
13 | Abnormal - Mitral valve prolapse | 11.5
14 | Abnormal - Mitral valve prolapse | 2.5
15 | Abnormal - S3 | 10.1
16 | Abnormal - S4 | 10.0
Table 2: Details of the noise types, their groupings and durations that have been mixed with clean heart sound recordings to generate the synthetic dataset Noise Type | Noise Grouping | Noise Duration
---|---|---
White | Color | Long
Pink | Color | Long
Red | Color | Long
Sensor movement | Movement | Short
Body movement | Movement | Short
Deep breathing | Internal | Long
Fast breathing | Internal | Long
Coughing | Internal | Short
Digestive sound | Internal | Short
Talking | Ambient | Long
Door open/close | Ambient | Short
Phone ringing | Ambient | Long
Music | Ambient | Long
Water flow | Ambient | Long
TV | Ambient | Long
Dishwasher | Ambient | Long
Washing machine | Ambient | Long
Kettle | Ambient | Long
Vacuum cleaner | Ambient | Long
Dog barking | Ambient | Short
Bird singing | Ambient | Long
Figure 1: (a) Phonocardiogram of a clean normal heart sound, (b)
Phonocardiogram of the same heart sound contaminated with dishwasher noise

###  3.1 Datasets

####  3.1.1 Synthetic Dataset

This section provides the details of the synthetic heart sound dataset used in
subsequent experiments and the stages of generating this dataset. Table 1
summarizes the specifications of the clean heart sounds, including their types
and durations used to produce the synthetic dataset.

As shown in Table 1, we collected multiple clean normal, and abnormal heart
sounds from different resources such as publicly available datasets and
YouTube. There are sixteen clean heart sounds, consisting of eight normal and
eight abnormal recordings. Abnormal recordings include more common murmurs and
extra heart sounds. We chose abnormalities that are more prevalent in publicly
available heart sound datasets. As shown in Table 1, out of these sixteen
recordings, fourteen signals are over 10 seconds long, and the duration of the
other two signals is 2.5-3.0 seconds. Given that we will explore the impact of
heart sound duration on the accuracy of the classification models, we included
both short- and long-duration signals. Short-duration recordings are long
enough to include at least two heartbeat cycles, but, at the same time, they
are significantly shorter than the majority of the signals. We assessed the
quality of these heart sound recordings through listening and visual
inspection of the waveforms to ensure they are noise-free or contain a very
low noise level.

Twenty-one different noise types were mixed with each of the base clean heart
sound recordings. To have a comprehensive set of noise types, we chose the
noise types that are common in clinical and non-clinical environments, such as
home-places. Table 2 summarizes the details of the noise types, their
groupings and durations. Noise types are categorized into four groups based on
their source: color, movement, internal, and ambient. Color noises were
generated through simulation, while internal and ambient noises were collected
from different publicly available datasets. Movement noises were captured
using a mobile phone from the body surface. Regarding the ambient noises,
noise types prevalent in clinical and non-clinical environments such as
homeplaces were used. Each clean heart sound was additively mixed with each
noise contamination in ten different SNR levels: -10, -5, 0, 5, 10, 15, 20,
25, 30, and 40. As shown in Table 2, these noise types are also categorized in
terms of length into short- and long-duration noises. In the case of short-
duration noises, they were mixed with heart sounds at random locations. Figure
1 (a) illustrates the phonocardiogram of a clean normal heart sound, and
Figure 1 (b) shows the phonocardiogram of the same heart sound contaminated
with dishwasher noise where SNR is equal to 10.

Using the process described above, 3360 synthetic heart sound recordings were
generated. The synthetic dataset includes 210 noisy permutations for each
clean heart sound recording. The specifications of the synthetic train and
test sets are as follows:

  * •

Half of the samples in the synthetic dataset (1680 recordings) are placed in
the train set, and the other half in the test set.

  * •

Train and test sets contain noisy permutations of different clean heart sound
recordings: noisy permutations of heart sound numbers 1, 3, 6, 8, 9, 11, 12
and 15 are placed in the train set, while noisy permutations of heart sound
numbers 2, 4, 5, 7, 10, 13, 14 and 16 are placed in the test set. The details
of these base clean heart sound recordings have been provided in Table 1.

  * •

Train and test sets are balanced across the two classes (normal and abnormal).

  * •

The train set contains only long-duration recordings, while the test set
contains both short- and long-duration signals.

  * •

Noise types are the same across the train and test sets and include all
twenty-one noise types, as summarized in Table 2.

  * •

SNR levels are the same across the train and test sets and include ten levels:
-10, -5, 0, 5, 10, 15, 20, 25, 30, and 40.

A synthetic heart sound dataset offers several advantages over publicly
available datasets for our use case. First, by synthetically adding noise to
heart sound signals, we can generate recordings contaminated with a large
variety of noise types common in both clinical and non-clinical settings.
Also, by using different SNR levels, we can control the intensity of noise
contamination in each of the recordings, allowing us to generate samples with
various noise levels, from roughly clean to very noisy. Such a controlled
synthetic setting enables us to thoroughly investigate the impact of different
noise variables, such as noise types, groupings, intensities, and durations,
on the performance of data-driven models. To date, publicly available heart
sound datasets have been mainly captured in controlled environments, and
recordings of such datasets are not diverse enough in terms of noise types and
intensities. Also, it is very difficult to accurately measure the amount of
noise in heart sound recordings in real-world datasets, and as a result, it
would not be possible to provide a detailed analysis of the impact of noise
and degradations on the performance of data-driven models using such datasets.

####  3.1.2 PhysioNet Dataset

The PhysioNet heart sound dataset [33] was published as part of the
PhsyioNet/Computing in Cardiology 2016 challenge. This dataset comprises six
smaller subset datasets that different research groups collected across the
world in controlled or uncontrolled environments. PhysioNet dataset contains
3240 heart sound recordings, out of which 2575 samples were captured from
healthy subjects while 665 samples were collected from pathologic subjects.
Some of the recordings were labelled as unsure, which means that they were too
noisy to be categorized as normal or abnormal. For this study, we excluded
these low-quality recordings from the dataset. It should be noted that this
dataset does not provide any information regarding the noise content (e.g.,
noise type and intensity) of the recordings. In the last few years, PhysioNet
dataset has been widely employed as the largest publicly available heart sound
dataset to develop data-driven heart sound classification models. This dataset
is used in our experiments for pre-training deep learning models.

Figure 2: Heart sound classification pipeline.

###  3.2 Data-driven Models

As shown in Figure 2, developing data-driven models for heart sound
classification includes three steps: pre-processing heart sound recordings,
extracting features from heart sounds, and training classification models.
This section provides the details of these three stages.

####  3.2.1 Pre-processing

In the pre-processing stage, long-duration heart sound recordings are split
into 5- or 10-second segments and short-duration recordings are zero-padded to
have similar durations. 10-second recordings are later used to develop support
vector machine models, and 5-second ones are employed to develop convolutional
neural network models. Given that deep learning models generally need a large
number of samples for training, splitting the recordings into 5-seconds
segments increases the number of available samples for training the deep
learning models. Then, amplitude normalization is performed to minimize the
variations in amplitudes across the signals, using the following equation (as
in Ref. [16]):

$X_{norm}(t)=\ \frac{X(t)}{Max\ (\left|X\right|)}$ (1)

In the above equation, $X\left(t\right)$ represents the value of the heart
sound signal at the time t, and $\operatorname{Max}(|\mathrm{X}|)$ is the
maximum of the absolute value of the heart sound signal.

####  3.2.2 Feature Extraction

After pre-processing the recordings, Linear- and Mel-scaled Short-Time Fourier
Transform (STFT) features are extracted from signals. STFT is the most widely
used time-frequency feature representation for heart sound classification
[34]. This feature representation is computed using the following equation
[35]:

$X(m,\omega)=\sum_{n=-\infty}^{\infty}x[n]\omega[n-m]e^{-i\omega n}$ (2)

Where $x[n]$ is the signal to be transformed and $\omega[n]$ is the window
function (Hann window). After computing the STFT of the signals, spectrogram
representations were computed using the following equation:

$S(m,w)=|X(m,w)|^{2}$ (3)

The spectrogram gives the power of the signal for each time and frequency
pair. We used Log-spectrograms as well as Mel-spectrograms as our two feature
representations. Mel-spectrogram is computed by converting the linear
frequency scale to the Mel scale using the following formula [36]:

$\operatorname{Mel}(f)=2595\log_{10}\left(1+\frac{f}{700}\right)$ (4)

We used Librosa library [37] to extract the above features. Window and hop
lengths were fixed at 256 and 128, respectively. As for Mel-spectrograms, 64
Mel bands were used. In order to reduce the computational cost of training the
support vector machine models, the average values of the features across the
time axis were computed (as in Ref. [38]).

Figure 3: Architecture of the CNN model with Mel-spectrogram as input (Mel-CNN
model)

####  3.2.3 Classification

After extracting the feature representations, they are used to train the
classifiers. Two different classifiers are employed in this study: Support
Vector Machine (SVM) and Convolutional Neural Network (CNN). Both classifiers
have been frequently used in the field to develop heart sound classification
models. Given that we use Log-spectrogram and Mel-spectrogram as input
features for these classifiers, four different models are developed: a) Log-
SVM, b) Mel-SVM, c) Log-CNN, and d) Mel-CNN.

For the SVM models, we implemented a linear SVM with default parameters as
provided in the Scikit-Learn library [39]. The synthetic dataset was used to
train and evaluate the SVM models.

CNN models were implemented using TensorFlow deep learning library. Figure 3
shows the architecture of the CNN model with the Mel-spectrogram as input.
This model consists of three convolutional layers. The first, second and third
convolutional layers have 16, 32 and 64 kernels, respectively. A kernel size
of (3, 3) was used for all three convolutional layers. Also, the stride was
fixed at 1, and the ReLu function was used as the activation function. Each
convolutional layer is followed by a max-pooling layer with a pool size of (2,
2). To reduce overfitting of the models, a dropout layer with a rate of 0.5
was used after each max-pooling layer. After convolutional and max-pooling
layers, a fully connected layer with 100 neurons and the ReLu activation
function was used. After this fully connected layer, another dropout layer
with a rate of 0.5 is placed. The final layer of this architecture is Softmax
which outputs the probability distributions of the potential outcomes (normal
or abnormal).

To train the models, Adam optimization [40] with a learning rate of 0.001 and
cross-entropy objective function were used. CNN models were first pre-trained
on the PhysioNet dataset for 60 epochs. This way, we can ensure that the CNN
models are pre-trained on a large variety of normal and abnormal heart sounds.
Then, all layers except fully connected layers were frozen, and the models
were fine-tuned using the synthetic training set for 10 epochs. The trained
CNN models were evaluated on the synthetic test set. This process was repeated
ten times, and average and standard deviation values were reported for each
metric.

It is worth noting that we first tried to train CNN models from scratch
without pre-training using only the synthetic dataset. However, we observed
those models were overfitting the synthetic dataset to an extreme extent. As a
result, we excluded them from this study.

####  3.2.4 Evaluation Metrics

The performance of the models is measured using two different metrics. The
first one is recall which is used to quantify the performance of models across
each class and calculated using the following formula:

$\text{ Recall }=\frac{TP}{TP+FN}$ (5)

Given that the synthetic test set used to evaluate models is balanced across
normal and abnormal classes, we also use accuracy to measure the overall
performance of the classification models. Overall accuracy is computed as
follows:

$\text{ Accuracy }=\frac{TP+TN}{TP+TN+FP+FN}$ (6)

In the above equations, TP, FP, TN, and FN are the number of true positive,
false positive, true negative, and false negative samples in the results test
set, respectively.

Table 3: Performance of the classification models on the synthetic test set Model |  | Recall
---
(Normal) %
| Recall
---
(Abnormal) %
Accuracy %
Log-SVM | 67.9 | 73.9 | 70.9
Mel-SVM | 68.8 | 75.6 | 72.2
Log-CNN | 89.4 $\pm$ 1.3 | 62.6 $\pm$ 1.8 | 76.0 $\pm$ 0.4
Mel-CNN | 82.3 $\pm$ 0.5 | 83.1 $\pm$ 1.1 | 82.7 $\pm$ 0.4
Figure 4: Accuracy of the classification models across short- and long-
duration heart sound recordings

##  4 Results

###  4.1 Classification Models’ Overall Performance

Table 3 summarizes the results of evaluating classification models on the
synthetic test set.

As shown in Table 3, CNN models outperform SVM models in terms of overall
accuracy. The Mel-CNN model achieves the highest accuracy and recall for the
abnormal class, while the Log-CNN model achieves the highest recall for the
normal class.

###  4.2 Noise and Degradation Impact

In this section, we explore the impact of signal duration, noise type, noise
grouping, noise duration and SNR on the accuracy of the classification models.

####  4.2.1 Signal Duration

As mentioned in Section 3.1.1, the synthetic dataset contains short- (2.5-3.0
seconds) and long-duration (over 10 seconds) recordings. This dataset was
split into training and test sets. The training set contains only long-
duration signals, while the test set used to evaluate the classification
models includes both short- and long-duration recordings. Figure 4 depicts the
impact of signal duration on the accuracy of the classification models.

As shown in Figure 4, the overall accuracies of the models are considerably
lower on short-duration heart sounds compared to long-duration ones. This drop
in the performance of the models is more extreme for CNN models compared to
SVM models. In other words, the accuracies of CNN models are over 80% on long-
duration signals, while they fall below 50% when these models are evaluated
using short-duration signals.

Figure 5: Accuracy of the classification models across heart sound recordings
contaminated with different noise types

####  4.2.2 Noise Type

As mentioned in Section 3.1.1, the synthetic dataset contains normal and
abnormal heart sounds contaminated with twenty-one noise types. Figure 5
illustrates the overall accuracy of the models evaluated using heart sound
signals contaminated with different noise types. In this plot, the noise types
have been arranged based on the average accuracy of the classification models,
from the noise type with the lowest (white noise) to the one with the highest
(sensor movement noise) average accuracy. There are 80 samples in the test set
for each noise type.

As shown in Figure 5, the classification models show different accuracies for
heart sounds contaminated with different noise types. Also, we can observe
that SVM models show larger fluctuations across different noise types than CNN
models, indicating they are more sensitive to noise type than CNN models. We
can see that for some noise types, like TV or dishwasher noise, SVM models
offer the lowest accuracies (below 55%), while for some others, like deep
breathing and phone ring noise, they achieve much higher accuracies (over
85%).

Figure 6: Accuracy of the classification models across heart sounds
contaminated with noises of four different groups

####  4.2.3 Noise Grouping

As summarized in Table 2, noise types in synthetic heart sounds are
categorized into four groups: color, movement, internal and ambient.

Figure 6 compares the accuracy of the classification models across these noise
groupings. As shown in Figure 6, all classification models show their lowest
performance on the recordings contaminated with color noises while performing
best on the ones with movement noises. All models are sensitive to noise
grouping, showing different accuracies across different noise groupings.

Figure 7: Accuracy of the classification models across heart sounds
contaminated with short- and long-duration noises

####  4.2.4 Noise Duration

Noise contaminations used to generate the synthetic dataset can be categorized
in terms of length into short- and long-duration noises (as specified in Table
2). Figure 7 compares the accuracy of the classification models across
recordings contaminated with short- and long-duration noises.

As shown in Figure 7, all models are sensitive to noise duration and show
lower accuracies when evaluated using signals contaminated with long-duration
noises compared to short-duration noises.

Figure 8: Accuracy of the classification models across heart sounds with
different SNR levels (from -10 to 40)

####  4.2.5 SNR

As discussed in Section 3.1.1, in order to generate the synthetic dataset,
clean heart sounds were mixed with noise contaminations with different SNR
levels (from -10 to +40). For each SNR level, 168 recordings are available in
the synthetic test set. Figure 8 depicts the accuracy of the classification
models across heart sound recordings with different SNR levels.

As shown in Figure 8, Log-SVM and Mel-CNN models show a steady increase in
accuracy from SNR -10 to 40. However, in the case of Mel-SVM and Log-CNN
models, we observe an increase in accuracies from SNR -10 to 20, while for SNR
levels higher than 20, the accuracies are roughly unchanged.

Table 4: Mean, standard deviation, minimum and maximum accuracies for each classification model across different noise types Model |  Mean % |  SD % |  Min % |  Max %
---|---|---|---|---
Log-SVM |  70.9 |  9.5 |  53.8 |  85.0
Mel-SVM |  72.2 |  10.5 |  56.3 |  92.5
Log-CNN |  76.8 |  4.1 |  65.7 |  81.4
Mel-CNN |  82.9 |  5.9 |  72.9 |  92.9

##  5 Discussion

###  5.1 Classification Models’ Overall Performance

In Section 4.1 of the results, we presented the overall performance of the
classification models. The results show that the gap between the recalls of
the normal and abnormal classes is larger for the Log-CNN model compared to
the other models. This can indicate that the Log-CNN model is biased towards
the normal class. This bias suggests that the Log-CNN model is overfitting the
synthetic dataset. The synthetic dataset used to train the classification
models was produced using a relatively small number of base clean heart
sounds. At the same time, we should note that deep learning models such as
CNNs generally need a large amount of training data, making them more prone to
overfitting the synthetic dataset than SVM models. The reason why we only
observe this overfitting in the case of the Log-CNN model may be that the
higher dimensionality of the Log-spectrogram feature representation, at twice
that of the Mel-spectrogram, makes the Log-CNN model more prone to overfitting
than the Mel-CNN.

###  5.2 Noise and Degradation Impact

In Section 4.2.1, we discussed the impact of heart sound signal duration on
the performance of classification models. All models showed a lower
performance when evaluated using short-duration recordings (2.5-3.0 seconds)
than long-duration signals (5-10 seconds). This finding is in line with the
results of the survey we previously carried out with a group of clinicians
regarding the impact of noise and degradations on the diagnosability of heart
sound recordings [21]. The majority of the survey’s respondents (92%) stated
that they needed to listen to at least six heartbeat cycles before using the
recordings for diagnosis [21]. At the same time, according to Chen et al.
[41], an average heartbeat cycle is 0.8 seconds long, which means that the
short-duration recordings in the synthetic dataset include around three
heartbeat cycles on average. Short-duration heart sound signals, such as
1-second [6, 41], or 3 seconds [42, 43] recordings, have been used in the
field to develop data-driven heart sound classification models. However, the
results show that the classification models perform considerably worse on the
short-duration recordings, which suggests that short-duration signals should
be avoided in situations where we can capture heart sound signals with longer
durations.

In Section 4.2.2, we showed the performance of the classification models
across heart sounds contaminated with twenty-one different noise types. Table
4 summarizes the mean, standard deviation, minimum and maximum accuracies for
each classificaion model across different noise types. As shown in Table 4,
for all noise types, the overall accuracy of the CNN models is over 65%, while
in the case of SVM models, the accuracy goes below 57% for a few noise types.
We can also see that the standard deviations of average accuracies are larger
for SVM models compared to CNN models, which confirms the lower sensitivity of
CNN models to noise types. Some noise types have a more detrimental impact on
the overall accuracy of the models than the other ones. For example, for SVM
models, TV and dishwasher sounds are the most destructive noise types, while
for CNN models, pink and white noises are more problematic. However, what is
common between these classification models is that they all react to the noise
type, and they do not offer the same accuracy for all noise types. We can also
observe that there is only one short-duration noise type (digestive sound) in
the left half of Figure 5, while in the right half, we can see five short-
duration noises (door open/close, dog bark, body movement, cough, sensor
movement). Given that in this diagram, the noise types have been ordered based
on the average accuracy of the classification models, this observation
confirms that the continuous long-duration noises have a more detrimental
effect on the accuracy of the models than transient short-duration artifacts.
These findings suggest that different noise types should not be treated the
same way by quality enhancement algorithms, as each noise type influences the
classification models to a different extent.

In Section 4.2.3, we compared the performance of the classification models on
heart sounds contaminated with noises from different sources. We observed that
color and ambient noises are more problematic for all models than movement and
internal noises. This finding is also in agreement with the results of the
survey we previously carried out with a group of clinicians. Survey
respondents stated that ambient noises are more disruptive to accurate
auscultation than internal or movement noises [21]. They also stated that
internal and movement noises are roughly similar in terms of their negative
impact on the diagnosability of heart sounds [21]. The observation that
ambient and color noises have a more detrimental impact on the accuracy of the
classification models than other noise types demonstrates that heart sounds
contaminated by such noises should be prioritized over other noise sources for
quality enhancement.

In Section 4.2.4, we explored the impact of noise duration on the accuracy of
classification models. We saw that all models perform worse on heart sounds
contaminated with long-duration noises than the ones contaminated with short-
duration noises. These results are also aligned with the survey results from
our previous study. The survey results indicated that the average quality
rating for the heart sounds corrupted with long-duration noises was roughly
half of those with short-duration noises [21]. This is intuitively correct
given the longer duration of distortion of the heart signal and shows that
long-duration noises must be prioritized over short-duration noises for
quality enhancement.

In section 4.2.5, we investigated the impact of the SNR of the recordings on
the overall accuracy of the classification models. The results show that all
classification models benefit from increased SNR levels, albeit with earlier
plateauing for Mel-SVM and Log-CNN models. These results indicate that
applying noise reduction techniques with the aim of improving the SNR of the
recordings will have a beneficial effect on the accuracy of the classification
models. However, the level of performance gain will vary from model to model.

Based on the above discussion, the major findings of this study can be
summarized as follows: first, in some cases, it is possible to reduce the
negative impact of noise and degradation on the classification models at the
heart sound acquisition stage. For example, in Section 4.2.1, we observed that
classification models perform worse on short-duration heart sounds compared to
long-duration ones. Also, in Section 4.2.3, we saw that ambient noises have a
more detrimental impact on classification models’ accuracy than internal or
movement noises. By capturing long-duration signals or reducing ambient
noises, clinicians will be able to decrease the destructive impact of such
degradations on the performance of the classification models. Therefore,
clinicians can use this study’s results to adapt the heart sound capture
process to minimize the negative impact of such noises and degradations.
Second, in order to decrease the misclassification rate of classification
models, it is necessary to assess the captured heart sound signals in terms of
noise and degradation characteristics. For example, in Section 4.2.5, we
observed that classification models show a higher misclassification rate when
evaluated using the signals with lower SNR levels. Also, in Section 4.2.4, we
saw that long-duration noises have a more destructive impact on the accuracy
of the classification models compared to short-duration noises. Assessing the
characteristics of heart sound signals, such as SNR at the pre-processing
stage, allows us to discard low-quality heart sounds or adjust the quality
enhancement based on the noise characteristics of the signal. Quality
enhancement of the heart sound signals has been widely employed in the field
as a pre-processing step to develop heart sound classification models [4, 5,
16, 44]. However, quality enhancement algorithms have been universally applied
to heart sound recordings, irrespective of the characteristics of noise and
degradation in the signal. At the same time, it has been shown that universal
quality enhancement can reduce the performance of classification models [45].
The results of this study show that the characteristics of noise and
degradation in a heart sound recording determine how and to what extent the
classification models are influenced. In this regard, assessing the
characteristics of the noise and degradations in heart sound signals will
allow us to develop targeted quality enhancement techniques which adapt the
type and aggressiveness of quality enhancement depending on the noise content
of the signals and the employed classification model.

###  5.3 Limitations

This study has some potential limitations. Firstly, as discussed in Section
3.1.1, the synthetic heart sound dataset used to explore the impact of noises
and degradations on classification models was generated using a relatively
small collection of clean heart sounds. As we observed in Section 4.1, the
limited scale of the synthetic dataset is probably the reason why Log-CNN
model is biased towards the normal class. Although training this model using a
larger synthetic dataset could alleviate the overfitting issue, the similarity
of the observed trends in the results across deep learning and SVM models
indicates that the bias issue is not significant enough to impact the findings
of this study. Also, this study explored the impact of noises and degradations
on a small set of classification models. While a large variety of feature
representations and classifiers have been employed in the field to develop
heart sound classification models, in this study, we focused on the
classification models used most frequently. However, there is still a slight
possibility that other classification models react differently to noises and
degradations in heart sound signals. Finally, as discussed in Section 3.2, we
employed a segmentation-free heart sound classification pipeline, which means
that we used fixed-length signals to train and evaluate the classification
models. In other words, we did not apply segmentation algorithms to segment
heart sound recordings into heartbeat cycles. Therefore, the results of this
study may not be generalizable to the cases where segmentation algorithms are
used as one of the stages in the modelling pipeline.

##  6 Conclusion

Noise and degradation in heart sound recordings can reduce the accuracy of the
data-driven classification models. In this study, we investigated how and to
what extent different heart sound signal characteristics impact the
performance of the data-driven classification models. The characteristics of
heart sound signals that have been explored in this paper include signal
duration, noise type, noise grouping, noise duration and SNR of the
recordings. We observed that different noises and degradations do not
influence the performance of heart sound classification models in the same
way, which means that some are more problematic for data-driven models and
some are less destructive. Clinicians can use the findings of this study to
identify noise and degradations that are more problematic to classification
models and consequently adapt the heart sound capture process to reduce the
negative impact of those degradations.

Universal heart sound quality enhancement, which has been frequently employed
in the field as a pre-processing step, applies enhancement algorithms
irrespective of the noise characteristics of the signals. However, the results
of this study reinforce the importance of signal quality assessment in the
heart sound classification pipelines. Quality assessment enables us to analyze
the captured signals in terms of noise and degradations and limit the
application of quality enhancement algorithms to specific heart sounds which
do not meet a required quality threshold. The findings of this study can be
leveraged to develop targeted heart sound quality enhancement approaches which
adapt the type and aggressiveness of quality enhancement based on the
characteristics of noise and degradations in heart sound signals.

Comparing the findings of this study with the results of the survey we
previously carried out with a group of clinicians regarding the impact of
noise on the diagnosability of heart sounds shows clinicians and machine
learning models suffer from noise and degradations in a similar manner. We saw
a strong agreement between the impact of signal duration, noise grouping and
noise duration on the performance of classification models and diagnosability
of heart sounds from the point of view of clinicians. Therefore, the findings
from our previous survey are now backed up by the results of this study.

In future, we will extend this work by generating a larger synthetic dataset
using a more diverse set of base heart sounds. Also, we will include a larger
set of classification models to see if the results of this study hold for
other heart sound classification models as well. The results of this study
show that by analyzing the noise and degradation characteristics in heart
sound signals, we will be able to develop targeted quality enhancement
pipelines. As another future work, we will compare the universal and targeted
heart sound quality enhancement approaches to determine which approach leads
to a lower rate of misclassification in heart sound classification models.

## Acknowledgement

This work was conducted with the financial support of the Science Foundation
Ireland Centre for Research Training in Digitally-Enhanced Reality (d-real)
under Grant No. 18/CRT/6224. For the purpose of Open Access, the author has
applied a CC BY public copyright licence to any Author Accepted Manuscript
version arising from this submission.

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