=Paper=
{{Paper
|id=Vol-3715/paper3
|storemode=property
|title=CardioView: a framework for detection Premature Ventricular Contractions with eXplainable Artificial Intelligence
|pdfUrl=https://ceur-ws.org/Vol-3715/paper3.pdf
|volume=Vol-3715
|authors=Giuseppe Arienzo,Alessia Auriemma Citarella,Fabiola De Marco,Anna Maria De Roberto,Luigi Di Biasi,Rita Francese,Genoveffa Tortora
|dblpUrl=https://dblp.org/rec/conf/ini-dh/ArienzoCMRBFT24
}}
==CardioView: a framework for detection Premature Ventricular Contractions with eXplainable Artificial Intelligence==
https://ceur-ws.org/Vol-3715/paper3.pdf
CardioView: a framework for detection Premature
Ventricular Contractions with eXplainable Artificial
Intelligence
Giuseppe Arienzo1,† , Alessia Auriemma Citarella1,† , Fabiola De Marco1,∗,† ,
Anna Maria De Roberto1,† , Luigi Di Biasi1,† , Rita Francese1,† and Genoveffa Tortora1,†
1
Computer Science Department of University of Salerno, Fisciano, SA 84084 IT
Abstract
Artificial Intelligence plays a vital role in disease diagnosis, but effectively classifying diverse Premature
Ventricular Contraction (PVC) subtypes remains a challenge. While computer-aided systems demonstrate
high performance, human oversight remains crucial for reliability. This study introduces Explainable AI
algorithms utilizing the GRADient-weighted Class Activation Mapping algorithm, as part of the proposed
framework CardioView, providing insights into the diagnosis process. With high accuracy, recall,
precision, and AUC (96.21%, 98.09%, 94.74%, 99.28% respectively), the system enhances understanding
of PVC classification. CardioView allows individuals to gain insights into the discrimination process,
revealing its operations and visualizing the components of the electrocardiogram waveform that aid in
distinguishing between PVC and non-PVC classes, as well as within various PVC subclasses. Furthermore,
CardioView integrates a human-in-the-loop approach, ensuring active involvement of cardiologists
throughout the diagnostic process and reinforcement learning mechanisms.
Keywords
Computer-aided diagnosis system, Convolutional Neural Networks, eXplainable Artificial Intelligence,
Premature Ventricular Contractions, Symbiotic Artificial Intelligence
1. Introduction
In the era of medical development, Computer-Aided Diagnosis (CAD) systems may become
a crucial component of the healthcare revolution, mainly when combined with Artificial In-
telligence (AI) tools. In particular, these systems may improve diagnostic accuracy, expedite
workflow, enable early detection, offer decision support, help individualized medication, and
act as teaching aids. CAD systems can potentially improve comprehension of patient condi-
tions, enhance patient outcomes, and streamline healthcare delivery [1]. While AI capabilities
INI-DH 2024: Workshop on Innovative Interfaces in Digital Healthcare, in conjunction with International Conference on
Advanced Visual Interfaces 2024 (AVI 2024), June 3–7, 2024, Arenzano, Genoa, Italy (2024)
∗
Corresponding author.
†
These authors contributed equally.
Envelope-Open g.arienzo99@gmail.com (G. Arienzo); aauriemmacitarella@unisa.it (A. Auriemma Citarella); fdemarco@unisa.it
(F. De Marco); m.derob89@gmail.com (A. M. De Roberto); ldibiasi@unisa.it (L. Di Biasi); francese@unisa.it
(R. Francese); tortora@unisa.it (G. Tortora)
Orcid 0000-0002-6525-0217 (A. Auriemma Citarella); 0000-0003-4285-9502 (F. De Marco); 0000-0001-6201-5193
(A. M. De Roberto); 0000-0002-9583-6681 (L. Di Biasi); 0000-0002-6929-0056 (R. Francese); 0000-0003-4765-8371
(G. Tortora)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
continue to advance, its inherent “black box” characteristic restrains its widespread adoption in
the digital health sector. Specifically, many AI techniques are not transparent in their underly-
ing methodology responsible for their exceptional performance, as documented in scientific
literature [2]. Human operators still need to learn to rely on CAD systems results without a
comprehensive understanding of the underlying processes. Establishing trust in AI requires
an elucidation explanation of the mechanisms utilized to generate its outcomes. This objec-
tive can be achieved through the eXplainable Artificial Intelligence (XAI) approach [3]. XAI
refers to developing AI systems that can explain their decision-making processes clearly and
comprehensively. Common XAI approaches include feature importance analysis, decision tree
analysis, and visualization tools such as heat maps and saliency maps, highlighting the most
significant portions of the data [4]. Particular relevant use of XAI is to highlight the portions
of a medical image that are more relevant for classification tasks, improving our understanding
of disease development and the ability to discover novel imaging biomarkers, disease patterns,
and abnormalities [5] [6].
Physiologically, in the cardiac system, electrical signals follow a defined path during a typical
cardiac cycle, initiating contraction sequentially: impulse for cardiac rhythm originates from
the sinoatrial node that represents the “cardiac pacemaker”, and from that situs, it spreads
along cardiac atria. Subsequently, this impulse travels down through the conduction pathways
and causes ventricular depolarization and contraction essential for pumping out blood. The
characteristic elements of a normal heartbeat consist of the P wave, the QRS complex (comprising
the Q, R, and S waves), and the T wave. The QRS complex represents the electrical activity linked
to ventricular contractions. Premature Ventricular Contraction (PVC) is an ectopic arrhythmia
originating outside the sinoatrial node, disrupting normal cardiac rhythm. Symptoms include
palpitations, skipped heartbeats, dizziness, and breathlessness. Ectopic beats can arise from
atrial or ventricular myocardium and manifest as premature heartbeats, differing from sustained
ectopic rhythms. PVCs result from abnormal myocardial depolarization, leading to premature
contractions and altered cardiac output. They can originate from various ventricular regions,
each generating distinct PVC morphologies based on the impulse origin. Among the diagnostic
criteria of PVC analysis we can find:
• premature QRS compared to what is expected from previous cardiac rhythm;
• different morphology of QRS compared to previous QRS analysis (broad QRS complex ≥
120 ms);
• discordant ST segment and T wave changes;
• PVC is usually followed by a compensatory pause;
• ectopic beats originating from the right ventricle have a left bundle block morphology
pattern, while ectopic beats originating from the left ventricle have a right bundle block
morphology pattern.
PVCs are common in the general population though their reported prevalence ranges widely,
between 1 and 40% [7] - [8]. This large variation likely depends on the different study populations
and different ways of recording and storing ECG [9]. The significance of PVC in the general
health population is controversial [10], while the association between PVCs and negative
prognosis in patients with structural heart disease is well established [11] - [12].
This research employs AI and XAI techniques to investigate PVCs, supporting cardiologists in
detecting PVC classes. Integrating XAI features into the model aims to enhance its transparency
and comprehensibility for cardiologists, enabling them to gain insights into PVC physiology
and potentially elevating diagnostic accuracy.
This study analyzed images from the MIT-BIH Arrhythmia Database, a widely accepted
resource in arrhythmia detection [13]. The chosen images were employed for training a
Convolutional Neural Network (CNN) for the PVCs classification. In addition, XAI features
were incorporated using the Grad-CAM approach, which generated images highlighting the
specific areas of the heartbeats considered crucial by the CNN for its decisional process [14].
Subsequently, clustering techniques, such as K-Means and Density-Based Spatial Clustering of
Applications with Noise (DBSCAN) [15], identified three distinct clusters based on the Grad-
CAM-generated images. This process contributes to the development of a framework, called
𝐶𝑎𝑟𝑑𝑖𝑜𝑉 𝑖𝑒𝑤.
Our key contributions are:
• We use the images generated by an XAI algorithm for improving the performance of a
CNN-based PVC classifier competitive with the state-of-art;
• We apply clustering algorithms to the images generated by the XAI algorithm and identify
three PVC clusters useful for identifying patterns in PVC sub-classes.
• We propose a visual framework, which incorporate the human in the loop approach,
ensuring active involvement and oversight of cardiologists throughout the PVC detection
process.
CardioView is designed to gather information through surveys administered to cardiologists.
Collecting responses assists us in enhancing the algorithms and performing reinforcement
learning (RL). In this preliminary study, we focus solely on the initial phase: the classification
of PVCs, the application of XAI algorithms, and their clusterization.
The paper organization follows: in Section 2, we reported the state of the art of using the XAI
in PVC detection. Section 3 describes the research design, materials, and methods employed
in the study. Section 4 provides the results of our work and related discussion. Section 5
summarizes the main findings of the study, implications, and future directions.
2. Related works
In this section, we provide a concise overview of existing research and current studies that
involve the classification of PVCs using XAI techniques. The necessity of explainable PVC
detection lies in the critical domain of medical diagnostics, particularly in cardiology. The
interpretability of the AI models helps to clarify the rationale behind the findings, improving
the care of patients. In [16], to show and visualize the saliency, the authors introduced a special
implementation of Grad-CAM for the CNN model and a second method, which involves learning
the input deletion mask for the LSTM model. In PVC, the LSTM network gives more weight to the
aberrant PVC rhythm than the CNN network does to the proximal beats. The authors reported
the following overall performance for eight arrhythmia classification tasks. In particular, the
best results are for [17]: 98% of Precision, 97% of recall and F1 score, and an accuracy of 97%.
In [18], the authors proposed a novel deep learning approach for multilabel classification of
ECG signals for the CPSC-2018 dataset. They reached an overall precision of 98.6%, a recall of
94.9%, an F1 score of 96.7% and 96.2 in accuracy. Additionally, class activation maps obtained
from the Grad-CAM enhance the model interpretability. The XAI framework ensured that the
CNN learned appropriate features, mitigating its black-box nature and instilling confidence
in the model’s results. Specifically for the PVC class, ECGs with PVC are distinguished by a
vast QRS complex. In [19], the authors classified 12-lead ECG recordings from the CPSC-2018
dataset with a deep neural network. For the PVC class, they obtained 86.9% Precision, 83.9% of
recall, 85.1% of F1 score, an AUC of 97.6%, and an accuracy of 97.1%. To evaluate the behaviour
of the model at the patient and population levels, they used the Shapley Additive exPlanations
approach (SHAP). In [20], to extract features from individual ECG leads, the authors used three
different One-dimensional Convolutional Neural Networks (1D-CNNs), introducing a novel
lead-wise attention module that combines the outputs from the three backbones to produce
a more reliable representation. Moreover, they emphasized the XAI framework significantly,
enabling our system to provide more meaningful and clinically relevant explanations for its
predictions. The suggested approach, known as LightX3ECG, reached for PVC class in the
CPSC-2018 dataset, a precision of 87.96%, a recall of 71.97%, an F1 score of 79.17% and 96.37% of
accuracy. Also, the authors make innovative use of the XAI-generated images for improving
the CNN classifier and for using clustering algorithms on the XAI dataset to identify PVC
sub-patterns.
3. Methods
3.1. Working hypothesis & Workflow
PVCs can indicate underlying heart conditions like cardiomyopathy or coronary artery dis-
ease [21]- [22]. Several research suggest multiple distinct categories of PVCs may exist, as they
vary in their associations with cardiac outcomes [23]. This observation leads to the formulation
of the following initial working hypotheses (WHs):
WH1 If multiple PVC outcomes exist, then multiple PVC classes exist.
WH2 If multiple classes of PVC exist, it is possible to partition an ECG dataset composed of
PVC and non-PVC ECG acquisition in multiple clusters, each related to a likely outcome.
A natural inference drawn from the WHs is that merely classifying ECG tracks into PVC and
NON-PVC classes during the examination of the clinicians may prove insufficient, as reported
in [24]. Furthermore, a crucial need arises to distinguish between different PVC classes has a
preventative measure against further cardiac risks. Given the potential implications of PVC
classes, developing precise and efficient methods for their detection and diagnosis is imperative.
Figure 1 illustrates the workflow of the proposed framework. The methodology initiates
with dataset analysis and preprocessing to minimize signal noise and enhance quality, utilizing
techniques tailored for both black and white (BW) and color (RGB) datasets. It comprises
two main phases: initially, the BW dataset trains a CNN model employing classic and k-fold
training, followed by feature extraction post-training; subsequently, the RGB dataset undergoes
training using the classic method only, with the resulting model applied to the Grad-CAM
algorithm for creating a modified Grad-CAM version of the dataset. This version then retrains
the model. Features are extracted similarly to the first phase. Finally, K-means and DBSCAN
clustering algorithms are applied to the extracted features to identify potential PVC patterns.
CardioView is structured to facilitate the incorporation of surveys obtained from cardiologists,
which constitutes a crucial element of the human-in-the-loop process integral to RL.
Figure 1: CardioView: Proposed Framework
3.2. Dataset & Pre-processing
This work primarily relies on the MIT-BIH Arrhythmia Database, containing 48 half-hour
extracts from two-channel ECG recordings from 47 subjects, allowing 24-hour continuous
monitoring. With data digitized at 360 samples per second per channel and 11-bit resolution, it
serves as a benchmark for arrhythmia detection algorithms and ML/DL models. Our dataset
includes 7130 elements for PVC QRS complexes and 75048 for non-PVC QRS complexes, with
subjects aged 23 to 89, 60% hospitalized, and 40% outpatients. In this work, we utilized a
dataset subset of a total of 14123 images. Pre-processing images is crucial for data quality
improvement, ensuring suitability for analysis by removing noise and enhancing accuracy. In
this study, raw data from the MIT-BIH Arrhythmia Database was converted into color images,
and the imperfections were removed with adaptiveThreshold function in the CV2 library.
Three datasets were employed: the BW and RGB version dataset and the dataset obtained by
Grad-CAM visualization.
3.3. CNN design
This work explores various models, selecting the best-performing one to ensure optimal Grad-
CAM representations. However, as networks become deeper, Grad-CAM representations may
become less reliable due to increased noise. The network architecture includes layers for image
normalization, convolution, pooling, upsampling, dropout, flattening, and fully connected
layers. A CNN is trained using k-fold cross-validation with 4 epochs and 5 folds, enhancing
performance evaluation and generalization. Data is split into training, validation, and test sets,
with an 80-10-10 ratio for standard training and a 90-10 ratio for k-fold validation. This approach
improves model robustness and generalization, ensuring effective training and evaluation of
the CNN network architecture.
3.4. XAI and clustering algorithms
The Grad-CAM algorithm enhances XAI by highlighting significant features contributing to
model predictions. It operates by selecting the target layer, calculating gradients, and computing
weights for activation maps. These maps are then aggregated and overlaid on the original image,
emphasizing regions of interest. Grad-CAM aids model validation and fine-tuning, aligning with
human intuition and expertise, improving model accountability, and building trust. This study
employs two clustering algorithms: K-means and DBSCAN. K-means is an unsupervised method
that partitions data into k clusters by iteratively updating cluster centroids. However, it requires
a predefined number of clusters, is sensitive to initial centroid selection, and struggles with
overlapping clusters. In contrast, DBSCAN does not require a predefined number of clusters and
group points based on spatial density, making it more adaptable to various cluster shapes and
sizes while effectively handling noise. Hyperparameter selection is crucial, impacting clustering
model performance significantly. The Elbow method was used to determine the optimal number
of clusters for K-means, with a knee point identified at k=3.
3.5. Proposed survey
The human-in-the-loop (HITL) and RL phase will incorporate a dynamic survey involving
experienced cardiologists. This survey allows them to express their confidence levels regarding
the outcomes generated by the Grad-CAM algorithm, fostering iterative refinement and im-
proving collaborative decision-making in PVC diagnosis from the perspective of symbiotic AI
(SAI) systems. Professionals will learn about XAI’s role in early PVC detection from cardiac
images. Participation wil be voluntary, with optional personal data collection. Subsequently,
participants will examine 20 images (10 randomly selected between PVC and non-PVC). They
will identify in each images the presence or absence of PVCs based on their experience (Figure
2).
Figure 2: Survey step 1
If their response aligns with the truth matrix, they will receive the corresponding Grad-CAM
image to assess its efficacy in identifying crucial points. They will rate their confidence in the
XAI algorithm on a Likert scale from 0 to 10 [25] and can leave comments for each Grad-CAM
image. Participants disagreeing with the Grad-CAM result can suggest an alternative image
zone for reinforcement learning. This step is grahically represented in Figure 3, where the result
of applying Grad-CAM algorithm on a non-PVC image is shown.
Figure 3: Survey steps
3.6. Performance measures
Performance evaluation is essential to assess the effectiveness of different ML and DL models.
This work uses the following metrics: (Equations 1-3):
𝑇𝑃 + 𝑇𝑁
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 (𝐴𝐶𝐶) = (1)
𝑇𝑁 + 𝐹𝑃 + 𝐹𝑁 + 𝑇𝑃
𝑇𝑃
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 (𝑃𝑅𝐸) = (2)
𝑇𝑃 + 𝐹𝑃
𝑇𝑃
𝑅𝑒𝑐𝑎𝑙𝑙 (𝑅𝐸) = (3)
𝑇𝑃 + 𝐹𝑁
where TP are the True Positives, the total number of samples correctly classified as positive,
FP are False Positive, the total number of items incorrectly classified as positive. TN (True
Negatives) and FN (False Negatives) represent the total number of samples appropriately defined
and incorrectly selected as negative, respectively.
The work assesses clustering algorithms using Silhouette Score [26], Calinski-Harabasz Index
[27], and Davies-Bouldin Index [28] to measure clustering quality.
The Silhouette Score (see Equation 4), which ranges from -1 to 1, evaluates how well a data
point matches its cluster in comparison to other clusters.
(𝑏 − 𝑎)
𝑆𝑖𝑙ℎ𝑜𝑢𝑒𝑡𝑡𝑒 𝑆𝑐𝑜𝑟𝑒 = (4)
𝑚𝑎𝑥(𝑎, 𝑏)
The Calinski-Harabasz Index (CH Index) (see Equation 5) measures the within-cluster to
between-cluster dispersion ratio.
𝐵 (𝑁 − 𝑘)
𝐶𝑎𝑙𝑖𝑛𝑠𝑘𝑖 − 𝐻 𝑎𝑟𝑎𝑏𝑎𝑠𝑧 𝐼 𝑛𝑑𝑒𝑥 = ∗ (5)
𝑊 (𝑘 − 1)
The Davies-Bouldin Index (DB Index) (see Equation 6) considers the internal similarity of the
cluster and calculates the average similarity between each cluster and its most similar cluster.
𝑘
1 𝑠(𝑖) + 𝑠(𝑗)
𝐷𝑎𝑣𝑖𝑒𝑠 − 𝐵𝑜𝑢𝑙𝑑𝑖𝑛 𝐼 𝑛𝑑𝑒𝑥 = ∗ ∑ 𝑚𝑎𝑥(𝑗 ≠ 𝑖)( ) (6)
𝑘 𝑖=1 𝑑(𝑖, 𝑗)
4. Results
Table 1 shows the CNN classification results on the test sets for the original dataset (CNN)
and the Grad-CAM version of the dataset (CNN-GC) with k-fold and classic training. Overall,
CNN-GC with classic training reaches the best results on the classification test (96.21% of ACC,
99.28 of AUC, 98.09% of RE and 94.74% of PRE). On the other hand, the results on the test set
with k-fold cross-validation are also high, reaching an ACC of 96.77%, 99.46% of AUC, 98.11% of
RE and 95.33% of PRE.
Table 1
Results of CNN
Model ACC (%) AUC (%) RE (%) PRE (%)
CNN (test) 95.64 98.91 97.13 94.17
CNN-GC (test) 96.21 99.28 98.09 94.74
K-fold
CNN (test) 96.77 99.46 98.11 95.33
Table 2 shows Silhouette Score, CH, and DB Indexes on MIT-BIH dataset for test set. K-
means partitions data into clusters based on centroid-based distance minimization and assumes
spherical clusters of equal size. In contrast, DBSCAN identifies clusters based on data density
without needing a predefined cluster number. This leads to different cluster results. Scatter plots
depict positive (PVC) and negative (NON-PVC) classified images in red and green respectively.
Generated clusters are shown in different colors. The best values of the three indexes are reached
using the Grad-CAM version of the dataset and the classic training. For binary classification (see
images on the left for Figure 4), the results of K-means and DBSCAN are very similar. On the
GRADCAM version of the dataset, DBSCAN turns out to be less suitable in identifying intraclass
variation of PVCs, despite detecting three clusters, since it cannot discriminate subpatterns
within PVCs, unlike k-means.
Table 2
Values of Silhouette Score, CH and DB indexes k-means and DBSCAN on test dataset
Train Silhouette Score CH Index DB Index
k-means (CL) 0.63 3219.87 0.50
K-means (KF) 0.58 2583.99 0.58
DBSCAN (CL) -0.39 75.66 6.97
DBSCAN (KF) 0.07 270.75 2.53
Test set (Grad-CAM)
k-means (CL) 0.64 3352.70 0.49
DBSCAN (CL) 0.22 419.80 2.60
Figure 4: K-Means (up) and DBSCAN (bottom) on test set GRADCAM version with classic training
5. Conclusions
In this study, the MIT-BIH dataset of BW images trained a CNN model using classic and K-fold
training, followed by processing the Grad-CAM dataset of RGB images solely with classic
training. Trained models extracted essential features, inputted into k-means and DBSCAN
clustering algorithms to identify PVC and NON-PVC patterns for early diagnosis. Future
works plan to integrate surveys for the human-in-the-loop phase to explain CNN workings,
increasing trust in medical AI. Grad-CAM facilitated visualization of ECG waveform segments
distinguishing PVC from NON-PVC. Clustering uncovered potential multiple PVC classes,
suggesting distinct outcomes. The proposed CNN achieved 96.21% accuracy, 98.09% recall,
94.74% precision, and 99.28% AUC on the GRADCAM dataset, showcasing promising results for
PVC detection.
Future studies aim to incorporate surveys into the human-in-the-loop phase to guarantee the
reinforcement learning of the proposed framework. By leveraging the insights and expertise
of medical professionals through surveys, we not only improve the interpretability and trans-
parency of AI models but also foster a synergistic partnership that ensures the development of
more reliable and trustworthy medical SAI systems.
6. Acknowledgments
This study was carried out within the FAIR - Future Artificial Intelligence Research and received
funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA
E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.3 – D.D. 1555
11/10/2022, PE00000013).
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