Advanced Visualization Techniques Research Articles

Shifting Sands: Examining and Mapping the Population Structure of Greece Through the Last Three Censuses

This paper aims to facilitate a more nuanced understanding of regional disparities in the population age structure at a local scale by applying a recent method for visualizing these disparities. Utilizing data from the three most recent population censuses in Greece, this method applies advanced data visualization techniques to map age distributions, highlighting significant variations in aging patterns across municipalities, towns, and districts. Traditional demographic analysis often overlooks local heterogeneities, leading to broad policies that often fail to address the unique needs of specific regions. Detailed maps are created by integrating geographic data with census data (using R and GIS), enabling policymakers to pinpoint areas with specific demographic challenges and opportunities. This localized approach reveals critical insights, such as regions experiencing rapid population aging, areas with younger population profiles, and zones undergoing demographic transitions. The visualization tool significantly improves the formulation of targeted strategies, enhancing the effectiveness of policies related to healthcare, workforce planning, and social services distribution. Through case studies and comparative analysis, we demonstrate the practical applications and advantages of this method in shaping public policy and strategic planning. This paper contributes to the field of geodemography by introducing and demonstrating a visualization method that enhances the accuracy of demographic analysis, providing policy makers with useful information to better address local demographic challenges and tailor strategies to specific regional needs.

Mapping the Path to Low-Carbon Behaviour: A Systematic Review of Trends, Gaps, and Future Directions

It is essential to conduct research on low-carbon behaviour in order to address global climate change and promote sustainable development. This study conducts a thorough examination of a bibliometric analysis of the existing literature in this field. We analysed 129 papers from the Web of Science Core Collection database to conduct a study on the concept of “low-carbon behaviour”. Review articles, proceedings papers, and editorial materials were all excluded. This investigation examines a diverse array of research environment factors, including the most highly ranked publications, publication trends, significant publishers, and patterns in citations and publications over time. The primary institutional contributors in the discipline and the most influential works worldwide are also mentioned. The application of advanced visualisation techniques, such as wordcloud representations, the spatial distribution of research output, and co-occurrence and overlay networks, is employed to generate maps of keyword relationships and collaboration networks using Bibliometrix Stable Version, VOSviewer 1.6.20, and Scimago Graphica Beta 1.0.45. This multi-modal methodology enables a comprehensive investigation of significant research issues and emerging directions in low-carbon behavioural studies. This study contributes to the ongoing endeavours to promote sustainable development and mitigate climate change by conducting a comprehensive evaluation of the current state of research and establishing a robust framework for future investigations into low-carbon behavioural patterns and behaviours.

Time–frequency ridge characterisation of sleep stage transitions: Towards improving electroencephalogram annotations using an advanced visualisation technique

Manual sleep stage scoring of polysomnography recordings is an expensive and time-consuming process, further complicated by inconsistent sleep stage agreement among sleep experts (clinicians and sleep technologists). Hence, development of automated sleep scoring algorithms are an emerging topic of interest. Automation typically mimics the clinical decision path by implementing a series of predefined rules, such as the American Academy of Sleep Medicine’s (AASM) scoring manual. Recently, data driven methods have emerged using machine or deep learning. Both manual and automated methods of scoring have known limitations; primarily, unacceptable variation in agreement between different scorers and algorithms. Within the literature, electroencephalogram (EEG) frequency is an important feature considered by both sleep experts and automated approaches for classifying sleep stages.This study presents a novel approach to sleep stage analysis, by developing a methodology to precisely determine the temporal location of sleep stage transitions. The current gold standard fails to identify such transitional changes, which leads to poor inter-scorer reliability. Therefore, development and implementation of such methodologies is a crucial, but overlooked, step in improving the consistency of scoring within sleep studies. In this work, EEG time–frequency ridge analysis was used to characterise the dominant frequency component of EEG signals in time, at the point of sleep stage transition. An in-depth analysis of N3 → N2 and N2 → N3 transitions in the 2018 PhysioNet challenge “You Snooze, You Win” and the Wisconsin Sleep Cohort (WSC) datasets (n = 994, n = 742; approximately 13,888 h of sleep data) showed consistent time–frequency patterns at the point of transition, from one sleep stage to another. This methodology allows simple and ‘interpretable’ features to be generated in future work, to precisely identify the temporal location of sleep stage transitions with the aim of improving inter-scorer reliability.

Research on the Application of Computer Technology in Biostatistics

This paper explores the application of computer technology in biostatistics, focusing on the technological advancements and their impact on data management, data processing and analysis, and data visualization. With the development of computer technology, data storage and management methods have significantly improved, particularly with the introduction of database systems and big data technologies, which have greatly enhanced the efficiency and accuracy of handling biostatistical data. Additionally, the application of machine learning and artificial intelligence has made biostatistical data analysis more intelligent and automated, providing new tools for identifying and predicting complex data patterns. Furthermore, advanced data visualization techniques have enhanced the interpretability of statistical results through clear graphical presentations, promoting the application of biostatistics. Through the analysis of specific case studies, this paper demonstrates the practical effects and challenges of computer technology in improving biostatistical research capabilities. Finally, the paper anticipates the future impact of emerging computing technologies, such as quantum computing, on biostatistics and suggests future research directions and recommendations.

Advanced monitoring of damage behavior and 3D visualization of fiber distribution in assessing crack resistance mechanisms in steel fiber-reinforced concrete

This study presents a novel approach to understanding the reinforcing mechanisms of steel fiber-reinforced concrete, particularly its crack resistance and toughening effects. By integrating dynamic surface strain monitoring with advanced three-dimensional visualization techniques, new insights are provided into how steel fibers enhance concrete's structural performance. X-ray computed tomography (CT) technology enabled detailed 3D visualization and analysis of steel fibers embedded within the concrete matrix, facilitating an in-depth examination of their distribution patterns, orientation, and positioning. Additionally, the Digital Image Correlation (DIC) three-dimensional full-field strain measurement system was utilized to dynamically monitor the loading process during three-point bending tests on semicircular concrete specimens. The findings indicate that the uniform distribution of steel fibers significantly reduces stress concentration and effectively delays crack initiation and propagation. Steel fibers bridge cracks, mitigate local stress concentrations, and substitute for the tensile components of concrete after macro-crack formation, thereby inhibiting further crack propagation. Quantitative analysis of fiber distribution through 3D image reconstruction and coordinate extraction confirmed a strong correlation between fiber orientation and crack resistance. This study contributes to the field by offering a comprehensive analysis of key parameters such as fracture process strain monitoring, 3D visualization, and quantification of fiber distribution, advancing the understanding of crack propagation mechanisms and failure processes in fiber-reinforced concrete.

OPTIMIZATION OF PHOTOVOLTAIC SYSTEM SAFETY THROUGH DATA ANALYSIS: DEVELOPMENT AND IMPLEMENTATION OF AN INTERACTIVE DASHBOARD FOR EVENT MONITORING AND PREVENTIVE STRATEGY FORMULATION

The development of the photovoltaic (PV) sector in Poland, as a crucial component of the renewableenergy transition strategy, faces a challenge related to limited user awareness of operational risks.Our study addresses this gap by presenting the development and implementation of an interactivedashboard for the National Fire Department’s Decision Support System (SWD PSP), aimed atoptimizing the safety of PV installations through data analysis and the formulation of preventivestrategies. Utilizing data from fire protection units, this tool enables the monitoring of incidentsand identification of potential threats, while simultaneously increasing societal awareness. Thedashboard, leveraging advanced data visualization techniques, provides easy navigation anddynamic presentation of statistics, facilitating quick responses to changing data and potentialhazards. The results of our study highlight the significance of such tools in enhancing the safety ofusing PV installations, which can contribute to the further development of the renewable energysector, ensuring its safety and efficiency.

Abstract P367: Cross-Species Epigenomic Maps of Intergenic Loci for Hypertension and Blood Pressure

Many non-coding SNPs identified in Genome-Wide Association Studies (GWAS) likely affect BP-related gene expression through epigenetic mechanisms. This study analyzes the comparative genomic and epigenomic landscapes in human and rat kidney tissues, including kidney proximal tubule (PT) and medullary thick ascending limb (mTAL), focusing on the major challenge of identifying conserved regulatory elements in intergenic regions. The purpose of our study is to identify BP gene regulatory elements in intergenic regions that are conserved from human to rat for in vivo validation and mechanistic studies. Our ultimate goal is to generate high-resolution, genome-wide epigenomic maps of key BP-relevant tissues, using ATAC-seq, Hi-C, CUT&Tag, DNA methylation profiling and RNA-seq. We are creating visualization hubs for human and rat, integrating our data within the UCSC Genome Browser environment. UCSC contains extensive data for humans but very little for rats, particularly for the updated genome assemblies. To address this gap, we integrated our results with data from the Rat Genome Database (RGD). To date, we harmonized ATAC-seq data from human and rat PT and mTAL and displayed these data in RGD’s JBrowse2 genome browser for unique comparative genome views. Based on our initial analysis of data density and intensity, we are developing a novel pattern recognition algorithm to identify epigenomic marks that are conserved across rat and human genomes. Existing algorithms predominantly focus on the conservation of coding genes, gene families, and genomic sequences. Here, average normalized peak intensities for ATAC-seq data across tissues were subject to canonical correlation between human and rat to identify conserved epigenomic patterns within the BP GWAS loci. Here we present an example of a human BP locus in the intergenic region between NPR3 and TARS1 on chromosome 5 which has a region of conserved synteny with rat chromosome 2. Comparative analysis and visualization of this region show significant correlation between ATAC-seq peaks from rat and human PT and mTAL at this locus, with predicted conserved regulatory elements. These visualizations can uncover novel biological insights and identify potential regulatory targets for intervention. This work underscores the importance of advanced visualization techniques in understanding hypertension pathogenesis, facilitating cross-species comparisons, and enhancing the utility of genomic databases.

Characterisation and Application of Bio-Inspired Hybrid Composite Sensors for Detecting Barely Visible Damage under Out-of-Plane Loadings.

Traditional inspection methods often fall short in detecting defects or damage in fibre-reinforced polymer (FRP) composite structures, which can compromise their performance and safety over time. A prime example is barely visible impact damage (BVID) caused by out-of-plane loadings such as indentation and low-velocity impact that can considerably reduce the residual strength. Therefore, developing advanced visual inspection techniques is essential for early detection of defects, enabling proactive maintenance and extending the lifespan of composite structures. This study explores the viability of using novel bio-inspired hybrid composite sensors for detecting BVID in laminated FRP composite structures. Drawing inspiration from the colour-changing mechanisms found in nature, hybrid composite sensors composed of thin-ply glass and carbon layers are designed and attached to the surface of laminated FRP composites exposed to transverse loading. A comprehensive experimental characterisation, including quasi-static indentation and low-velocity impact tests alongside non-destructive evaluations such as ultrasonic C-scan and visual inspection, is conducted to assess the sensors' efficacy in detecting BVID. Moreover, a comparison between the two transverse loading types, static indentation and low-velocity impact, is presented. The results suggest that integrating sensors into composite structures has a minimal effect on mechanical properties such as structural stiffness and energy absorption, while substantially improving damage visibility. Additionally, the influence of fibre orientation of the sensing layer on sensor performance is evaluated, and correlations between internal and surface damage are demonstrated.

Magnetized microcilia-based triboelectric nanogenerators with mechanoluminescence for energy harvesting and signal sensing

Flexible triboelectric nanogenerators with multimodal sensing capabilities have received considerable attention due to their potential for the development of wearable technology. However, the accurate and reliable display of motion trajectories represents a significant challenge. The structure design of the triboelectric nanogenerators yarn with magnetized microcilia and mechanoluminescence (MLY-TENG) was designed, which can be woven into the fabric of display force trajectories. The positive electrode is constructed by the braided yarn with a porous structure, consisting of polyurethane formed by wet spinning. Meanwhile, the flexible negative electrode is designed by the recombination of magnetized microcilia and the mechanoluminescent structure of polydimethylsiloxane, matching with the porous structure of the positive electrode, which enhances the electric transfer. At a compression depth of 100 %, compression frequency of 5 Hz, and magnetized powder of 50 wt%, the proposed MLY-TENG shows the triboelectric properties of 109.2 V and exhibits excellent cyclic stability. Furthermore, the magnetized microcilia on the luminescent magnetized microcilia (LMM) film with the polyurethane-copper-polyurethane (PWP)-based fabric (MLF-TENG) distinguishes the shape and area of object recognition with electrical signals and visual sensing of mechanoluminescence. The MLY-TENG offers the possibility of the development of advanced visualization techniques for wearable electronic devices.

Research fronts and researchers of World Journal of Psychiatry in 2023: A visualization and analysis of mapping knowledge domains.

In the rapidly evolving landscape of psychiatric research, 2023 marked another year of significant progress globally, with the World Journal of Psychiatry (WJP) experiencing notable expansion and influence. To conduct a comprehensive visualization and analysis of the articles published in the WJP throughout 2023. By delving into these publications, the aim is to determine the valuable insights that can illuminate pathways for future research endeavors in the field of psychiatry. A selection process led to the inclusion of 107 papers from the WJP published in 2023, forming the dataset for the analysis. Employing advanced visualization techniques, this study mapped the knowledge domains represented in these papers. The findings revealed a prevalent focus on key topics such as depression, mental health, anxiety, schizophrenia, and the impact of coronavirus disease 2019. Additionally, through keyword clustering, it became evident that these papers were predominantly focused on exploring mental health disorders, depression, anxiety, schizophrenia, and related factors. Noteworthy contributions hailed authors in regions such as China, the United Kingdom, United States, and Turkey. Particularly, the paper garnered the highest number of citations, while the American Psychiatric Association was the most cited reference. It is recommended that the WJP continue in its efforts to enhance the quality of papers published in the field of psychiatry. Additionally, there is a pressing need to delve into the potential applications of digital interventions and artificial intelligence within the discipline.

Smart Report for Healthcare System

In the ever-evolving landscape of healthcare, the effective interpretation and presentation of pathology patient data play a pivotal role in diagnosis, treatment, and research. Recognizing the transformative power of advanced data visualization techniques, this project endeavors to forge a comprehensive and user-friendly a platform dedicated to illuminating pathology patient data. By leveraging cutting-edge visualization tools, our the initiative aspires to furnish healthcare professionals, researchers, and patients with an intuitive and holistic representation of intricate pathology data. As healthcare continues to evolve, the potential benefits of Smart Healthcare Systems are evident, including enhanced patient outcomes, reduced costs, and improved healthcare delivery. In conclusion, this report underscores the profound impact of Smart Healthcare Systems on the future of medical services. It provides valuable insights for healthcare professionals, policymakers, and stakeholders to navigate the changing landscape and maximizing the benefits of smart technologies in healthcare.

Scientometric Research and Critical Analysis of Gait and Balance in Older Adults.

Gait and balance have emerged as a critical area of research in health technology. Gait and balance studies have been affected by the researchers' slow follow-up of research advances due to the absence of visual inspection of the study literature across decades. This study uses advanced search methods to analyse the literature on gait and balance in older adults from 1993 to 2022 in the Web of Science (WoS) database to gain a better understanding of the current status and trends in the field for the first time. The study analysed 4484 academic publications including journal articles and conference proceedings on gait and balance in older adults. Bibliometric analysis methods were applied to examine the publication year, number of publications, discipline distribution, journal distribution, research institutions, application fields, test methods, analysis theories, and influencing factors in the field of gait and balance. The results indicate that the publication of relevant research documents has been steadily increasing from 1993 to 2022. The United States (US) exhibits the highest number of publications with 1742 articles. The keyword "elderly person" exhibits a strong citation burst strength of 18.04, indicating a significant focus on research related to the health of older adults. With a burst factor of 20.46, Harvard University has made impressive strides in the subject. The University of Pittsburgh displayed high research skills in the area of gait and balance with a burst factor of 7.7 and a publication count of 103. The research on gait and balance mainly focuses on physical performance evaluation approaches, and the primary study methods include experimental investigations, computational modelling, and observational studies. The field of gait and balance research is increasingly intertwined with computer science and artificial intelligence (AI), paving the way for intelligent monitoring of gait and balance in the elderly. Moving forward, the future of gait and balance research is anticipated to highlight the importance of multidisciplinary collaboration, intelligence-driven approaches, and advanced visualization techniques.

Sustainable Energy Consumption Analysis through Data Driven Insights

Energy is the backbone of our society, supporting our daily activities and driving progress. It plays a crucial role in shaping our modern way of life. The future of global energy consumption is influenced by many factors, including demographics, economic dynamics, technological developments, political actions, environmental demands and geopolitical considerations. As the world's population continues to grow and urbanize, the demand for energy is increasing. At the same time, rapid technological innovations are shaping the energy landscape and changing production, distribution and consumption patterns. In the midst of this development, it is very important to optimize energy consumption, accurately anticipate needs, curb climate change, limit emissions of greenhouse gasses, fight against pollution and promote sustainability. This study includes an in-depth analysis of historical consumption trends, assessing the multiple benefits of renewable energy integration, estimating carbon emissions, formulating practical policy recommendations and providing empirically informed insights. The work is based on various data obtained from platforms such as Kaggle and using advanced visualization techniques such as Power BI dashboards. The study provides invaluable perspectives on future energy needs, the penetration of renewable sources into the energy mix, and the strategic needs to achieve sustainable energy use.

Analyzing SME's Data Visualization, Business Challenges, and Solutions: A Seven Stars Review

This comprehensive report encapsulates a thorough analysis conducted on the extensive Seven Stars dataset. Leveraging advanced data visualization techniques, the analysis has been meticulously executed to extract meaningful insights and elucidate intricate patterns within the dataset. By delving deep into the data, the report identifies a spectrum of potential business challenges that the organization may encounter, both in the present and future landscapes. These challenges encompass diverse realms such as market fluctuations, resource allocation, and operational inefficiencies. In response to the identified challenges, a robust set of solutions is proposed, tailored to address each issue methodically. Drawing upon best practices and industry expertise, these solutions aim to bolster the organization's resilience and competitiveness in the dynamic business environment. Moreover, to facilitate seamless data management and decision-making processes, a bespoke dashboard has been meticulously crafted. This intuitive dashboard serves as a centralized platform, enabling stakeholders to effortlessly manipulate and analyze data from disparate sources, thereby fostering informed decision-making and strategic planning. In essence, this paper serves as a comprehensive roadmap for the organization's data-driven journey, guiding it towards sustainable growth and success in an ever-evolving business landscape. Through diligent analysis, strategic foresight, and proactive problem-solving, the organization can chart a course towards long-term prosperity and resilience.

Enhancing surgical planning for abdominal tumors in children through advanced 3D visualization techniques: a systematic review of future prospects.

Preoperative three-dimensional (3D) reconstruction using sectional imaging is increasingly used in challenging pediatric cases to aid in surgical planning. Many case series have described various teams' experiences, discussing feasibility and realism, while emphasizing the technological potential for children. Nonetheless, general knowledge on this topic remains limited compared to the broader research landscape. The aim of this review was to explore the current devices and new opportunities provided by preoperative Computed Tomography (CT) scans or Magnetic Resonance Imaging (MRI). A systematic review was conducted to screen pediatric cases of abdominal and pelvic tumors with preoperative 3D reconstruction published between 2000 and 2023. Surgical planning was facilitated through virtual reconstruction or 3D printing. Virtual reconstruction of complex tumors enables precise delineation of solid masses, formulation of dissection plans, and suggests dedicated vessel ligation, optimizing tissue preservation. Vascular mapping is particularly relevant for liver surgery, large neuroblastoma with imaging-defined risk factors (IDRFs), and tumors encasing major vessels, such as complex median retroperitoneal malignant masses. 3D printing can facilitate specific tissue preservation, now accessible with minimally invasive procedures like partial nephrectomy. The latest advancements enable neural plexus reconstruction to guide surgical nerve sparing, for example, hypogastric nerve modelling, typically adjacent to large pelvic tumors. New insights will soon incorporate nerve plexus images into anatomical segmentation reconstructions, facilitated by non-irradiating imaging modalities like MRI. Although not yet published in pediatric surgical procedures, the next anticipated advancement is augmented reality, enhancing real-time intraoperative guidance: the surgeon will use a robotic console overlaying functional and anatomical data onto a magnified surgical field, enhancing robotic precision in confined spaces.

Standards for providing emergency care at the prehospital and early hospital stages for patients with mechanical jaundice

Mechanical jaundice is a condition characterized by obstructive disorders in the biliary system, posing a diagnostic challenge that requires advanced visualization techniques and laboratory tests for accurate diagnosis. This article explores the role of biochemical markers, presents an analysis of the algorithm for laboratory diagnosis and visualization techniques in order to stratify patients with jaundice syndrome, and conduct timely and effective conservative treatment with further surgical interventions. A comprehensive diagnostic and therapeutic algorithm has been developed whose use by emergency physicians at the prehospital and early hospital stages will lead to a rapid and qualitative elimination of biliary obstruction, a reduction in the level of complications, and the achievement of optimal treatment outcomes for patients.

Money market insights in China: Evidence from visual analytics approach

This research employs visual analytics approaches to demystify the complex dynamics of China’s money market, spanning from 1984 to 2020. Our objective is to transform intricate financial data into intuitive visual representations, thereby enhancing understanding and decision-making. We utilize advanced visual analytics techniques to analyze key aspects like money supply, deposits, loans, and foreign exchange. The study reveals significant trends and insights, contributing to a more comprehensive understanding of financial dynamics in China. These findings serve as valuable tools for economists and policymakers, guiding more informed decisions in financial governance.

Theory-Guided Feature Selection in Cybercrime Data Science

Cybercrime data science is being significantly hampered by the presence of 'noisy' features within vast and complex datasets. We draw from the theoretical insights of the behavioural sciences to propose a feature selection model to enrich and improve the value and interpretability of cybercrime intelligence datasets. We piloted our theory-guided feature selection approach on a subset of intelligence datafeeds provided by a global fraud and cybercrime tracking firm. The results of the proposed social influence feature selection model show significant improvement in the interpretability of the machine learning-based exploratory analysis and advanced visualization techniques in an experimental setting. The feature selection model yielded rich insights about cybercriminal psychological tactics from social engineering scam data and has potential applicability in the areas of cyberthreat response and cybersecurity awareness training. Our study shows the value of an interdisciplinary theory-guided approach to cybercrime data analytics that integrates scientific knowledge from the behavioural sciences and data science expertise. Our paper concludes by suggesting avenues for future research on theory-guided feature selection seeking to incorporate behavioural science knowledge in cybercrime data science. We intend to refine, automate, evaluate, and scale our model in future research to assess its effectiveness in producing insights about cybercriminal activities and informing decision-making in a naturalistic and real-time setting. In future research efforts, we aim to automate the encoding of features and apply a wider range of machine learning tools and evaluation metrics to extract more meaningful insights into cybercriminal psychological tactics. We also intend to refine our model on larger datasets to enhance its efficiency and responsiveness to real-time cybercrime data. We call on data scientists and cybercrime domain experts to work together to apply theory-guided feature selection to improve processes of knowledge discovery that enhance our cybersecurity capabilities.

Blending Conventional Learning Approach with Contemporary Visualization Technologies: Prerequisite Toward Evidence-Based Surface Anatomy.

Proficiency in surface anatomy knowledge plays a pivotal role in fostering complication-free and efficacious clinical practice across a wide spectrum of healthcare specialties. This comprehensive understanding and adept utilization of surface anatomy principles serve as the linchpin for deciphering normal anatomical structures within medical imaging and the aptitude to articulate the topographical attributes, visual characteristics, and interrelations of palpable anatomical entities. Despite the advent of advanced direct visualization techniques, such as ultrasound guidance, which assist in interventional procedures, clinicians have not relinquished the indispensable requirement for pertinent surface anatomy knowledge to ensure the successful and secure execution of procedures. Regrettably, evidence-based surface anatomy remains an underemphasized facet in the pedagogy of anatomical sciences. The current narrative review underscores the various methodologies employed in imparting surface anatomy education to students. However, it is noteworthy that no singular, unequivocal best practice has emerged for the teaching and acquisition of surface anatomy knowledge. Therefore, as custodians of anatomical education, there is a pressing need to innovate and amalgamate contemporary pedagogical approaches with state-of-the-art technologies to furnish students with evidence-based surface anatomy insights, thereby enhancing comprehension, retention, and the lasting utility of this essential domain of medical knowledge.

Comparative study of time-frequency transformation methods for ECG signal classification

In this study, we highlighted the growing need for automated electrocardiogram (ECG) signal classification using deep learning to overcome the limitations of traditional ECG interpretation algorithms that can lead to misdiagnosis and inefficiency. Convolutional neural networks (CNN) application to ECG signals is gaining significant attention owing to their exceptional image-classification capabilities. However, we addressed the lack of standardized methods for converting 1D ECG signals into 2D-CNN-compatible input images by using time-frequency methods and selecting hyperparameters associated with these methods, particularly the choice of function. Furthermore, we investigated the effects of fine-tuned training, a technique where pre-trained weights are adapted to a specific dataset, on 2D-CNNs for ECG classification. We conducted the experiments using the MIT-BIH Arrhythmia Database, focusing on classifying premature ventricular contractions (PVCs) and abnormal heartbeats originating from ventricles. We employed several CNN architectures pre-trained on ImageNet and fine-tuned using the proposed ECG datasets. We found that using the Ricker Wavelet function outperformed other feature extraction methods with an accuracy of 96.17%. We provided crucial insights into CNNs for ECG classification, underscoring the significance of fine-tuning and hyperparameter selection in image transformation methods. The findings provide valuable guidance for researchers and practitioners, improving the accuracy and efficiency of ECG analysis using 2D-CNNs. Future research avenues may include advanced visualization techniques and extending CNNs to multiclass classification, expanding their utility in medical diagnosis.