Zhuoxi Feng^#,1, Jinyang Liu^#,2, Zihao Wu¹, Ziran Geng¹, Zhimin Liu^1,*

Congenital Heart Disease, Vol.20, No.5, pp. 625-636, 2025, DOI:10.32604/chd.2025.069214 - 30 November 2025

Abstract Congenitally corrected transposition of the great arteries (CCTGA) is a rare congenital heart disease characterized by atrioventricular, ventriculoarterial, and conduction system discordance, commonly accompanied by atrioventricular block (AVB). Pacing in patients with CCTGA and AVB (both pediatric and adult) poses challenges in strategy selection, procedural complexity, and clinical decision-making due to limited evidence. Conventional morphological left ventricular pacing is widely adopted but may induce ventricular dyssynchrony, heart failure, and tricuspid valve dysfunction. While cardiac resynchronization therapy serves as an upgrade for pacing-induced cardiomyopathy and heart failure, its application may be limited by coronary sinus anatomical… More >

Xinchao Han^1,2, Aojun Zhang^1,2, Runchuan Li^1,2,*, Shengya Shen³, Di Zhang^1,2, Bo Jin^1,2, Longfei Mao^1,2, Linqi Yang^1,2, Shuqin Zhang^1,2

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3443-3465, 2025, DOI:10.32604/cmc.2024.059403 - 17 February 2025

Abstract Electrocardiogram (ECG) analysis is critical for detecting arrhythmias, but traditional methods struggle with large-scale Electrocardiogram data and rare arrhythmia events in imbalanced datasets. These methods fail to perform multi-perspective learning of temporal signals and Electrocardiogram images, nor can they fully extract the latent information within the data, falling short of the accuracy required by clinicians. Therefore, this paper proposes an innovative hybrid multimodal spatiotemporal neural network to address these challenges. The model employs a multimodal data augmentation framework integrating visual and signal-based features to enhance the classification performance of rare arrhythmias in imbalanced datasets. Additionally, More >

Manouk H. C. Linderhof^1,#, Hoang H. Nguyen^1,2,#, Annemien E. van den Bosch¹, Mathijs S. van Schie¹, Vehpi Yildirim¹, Yannick J. H. J. Taverne³, Natasja M. S. de Groot^1,*

Congenital Heart Disease, Vol.19, No.6, pp. 577-592, 2024, DOI:10.32604/chd.2025.062129 - 27 January 2025

Abstract Surgical ablation (SA) has become an essential rhythm-control strategy for managing tachyarrhythmias in patients with congenital heart disease. Atrial tachyarrhythmias, such as atrial flutter and atrial fibrillation, are prevalent in congenital heart disease, affecting up to 50% of patients, and pose significant risks, including increased morbidity and mortality. Ventricular tachyarrhythmias, though less common, can lead to sudden cardiac death, particularly in conditions like Tetralogy of Fallot. Prior studies suggested that SA for tachyarrhythmias in patients with congenital heart disease offers significant benefits, including superior long-term rhythm control compared to catheter ablation (CA). Atrial tachyarrhythmia burden… More > Graphic Abstract

Abbas Ali Hassan, Fardin Abdali-Mohammadi^*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 971-983, 2024, DOI:10.32604/cmc.2024.053817 - 15 October 2024

Abstract From a medical perspective, the 12 leads of the heart in an electrocardiogram (ECG) signal have functional dependencies with each other. Therefore, all these leads report different aspects of an arrhythmia. Their differences lie in the level of highlighting and displaying information about that arrhythmia. For example, although all leads show traces of atrial excitation, this function is more evident in lead II than in any other lead. In this article, a new model was proposed using ECG functional and structural dependencies between heart leads. In the prescreening stage, the ECG signals are segmented from… More >

Batyrkhan Omarov^1,2,*, Meirzhan Baikuvekov¹, Daniyar Sultan¹, Nurzhan Mukazhanov³, Madina Suleimenova², Maigul Zhekambayeva³

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 341-359, 2024, DOI:10.32604/cmc.2024.052437 - 18 July 2024

Abstract This research introduces an innovative ensemble approach, combining Deep Residual Networks (ResNets) and Bidirectional Gated Recurrent Units (BiGRU), augmented with an Attention Mechanism, for the classification of heart arrhythmias. The escalating prevalence of cardiovascular diseases necessitates advanced diagnostic tools to enhance accuracy and efficiency. The model leverages the deep hierarchical feature extraction capabilities of ResNets, which are adept at identifying intricate patterns within electrocardiogram (ECG) data, while BiGRU layers capture the temporal dynamics essential for understanding the sequential nature of ECG signals. The integration of an Attention Mechanism refines the model’s focus on critical segments… More >

Maie Aboghazalah^1,*, Passent El-kafrawy², Abdelmoty M. Ahmed³, Rasha Elnemr⁵, Belgacem Bouallegue³, Ayman El-sayed⁴

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3855-3875, 2024, DOI:10.32604/cmc.2023.039936 - 20 June 2024

Abstract Heart monitoring improves life quality. Electrocardiograms (ECGs or EKGs) detect heart irregularities. Machine learning algorithms can create a few ECG diagnosis processing methods. The first method uses raw ECG and time-series data. The second method classifies the ECG by patient experience. The third technique translates ECG impulses into Q waves, R waves and S waves (QRS) features using richer information. Because ECG signals vary naturally between humans and activities, we will combine the three feature selection methods to improve classification accuracy and diagnosis. Classifications using all three approaches have not been examined till now. Several More >

S. Jayanthi^*, S. Prasanna Devi

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2137-2158, 2024, DOI:10.32604/cmc.2024.045975 - 27 February 2024

Abstract In healthcare, the persistent challenge of arrhythmias, a leading cause of global mortality, has sparked extensive research into the automation of detection using machine learning (ML) algorithms. However, traditional ML and AutoML approaches have revealed their limitations, notably regarding feature generalization and automation efficiency. This glaring research gap has motivated the development of AutoRhythmAI, an innovative solution that integrates both machine and deep learning to revolutionize the diagnosis of arrhythmias. Our approach encompasses two distinct pipelines tailored for binary-class and multi-class arrhythmia detection, effectively bridging the gap between data preprocessing and model selection. To validate… More >

Muhammad Aleem Raza¹, Muhammad Anwar², Kashif Nisar³, Ag. Asri Ag. Ibrahim^3,*, Usman Ahmed Raza¹, Sadiq Ali Khan⁴, Fahad Ahmad⁵

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3817-3834, 2023, DOI:10.32604/cmc.2023.032275 - 26 December 2023

Abstract With the help of computer-aided diagnostic systems, cardiovascular diseases can be identified timely manner to minimize the mortality rate of patients suffering from cardiac disease. However, the early diagnosis of cardiac arrhythmia is one of the most challenging tasks. The manual analysis of electrocardiogram (ECG) data with the help of the Holter monitor is challenging. Currently, the Convolutional Neural Network (CNN) is receiving considerable attention from researchers for automatically identifying ECG signals. This paper proposes a 9-layer-based CNN model to classify the ECG signals into five primary categories according to the American National Standards Institute More >

Charis Tan^1,2 , Diana Zannino³, Carley Clendenning³, Sophie Offen⁴, Thomas L. Gentles⁵, Julian Ayer⁶, David Tanous⁷, Vishva Wijesekera⁸, Leeanne Grigg⁹, David Celermajer^2,4,10, Mark McGuire^2,4 , Yves d’Udekem^3,11,12, Rachael Cordina^2,4,10,*

Congenital Heart Disease, Vol.18, No.5, pp. 507-523, 2023, DOI:10.32604/chd.2023.028829 - 10 November 2023

Abstract Objective: Sudden cardiac death (SCD) and malignant ventricular arrhythmia (VA) are increasingly recognized as important issues for people living with a Fontan circulation, but data are lacking. We sought to characterize the cohort who had sudden cardiac death, most likely related to VA and/or documented VA in the Australia and New Zealand Fontan Registry including risk factors and clinical outcomes. Methods: A retrospective cohort study was performed. Inclusion criteria were documented non-sustained ventricular tachycardia, sustained ventricular tachycardia, ventricular fibrillation, resuscitated cardiac arrest or SCD > 30 days post-Fontan completion. Results: Of 1611 patients, 20 (1.2%) had VA;… More >

S. R. Deepa¹, M. Subramoniam^2,*, R. Swarnalatha³, S. Poornapushpakala², S. Barani²

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 745-761, 2023, DOI:10.32604/iasc.2023.034211 - 29 April 2023

Abstract The non-invasive evaluation of the heart through EectroCardioGraphy (ECG) has played a key role in detecting heart disease. The analysis of ECG signals requires years of learning and experience to interpret and extract useful information from them. Thus, a computerized system is needed to classify ECG signals with more accurate results effectively. Abnormal heart rhythms are called arrhythmias and cause sudden cardiac deaths. In this work, a Computerized Abnormal Heart Rhythms Detection (CAHRD) system is developed using ECG signals. It consists of four stages; preprocessing, feature extraction, feature optimization and classifier. At first, Pan and… More >