Muhammad Sohail Irshad^1,2,*, Tehreem Masood^1,2, Arfan Jaffar^1,2, Muhammad Rashid³, Sheeraz Akram^1,2,4,*, Abeer Aljohani⁵

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4805-4824, 2024, DOI:10.32604/cmc.2024.050931 - 20 June 2024

Abstract The application of deep learning techniques in the medical field, specifically for Atrial Fibrillation (AFib) detection through Electrocardiogram (ECG) signals, has witnessed significant interest. Accurate and timely diagnosis increases the patient’s chances of recovery. However, issues like overfitting and inconsistent accuracy across datasets remain challenges. In a quest to address these challenges, a study presents two prominent deep learning architectures, ResNet-50 and DenseNet-121, to evaluate their effectiveness in AFib detection. The aim was to create a robust detection mechanism that consistently performs well. Metrics such as loss, accuracy, precision, sensitivity, and Area Under the Curve… More >

Iqra Afzal¹, Fiaz Majeed¹, Muhammad Usman Ali², Shahzada Khurram³, Akber Abid Gardezi⁴, Shafiq Ahmad⁵, Saad Aladyan⁵, Almetwally M. Mostafa⁶, Muhammad Shafiq^7,*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2191-2207, 2022, DOI:10.32604/cmc.2022.022711 - 07 December 2021

Abstract In this era of electronic health, healthcare data is very important because it contains information about human survival. In addition, the Internet of Things (IoT) revolution has redefined modern healthcare systems and management by providing continuous monitoring. In this case, the data related to the heart is more important and requires proper analysis. For the analysis of heart data, Electrocardiogram (ECG) is used. In this work, machine learning techniques, such as adaptive boosting (AdaBoost) is used for detecting normal sinus rhythm, atrial fibrillation (AF), and noise in ECG signals to improve the classification accuracy. The… More >

Jianming Wang, Xianyang Zhu^*, Xiaotang Sheng, Jingsong Geng, Jiawang Xiao, Qiguang Wang^*

Congenital Heart Disease, Vol.17, No.1, pp. 61-70, 2022, DOI:10.32604/CHD.2022.017225 - 26 October 2021

Abstract Background: Left atrial appendage closure (LAAC) with simultaneous interventional occlusion therapy for congenital interatrial communication has become a new focus of patients with nonvalvular atrial fibrillation. Little is known about the results of mid-and long-term results. Objective: The aim of this study was to evaluate the midand long-term safety and effectiveness of simultaneous transcatheter closure of the left atrial appendage (LAA) and congenital interatrial communication closure in atrial fibrillation (AF) patients. Methods: From Jan 2016 to June 2017, 27 patients with AF were treated with simultaneous transcatheter closure of the LAA and atrial septal defect (ASD, n… More >

YUAN SONG^1,#, CAI WEI^2,#, JINGJING WANG^3,*

BIOCELL, Vol.45, No.3, pp. 733-744, 2021, DOI:10.32604/biocell.2021.013236 - 03 March 2021

Abstract Oxidative stress-mediated cell death in cardiomyocytes contributes to the development of atrial fibrillation. However, the detailed mechanisms are still unclear. In the present study, we established atrial fibrillation models in mice. The cardiomyocytes were isolated from atrial fibrillation mice and normal mice and were cultured in vitro, respectively. The results showed that cell proliferation and viability in cardiomyocytes with atrial fibrillation were significantly lower than the cells from the normal mice. Consistently, atrial fibrillation cardiomyocytes were prone to suffer from apoptotic cell death. Also, the oxidative stress and ferroptosis-associated signatures were significantly increased in atrial… More >

Yunxiang Zhao¹, Jinyong Cheng^{1, *}, Ping Zhang¹, Xueping Peng²

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1615-1628, 2020, DOI:10.32604/cmc.2020.09938 - 30 June 2020

Abstract Atrial fibrillation is the most common persistent form of arrhythmia. A method based on wavelet transform combined with deep convolutional neural network is applied for automatic classification of electrocardiograms. Since the ECG signal is easily inferred, the ECG signal is decomposed into 9 kinds of subsignals with different frequency scales by wavelet function, and then wavelet reconstruction is carried out after segmented filtering to eliminate the influence of noise. A 24-layer convolution neural network is used to extract the hierarchical features by convolution kernels of different sizes, and finally the softmax classifier is used to More >

Michael A. Brock, John‐Anthony Coppola, Jana Reid, Diego Moguillansky

Congenital Heart Disease, Vol.14, No.6, pp. 924-930, 2019, DOI:10.1111/chd.12857

Abstract Objective: The primary aim of our work is to determine the incidence of atrial fibrillation following cardiac surgery in adults with congenital heart disease. Secondary aims include identifying risk factors predictive of developing early postoperative atrial fibrillation and morbidities associated with early postoperative atrial fibrillation.
Design: Retrospective analysis.
Setting: Single center, quaternary care children’s hospital.
Patients: This review included patients at least 18 years of age with known congenital heart disease who underwent cardiac surgery requiring a median sternotomy at our congenital heart center from January 1, 2012 to December 31, 2016.
Interventions: None.
Outcome Measures: The primary outcome was early… More >