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 >

Deema Mohammed Alsekait¹, Ahmed Younes Shdefat², Ayman Nabil³, Asif Nawaz^4,*, Muhammad Rizwan Rashid Rana⁴, Zohair Ahmed⁵, Hanaa Fathi⁶, Diaa Salama AbdElminaam^6,7,8

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3967-3990, 2024, DOI:10.32604/cmc.2024.054591 - 12 September 2024

Abstract Heart disease remains a leading cause of morbidity and mortality worldwide, highlighting the need for improved diagnostic methods. Traditional diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults, which can reduce accuracy, especially with poor-quality images. Additionally, these methods often require significant time and expertise, making them less accessible in resource-limited settings. Emerging technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision, ultimately improving patient outcomes and reducing healthcare costs. This study introduces Heart-Net, a multi-modal deep learning framework designed to… More >

Koren Hyogene Kwag^1,*, Ibrahim Kak¹, Ying Li², Walid Khass¹, Alec McKechnie¹, Oksana Nulman¹, Brande Brown¹, Manoj Chhabra¹

Congenital Heart Disease, Vol.19, No.1, pp. 107-121, 2024, DOI:10.32604/chd.2024.045953 - 20 March 2024

Abstract Objective: Accurate measurement of QT interval, the ventricular action potential from depolarization to repolarization, is important for the early detection of Long QT syndrome. The most effective QT correction (QTc) formula has yet to be determined in the pediatric population, although it has intrinsically greater extremes in heart rate (HR) and is more susceptible to errors in measurement. The authors of this study compare six different QTc methods (Bazett, Fridericia, Framingham, Hodges, Rautaharju, and a computer algorithm utilizing the Bazett formula) for consistency against variations in HR and RR interval. Methods: Descriptive Retrospective Study. We… More >

Dengyong Zhang^1,2, Minzhi Yuan^1,2, Feng Li^1,2, Lebing Zhang^3,*, Yanqiang Sun⁴, Yiming Ling⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2809-2824, 2024, DOI:10.32604/cmes.2023.029181 - 15 December 2023

Abstract Electrocardiogram (ECG) signal is one of the noninvasive physiological measurement techniques commonly used in cardiac diagnosis. However, in real scenarios, the ECG signal is susceptible to various noise erosion, which affects the subsequent pathological analysis. Therefore, the effective removal of the noise from ECG signals has become a top priority in cardiac diagnostic research. Aiming at the problem of incomplete signal shape retention and low signal-to-noise ratio (SNR) after denoising, a novel ECG denoising network, named attention-based residual dense shrinkage network (ARDSN), is proposed in this paper. Firstly, the shallow ECG characteristics are extracted by 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 >

Premanand S., Sathiya Narayanan^*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 25-45, 2023, DOI:10.32604/cmc.2023.042590 - 31 October 2023

Abstract Machine Learning (ML) and Deep Learning (DL) technologies are revolutionizing the medical domain, especially with Electrocardiogram (ECG), by providing new tools and techniques for diagnosing, treating, and preventing diseases. However, DL architectures are computationally more demanding. In recent years, researchers have focused on combining the computationally less intensive portion of the DL architectures with ML approaches, say for example, combining the convolutional layer blocks of Convolution Neural Networks (CNNs) into ML algorithms such as Extreme Gradient Boosting (XGBoost) and K-Nearest Neighbor (KNN) resulting in CNN-XGBoost and CNN-KNN, respectively. However, these approaches are homogenous in the… More >

Batyrkhan Omarov^1,*, Meirzhan Baikuvekov¹, Zeinel Momynkulov², Aray Kassenkhan³, Saltanat Nuralykyzy³, Mereilim Iglikova³

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3745-3761, 2023, DOI:10.32604/cmc.2023.042627 - 08 October 2023

Abstract Heart disease is a leading cause of mortality worldwide. Electrocardiograms (ECG) play a crucial role in diagnosing heart disease. However, interpreting ECG signals necessitates specialized knowledge and training. The development of automated methods for ECG analysis has the potential to enhance the accuracy and efficiency of heart disease diagnosis. This research paper proposes a 3D Convolutional Long Short-Term Memory (Conv-LSTM) model for detecting heart disease using ECG signals. The proposed model combines the advantages of both convolutional neural networks (CNN) and long short-term memory (LSTM) networks. By considering both the spatial and temporal dependencies of… More >

Muhammad Tayyeb¹, Muhammad Umer¹, Khaled Alnowaiser², Saima Sadiq³, Ala’ Abdulmajid Eshmawi⁴, Rizwan Majeed⁵, Abdullah Mohamed⁶, Houbing Song⁷, Imran Ashraf^8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1677-1694, 2023, DOI:10.32604/cmes.2023.026535 - 26 June 2023

Abstract Cardiovascular problems have become the predominant cause of death worldwide and a rise in the number of patients has been observed lately. Currently, electrocardiogram (ECG) data is analyzed by medical experts to determine the cardiac abnormality, which is time-consuming. In addition, the diagnosis requires experienced medical experts and is error-prone. However, automated identification of cardiovascular disease using ECGs is a challenging problem and state-of-the-art performance has been attained by complex deep learning architectures. This study proposes a simple multilayer perceptron (MLP) model for heart disease prediction to reduce computational complexity. ECG dataset containing averaged signals More >

Doaa Sami Khafaga¹, Amel Ali Alhussan^1,*, Abdelaziz A. Abdelhamid^2,3, Abdelhameed Ibrahim⁴, Mohamed Saber⁵, El-Sayed M. El-kenawy^6,7

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1469-1482, 2023, DOI:10.32604/csse.2023.031943 - 03 November 2022

Abstract Arrhythmia has been classified using a variety of methods. Because of the dynamic nature of electrocardiogram (ECG) data, traditional handcrafted approaches are difficult to execute, making the machine learning (ML) solutions more appealing. Patients with cardiac arrhythmias can benefit from competent monitoring to save their lives. Cardiac arrhythmia classification and prediction have greatly improved in recent years. Arrhythmias are a category of conditions in which the heart's electrical activity is abnormally rapid or sluggish. Every year, it is one of the main reasons of mortality for both men and women, worldwide. For the classification of… More >

Doaa Sami Khafaga¹, El-Sayed M. El-kenawy^2,3, Faten Khalid Karim^1,*, Sameer Alshetewi⁴, Abdelhameed Ibrahim⁵, Abdelaziz A. Abdelhamid^6,7, D. L. Elsheweikh⁸

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2379-2395, 2023, DOI:10.32604/cmc.2023.032886 - 31 October 2022

Abstract Electrocardiogram (ECG) signal is a measure of the heart’s electrical activity. Recently, ECG detection and classification have benefited from the use of computer-aided systems by cardiologists. The goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization (DTO) and Differential Evolution Algorithm (DEA) into a unified algorithm to optimize the hyperparameters of neural network (NN) for boosting the ECG classification accuracy. In addition, we proposed a new feature selection method for selecting the significant feature that can improve the overall performance. To prove the superiority of the More >