S. Jayanthi^*, S. Prasanna Devi

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

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 our system, we have rigorously… 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

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 (ANSI) standards and the Association… 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

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; 14 (1.0%) had… 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

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 Tompkins algorithm is employed to… More >

Fatma Taher¹, Hamoud Alshammari², Lobna Osman³, Mohamed Elhoseny⁴, Abdulaziz Shehab^5,2,*, Eman Elayat⁶

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4485-4499, 2023, DOI:10.32604/cmc.2023.036118

Abstract Cardiac diseases are one of the greatest global health challenges. Due to the high annual mortality rates, cardiac diseases have attracted the attention of numerous researchers in recent years. This article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia diseases. The fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection algorithms. An ensemble of classifiers is then applied to the fusion’s results. The proposed model classifies the arrhythmia dataset from the University of California, Irvine into normal/abnormal classes as well as 16 classes of arrhythmia. Initially, at the preprocessing steps,… More >

Elio Caruso^1,*, Silvia Farruggio¹, Alfredo Di Pino¹, Paolo Guccione¹, Mohammadrafie Khorgami²

Congenital Heart Disease, Vol.17, No.5, pp. 551-556, 2022, DOI:10.32604/chd.2022.023711

Abstract We present the case of an infant admitted to our department for a rapid broad complex tachycardia and cardiovascular collapse. The patient was submitted to genetic testing because of a conduction defect at baseline ECG and family history of gene mutation. A new SCN5A gene mutation variant was found leading to diagnosis of sodium-channel dysfunction arrhythmia. More > Graphic Abstract

Federica Caldaroni¹, Massimo Chessa², Alessandro Varrica¹, Alessandro Giamberti^1,*

Congenital Heart Disease, Vol.17, No.4, pp. 491-494, 2022, DOI:10.32604/chd.2022.019119

Abstract Multimodal imaging, including augmented or mixed reality, transforms the physicians’ interaction with clinical imaging, allowing more accurate data interpretation, better spatial resolution, and depth perception of the patient’s anatomy. We successfully overlay 3D holographic visualization to magnetic resonance imaging images for preoperative decision making of a complex case of cardiac tumour in a 7-year-old girl. More >

Fuad A. M. Al-Yarimi^*

Computer Systems Science and Engineering, Vol.44, No.1, pp. 129-142, 2023, DOI:10.32604/csse.2023.024297

Abstract A critical component of dealing with heart disease is real-time identification, which triggers rapid action. The main challenge of real-time identification is illustrated here by the rare occurrence of cardiac arrhythmias. Recent contributions to cardiac arrhythmia prediction using supervised learning approaches generally involve the use of demographic features (electronic health records), signal features (electrocardiogram features as signals), and temporal features. Since the signal of the electrical activity of the heartbeat is very sensitive to differences between high and low heartbeats, it is possible to detect some of the irregularities in the early stages of arrhythmia. This paper describes the training… More >

Jian Liu^1,*, Xiaodong Xia¹, Chunyang Han², Jiao Hui³, Jim Feng⁴

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 265-278, 2022, DOI:10.32604/cmc.2022.029227

Abstract As a common and high-risk type of disease, heart disease seriously threatens people’s health. At the same time, in the era of the Internet of Thing (IoT), smart medical device has strong practical significance for medical workers and patients because of its ability to assist in the diagnosis of diseases. Therefore, the research of real-time diagnosis and classification algorithms for arrhythmia can help to improve the diagnostic efficiency of diseases. In this paper, we design an automatic arrhythmia classification algorithm model based on Convolutional Neural Network (CNN) and Encoder-Decoder model. The model uses Long Short-Term Memory (LSTM) to consider the… More >

Ajina Mohamed Ameer^*, M. Victor Jose

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1349-1361, 2022, DOI:10.32604/iasc.2022.023924

Abstract This paper presents a novel approach for arrhythmia detection and classification using modified recurrent neural network. In medicine and analytics, arrhythmia detections is a hot topic, specifically when it comes to cardiac identification. In the research methodology, there are 4 main steps. Acquisition and pre-processing of data, electrocardiogram (ECG) feature extraction utilizing QRS (Quick Response Systems) peak, and ECG signal classification using a Modified Recurrent Neural Network (Modified RNN) for arrhythmia diagnosis. The Massachusetts Institute of Technology-Beth Israel Hospital. (MIT-BIH) Arrhythmia database was used, as well as the image accuracy. Medium filter is used in the pre-processing. Feature extraction is… More >