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  • Open Access

    ARTICLE

    Handling High Dimensionality in Ensemble Learning for Arrhythmia Prediction

    Fuad Ali Mohammed Al-Yarimi*

    Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1729-1742, 2022, DOI:10.32604/iasc.2022.022418

    Abstract Computer-aided arrhythmia prediction from ECG (electrocardiograms) is essential in clinical practices, which promises to reduce the mortality caused by inexperienced clinical practitioners. Moreover, computer-aided methods often succeed in the early detection of arrhythmia scope from electrocardiogram reports. Machine learning is the buzz of computer-aided clinical practices. Particularly, computer-aided arrhythmia prediction methods highly adopted machine learning methods. However, the high dimensionality in feature values considered for the machine learning models’ training phase often causes false alarming. This manuscript addressed the high dimensionality in the learning phase and proposed an (Ensemble Learning method for Arrhythmia Prediction) ELAP (ensemble learning-based arrhythmia prediction). The… More >

  • Open Access

    ARTICLE

    Heart Sound Analysis for Abnormality Detection

    Zainab Arshad1, Sohail Masood Bhatti2,*, Huma Tauseef3, Arfan Jaffar2

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1195-1205, 2022, DOI:10.32604/iasc.2022.022160

    Abstract According to the World Health Organization, 31% death rate in the World is because of cardiovascular diseases like heart arrhythmia and heart failure. Early diagnosis of heart problems may help in timely treatment of the patients and hence control death rate. Heart sounds are good signals of heart health if examined by an expert. Moreover, heart sounds can be analyzed with inexpensive and portable medical devices. Automatic heart sound classification can be very useful in diagnosing heart problems. Major focus of this research is to study the existing techniques for heart sound classification and develop a more sophisticated method. A… More >

  • Open Access

    ARTICLE

    Automatic Heart Disease Detection by Classification of Ventricular Arrhythmias on ECG Using Machine Learning

    Khalid Mahmood Aamir1, Muhammad Ramzan1,2, Saima Skinadar1, Hikmat Ullah Khan3, Usman Tariq4, Hyunsoo Lee5, Yunyoung Nam5,*, Muhammad Attique Khan6

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 17-33, 2022, DOI:10.32604/cmc.2022.018613

    Abstract This paper focuses on detecting diseased signals and arrhythmias classification into two classes: ventricular tachycardia and premature ventricular contraction. The sole purpose of the signal detection is used to determine if a signal has been collected from a healthy or sick person. The proposed research approach presents a mathematical model for the signal detector based on calculating the instantaneous frequency (IF). Once a signal taken from a patient is detected, then the classifier takes that signal as input and classifies the target disease by predicting the class label. While applying the classifier, templates are designed separately for ventricular tachycardia and… More >

  • Open Access

    ARTICLE

    Arrhythmia and Disease Classification Based on Deep Learning Techniques

    Ramya G. Franklin1,*, B. Muthukumar2

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 835-851, 2022, DOI:10.32604/iasc.2022.019877

    Abstract Electrocardiography (ECG) is a method for monitoring the human heart’s electrical activity. ECG signal is often used by clinical experts in the collected time arrangement for the evaluation of any rhythmic circumstances of a topic. The research was carried to make the assignment computerized by displaying the problem with encoder-decoder methods, by using misfortune appropriation to predict standard or anomalous information. The two Convolutional Neural Networks (CNNs) and the Long Short-Term Memory (LSTM) fully connected layer (FCL) have shown improved levels over deep learning networks (DLNs) across a wide range of applications such as speech recognition, prediction etc., As CNNs… More >

  • Open Access

    ARTICLE

    An Attention Based Neural Architecture for Arrhythmia Detection and Classification from ECG Signals

    Nimmala Mangathayaru1,*, Padmaja Rani2, Vinjamuri Janaki3, Kalyanapu Srinivas4, B. Mathura Bai1, G. Sai Mohan1, B. Lalith Bharadwaj1

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2425-2443, 2021, DOI:10.32604/cmc.2021.016534

    Abstract Arrhythmia is ubiquitous worldwide and cardiologists tend to provide solutions from the recent advancements in medicine. Detecting arrhythmia from ECG signals is considered a standard approach and hence, automating this process would aid the diagnosis by providing fast, cost-efficient, and accurate solutions at scale. This is executed by extracting the definite properties from the individual patterns collected from Electrocardiography (ECG) signals causing arrhythmia. In this era of applied intelligence, automated detection and diagnostic solutions are widely used for their spontaneous and robust solutions. In this research, our contributions are two-fold. Firstly, the Dual-Tree Complex Wavelet Transform (DT-CWT) method is implied… More >

  • Open Access

    ARTICLE

    3D Non-Fluoroscopic Cryoablation of Right-Sided Accessory Pathways in Children: Monocentric Study and Literature Review

    Fabrizio Drago*, Irma Battipaglia, Pietro Paolo Tamborrino, Luigina Porco, Camilla Calvieri, Mario Salvatore Russo, Vincenzo Pazzano, Romolo Remoli, Massimo Stefano Silvetti

    Congenital Heart Disease, Vol.16, No.6, pp. 561-572, 2021, DOI:10.32604/CHD.2021.016623

    Abstract Background: Cryoablation of accessory pathways (APs) is effective and very safe in children, as previously reported by our group. The aim of this retrospective study was to evaluate the current efficacy of 3D non-fluoroscopic cryoablation of right sided APs in children, comparing results obtained with the Ensite VelocityTM and the more recent Ensite PrecisionTM 3D mapping systems. Methods and Results: From January 2016 to December 2019, 102 pediatric patients [mean age 12.5 ± 2.8, 62 males (61% of total cohort)] with right APs underwent 3D non-fluoroscopic transcatheter cryoablation at our Institution. Fifteen (14.7%) patients had previously undergone catheter ablation. Acute… More >

  • Open Access

    CASE REPORT

    Arrhythmias in Common Arterial Trunk (CAT): Uncommon Atrial Tachycardia in CAT with Anomalous Pulmonary Venous Connection and Re-entry Atrial Tachycardia in CAT with HIV Seropositive Mother

    Elio Caruso1, Silvia Farruggio1,*, Davide Calvaruso1, Corrado Di Mambro1, David Angel Ortiz Ruiz1, Salvatore Agati1, Rafie Khoargami2

    Congenital Heart Disease, Vol.16, No.4, pp. 417-425, 2021, DOI:10.32604/CHD.2021.015808

    Abstract We show a brief report of two common arterial trunk cases (CAT) with different arrhythmias and discuss anatomy, clinical and diagnostic management. The burden of volume and pressure overload of this cardiac malformation may predispose to different types of arrhythmia before and after surgical repair. Because of labile hemodynamic state in this group of patients, prompt diagnosis of any arrhythmia is mandatory as the devastating factor on prognosis. The first patient with a diagnosis of CAT Type II Collett and Edwards (CE) had a particular history with HIV seropositive mother assuming antiretroviral therapy during pregnancy, who presented hyperbilirubinemia and liver… More >

  • Open Access

    REVIEW

    Fetal Bradyarrhythmias: Etiopathogenesis, Diagnosis and Treatment: Between Literature Review and Experience of a Tertiary Center

    Elio Caruso*, Silvia Farruggio, Salvatore Agati, Corrado Di Mambro

    Congenital Heart Disease, Vol.16, No.4, pp. 309-331, 2021, DOI:10.32604/CHD.2021.015470

    Abstract Fetal arrhythmias reach up around 10% of the total third-level perinatal cardiology references. Sustained bradycardia is defined as a baseline fetal heart rate (FHR) of less than 110 bpm sustained for at least 10 min. The overall incidence of malignant fetal bradyarrhythmias, such as complete atrioventricular block (AVB) and channellopathies, is relatively rare, 1:5000 pregnancies, but represents a serious emergency for the gynecologist, neonatologists, and pediatric cardiologists. Fetal complete AVB is strongly associated with maternal connective tissue disease, but it can be also associated with congenital heart disease and usually with a poorer prognosis with high risk of fetal hydrops… More >

  • Open Access

    ARTICLE

    Higher Child-Reported Internalizing and Parent-Reported Externalizing Behaviors were Associated with Decreased Quality of Life among Pediatric Cardiac Patients Independent of Diagnosis: A Cross-Sectional Mixed-Methods Assessment

    Jacqueline S. Lee1,2, Angelica Blais1,2, Julia Jackson1, Bhavika J. Patel1, Lillian Lai4, Gary Goldfield1,3, Renee Sananes5, Patricia E. Longmuir1,2,3,*

    Congenital Heart Disease, Vol.16, No.3, pp. 255-267, 2021, DOI:10.32604/CHD.2021.014628

    Abstract Background: Pediatric cardiology patients often experience decreased quality of life (QoL) and higher rates of mental illness, particularly with severe disease, but the relationship between them and comparisons across diagnostic groups are limited. This mixed-methods cross-sectional study assessed the association between QoL anxiety and behavior problems among children with structural heart disease, arrhythmia, or other cardiac diagnoses. Methods: Children (6–14 years, n = 76, 50% female) and their parents completed measures of QoL (PedsQL), behavior (BASC-2, subset of 19 children) and anxiety (MASC-2, children 8+ years). Pearson correlations/regression models examined associations between QoL, behavior and anxiety, controlling for age, sex,… More >

  • Open Access

    ARTICLE

    Cardiac Arrhythmia Disease Classification Using LSTM Deep Learning Approach

    Muhammad Ashfaq Khan, Yangwoo Kim*

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 427-443, 2021, DOI:10.32604/cmc.2021.014682

    Abstract Many approaches have been tried for the classification of arrhythmia. Due to the dynamic nature of electrocardiogram (ECG) signals, it is challenging to use traditional handcrafted techniques, making a machine learning (ML) implementation attractive. Competent monitoring of cardiac arrhythmia patients can save lives. Cardiac arrhythmia prediction and classification has improved significantly during the last few years. Arrhythmias are a group of conditions in which the electrical activity of the heart is abnormal, either faster or slower than normal. It is the most frequent cause of death for both men and women every year in the world. This paper presents a… More >

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