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  • Open Access

    ARTICLE

    Heart Disease Classification Using Multiple K-PCA and Hybrid Deep Learning Approach

    S. Kusuma*, Dr. Jothi K. R

    Computer Systems Science and Engineering, Vol.41, No.3, pp. 1273-1289, 2022, DOI:10.32604/csse.2022.021741 - 10 November 2021

    Abstract One of the severe health problems and the most common types of heart disease (HD) is Coronary heart disease (CHD). Due to the lack of a healthy lifestyle, HD would cause frequent mortality worldwide. If the heart attack occurs without any symptoms, it cannot be cured by an intelligent detection system. An effective diagnosis and detection of CHD should prevent human casualties. Moreover, intelligent systems employ clinical-based decision support approaches to assist physicians in providing another option for diagnosing and detecting HD. This paper aims to introduce a heart disease prediction model including phases like… More >

  • Open Access

    ARTICLE

    Cardiovascular Disease Prediction Among the Malaysian Cohort Participants Using Electrocardiogram

    Mohd Zubir Suboh1,2, Nazrul Anuar Nayan1,3,*, Noraidatulakma Abdullah4,5, Nurul Ain Mhd Yusof4, Mariatul Akma Hamid4, Azwa Shawani Kamalul Arinfin4, Syakila Mohd Abd Daud4, Mohd Arman Kamaruddin4, Rosmina Jaafar1, Rahman Jamal4

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1111-1132, 2022, DOI:10.32604/cmc.2022.022123 - 03 November 2021

    Abstract A comprehensive study was conducted to differentiate cardiovascular disease (CVD) subjects from non-CVD subjects using short recording electrocardiogram (ECG) of 244 Malaysian adults in The Malaysian Cohort project. An automated peak detection algorithm to detect nine fiducial points of electrocardiogram (ECG) was developed. Forty-eight features were extracted in both time and frequency domains, including statistical features obtained from heart rate variability and Poincare plot analysis. These include five new features derived from spectrum counts of five different frequency ranges. Feature selection was then made based on p-value and correlation matrix. Selected features were used as… More >

  • Open Access

    ARTICLE

    Intelligent Biomedical Electrocardiogram Signal Processing for Cardiovascular Disease Diagnosis

    R. Krishnaswamy1,*, B. Sivakumar2, B. Viswanathan3, Fahd N. Al-Wesabi4,5, Marwa Obayya6, Anwer Mustafa Hilal7

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 255-268, 2022, DOI:10.32604/cmc.2022.021995 - 03 November 2021

    Abstract Automatic biomedical signal recognition is an important process for several disease diagnoses. Particularly, Electrocardiogram (ECG) is commonly used to identify cardiovascular diseases. The professionals can determine the existence of cardiovascular diseases using the morphological patterns of the ECG signals. In order to raise the diagnostic accuracy and reduce the diagnostic time, automated computer aided diagnosis model is necessary. With the advancements of artificial intelligence (AI) techniques, large quantity of biomedical datasets can be easily examined for decision making. In this aspect, this paper presents an intelligent biomedical ECG signal processing (IBECG-SP) technique for CVD diagnosis.… More >

  • Open Access

    ARTICLE

    Convolutional Neural Network-Based Identity Recognition Using ECG at Different Water Temperatures During Bathing

    Jianbo Xu, Wenxi Chen*

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1807-1819, 2022, DOI:10.32604/cmc.2022.021154 - 03 November 2021

    Abstract This study proposes a convolutional neural network (CNN)-based identity recognition scheme using electrocardiogram (ECG) at different water temperatures (WTs) during bathing, aiming to explore the impact of ECG length on the recognition rate. ECG data was collected using non-contact electrodes at five different WTs during bathing. Ten young student subjects (seven men and three women) participated in data collection. Three ECG recordings were collected at each preset bathtub WT for each subject. Each recording is 18 min long, with a sampling rate of 200 Hz. In total, 150 ECG recordings and 150 WT recordings were… 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 - 03 November 2021

    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… More >

  • Open Access

    ARTICLE

    Automated Learning of ECG Streaming Data Through Machine Learning Internet of Things

    Mwaffaq Abu-Alhaija, Nidal M. Turab*

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 45-53, 2022, DOI:10.32604/iasc.2022.021426 - 26 October 2021

    Abstract Applying machine learning techniques on Internet of Things (IoT) data streams will help achieve better understanding, predict future perceptions, and make crucial decisions based on those analytics. The collaboration between IoT, Big Data and machine learning can be found in different domains such as Health care, Smart cities, and Telecommunications. The aim of this paper is to develop a method for automated learning of electrocardiogram (ECG) streaming data to detect any heart beat anomalies. A promising solution is to use medical sensors that transfer vital signs to medical care computer systems, combined with machine learning, More >

  • Open Access

    ARTICLE

    Design and Realization of Non Invasive Fetal ECG Monitoring System

    Abdulfattah Noorwali1, Ameni Yengui2,*, Kaiçar Ammous2, Anis Ammous1

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 455-466, 2022, DOI:10.32604/iasc.2022.020574 - 26 October 2021

    Abstract Early fetal cardiac diseases and heart abnormalities can be detected and appropriately treated by monitoring fetal health during pregnancy. Advancements in computer sciences and the technology of sensors show that is possible to monitor fetal electrocardiogram (fECG). Both signal processing and experimental aspects are needed to be investigated to monitor fECG. In this study, we aim to design and realize a non invasive fECG monitoring system. In the first part of this work, a remote study process of the electrical activity of the heart is achieved. In fact, our proposed design considers transmitting the detected… More >

  • Open Access

    ARTICLE

    Heart Disease Diagnosis Using Electrocardiography (ECG) Signals

    V. R. Vimal1,*, P. Anandan2, N. Kumaratharan3

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 31-43, 2022, DOI:10.32604/iasc.2022.017622 - 26 October 2021

    Abstract

    Electrocardiogram (ECG) monitoring models are commonly employed for diagnosing heart diseases. Since ECG signals are normally acquired for a longer time duration with high resolution, there is a need to compress the ECG signals for transmission and storage. So, a novel compression technique is essential in transmitting the signals to the telemedicine center to monitor and analyse the data. In addition, the protection of ECG signals poses a challenging issue, which encryption techniques can resolve. The existing Encryption-Then-Compression (ETC) models for multimedia data fail to properly maintain the trade-off between compression performance and signal quality.

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  • Open Access

    A Global Training Model for Beat Classification Using Basic Electrocardiogram Morphological Features

    Shubha Sumesh1, John Yearwood1, Shamsul Huda1 and Shafiq Ahmad2,*

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4503-4521, 2022, DOI:10.32604/cmc.2022.015474 - 11 October 2021

    Abstract

    Clinical Study and automatic diagnosis of electrocardiogram (ECG) data always remain a challenge in diagnosing cardiovascular activities. The analysis of ECG data relies on various factors like morphological features, classification techniques, methods or models used to diagnose and its performance improvement. Another crucial factor in the methodology is how to train the model for each patient. Existing approaches use standard training model which faces challenges when training data has variation due to individual patient characteristics resulting in a lower detection accuracy. This paper proposes an adaptive approach to identify performance improvement in building a training model

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  • 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 - 22 September 2021

    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… More >

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