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  • Open Access


    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

    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 collected. The R peaks were… More >

  • Open Access


    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


    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. In this view, this study… More >

  • 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


    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 that analyze global training… More >

  • Open Access


    Evaluation of Pencil Lead Based Electrodes for Electrocardiogram Monitoring in Hot Spring

    Ratha Yeu1, Namhui Ra2, Seong-A Lee3, Yunyoung Nam4,*

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1411-1425, 2021, DOI:10.32604/cmc.2020.013761

    Abstract Electrocardiogram (ECG) electrodes are conductive pads applied to the skin to measure cardiac activity. Ag/AgCl electrodes are the commercial product which widely used to obtain ECGs. When monitoring the ECG in a hot spring, Ag/AgCl electrodes must be waterproofed; however, this is time-consuming, and the adhesive may tear the skin on removal. For solving the problem, we developed the carbon pencil lead (CPL) electrodes for use in hot springs. Both CPL and Ag/AgCl electrodes were connected to ECG100C’s cables. The Performance was evaluated in three conditions as following: hot spring water with and without bubble, and in cold water. In… More >

  • Open Access


    PDNet: A Convolutional Neural Network Has Potential to be Deployed on Small Intelligent Devices for Arrhythmia Diagnosis

    Fei Yang1,2,#, Xiaoqing Zhang1,*,#, Yong Zhu3

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 365-382, 2020, DOI:10.32604/cmes.2020.010798

    Abstract Heart arrhythmia is a group of irregular heartbeat conditions and is usually detected by electrocardiograms (ECG) signals. Over the past years, deep learning methods have been developed to classify different types of heart arrhythmias through ECG based on computer-aided diagnosis systems (CADs), but these deep learning methods usually cannot trade-off between classification performance and parameters of deep learning methods. To tackle this problem, this work proposes a convolutional neural network (CNN) model named PDNet to recognize different types of heart arrhythmias efficiently. In the PDNet, a convolutional block named PDblock is devised, which is comprised of a pointwise convolutional layer… More >

  • Open Access


    Probabliistic Analysis Of Electrocardiogram (Ecg) Heart Signal

    Amjad Gawanmeh1,3,∗, Usman Pervez2, Osman Hasan2,3

    Computer Systems Science and Engineering, Vol.33, No.1, pp. 21-29, 2018, DOI:10.32604/csse.2018.33.021

    Abstract Electrocardiography (ECG) is a heart signal wave that is recorded using medical sensors, which are normally attached to the human body by the heart. ECG waves have repetitive patterns that can be efficiently used in the diagnosis of heart problems as they carry several characteristics of heart operation. Traditionally, the analysis of ECG waves is done using informal techniques, like simulation, which is in-exhaustive and thus the analysis results may lead to ambiguities and life threatening scenarios in extreme cases. In order to overcome such problems, we propose to analyze ECG heart signals using probabilistic model checking, which is a… More >

  • Open Access


    Utility of incomplete right bundle branch block as an isolated ECG finding in children undergoing initial cardiac evaluation

    Omar Meziab, Dominic J. Abrams, Mark E. Alexander, Laura Bevilacqua, Vassilios Bezzerides, Doug Y. Mah, Edward P. Walsh, John K. Triedman

    Congenital Heart Disease, Vol.13, No.3, pp. 419-427, 2018, DOI:10.1111/chd.12589

    Abstract Objective: This study evaluates the ability of experienced pediatric electrophysiologists (EPs) to reliably classify incomplete right bundle branch block (IRBBB) and assesses its clinical utility as an isolated ECG finding in a group of healthy outpatient children without prior cardiac evaluation.
    Design: We performed a retrospective analysis of all electrocardiographic and echocardiographic records at Boston Children’s Hospital between January 1, 2005, and December 31, 2014. Echocardiographic diagnoses were identified if registered between the date of the index electrocardiogram and the ensuing year. A selected subset of 473 ECGs was subsequently reanalyzed in a blinded manner by six pediatric EPs to… More >

  • Open Access


    Application of pediatric Appropriate Use Criteria for initial outpatient evaluation of asymptomatic patients with abnormal electrocardiograms

    Soham Dasgupta1, Shae Anderson1, Michael Kelleman2, Ritu Sachdeva1

    Congenital Heart Disease, Vol.14, No.2, pp. 230-235, 2019, DOI:10.1111/chd.12687

    Abstract Introduction: In the pediatric Appropriate Use Criteria (AUC), abnormal electrocardiogram (ECG) in an asymptomatic patient has been rated as an “Appropriate” indication for transthoracic echocardiogram (TTE). We hypothesized that the yield of abnormal findings on TTE for this indication will be low.
    Methods: All asymptomatic patients (≤ 18 years) from January 1, 2015 to December 31, 2017 who underwent initial outpatient evaluation at our center and had a TTE ordered for an abnormal ECG, were included. Clinic records were reviewed to obtain ECG and TTE findings.
    Results: Of the 199 study patients, 13 (6.5%) had abnormal findings. Incomplete right bundle… More >

  • Open Access


    A Mobile Cloud-Based eHealth Scheme

    Yihe Liu1, Aaqif Afzaal Abbasi2, Atefeh Aghaei3, Almas Abbasi4, Amir Mosavi5, 6, 7, Shahaboddin Shamshirband8, 9, *, Mohammed A. A. Al-qaness10

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 31-39, 2020, DOI:10.32604/cmc.2020.07708

    Abstract Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid pace. Similarly, the field of health informatics is also considered as an extremely important field. This work observes the collaboration between these two fields to solve the traditional problem of extracting Electrocardiogram signals from trace reports and then performing analysis. The developed system has two front ends, the first dedicated for the user to perform the photographing of the trace report. Once the photographing is complete, mobile computing is used to extract the signal. Once the signal is extracted, it is uploaded into the… More >

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