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    Signal-piloted processing metaheuristic optimization and wavelet decomposition based elucidation of arrhythmia for mobile healthcare

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    SPP_MOFS_WD_Arrhythmia_RG-3.pdf
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    Type
    Article
    Author
    Mian Qaisar, Saeed cc
    Khan, Sibghatullah
    Dallet, Dominique cc
    Pławiak, Paweł cc
    Krichen, Moez cc
    Subject
    Arrhythmia classification
    Compression
    Dimension reduction
    Electrocardiogram (ECG)
    Feature extraction
    Healthcare
    Level-crossing sampling; Multirate processing
    Metaheuristic optimization
    Machine learning
    QRS selection
    Wavelet decomposition
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    Date
    2022
    
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    Abstract
    The next generation healthcare systems will be based on the cloud connected wireless biomedical wearables. The key performance indicators of such systems are the compression, computational efficiency, transmission and power effectiveness with precision. The electrocardiogram (ECG) signals processing based novel technique is presented for the diagnosis of arrhythmia. It employs a novel mix of the Level-Crossing Sampling (LCS), Enhanced Activity Selection (EAS) based QRS complex selection, multirate processing, Wavelet Decomposition (WD), Metaheuristic Optimization (MO), and machine learning. The MIT-BIH dataset is used for experimentation. Dataset contains 5 classes namely, "Atrial premature contraction", "premature ventricular contraction", "right bundle branch block", "left bundle branch block" and "normal sinus". For each class, 450 cardiac pulses are collected from 3 different subjects. The performance of Marine Predators Algorithm (MPA) and Artificial Butterfly Optimization Algorithm (ABOA) is investigated for features selection. The selected features sets are passed to classifiers that use machine learning for an automated diagnosis. The performance is tested by using multiple evaluation metrics while following the 10-fold cross validation (10-CV). The LCS and EAS results in a 4.04-times diminishing in the average count of collected samples. The multirate processing lead to a more than 7-times computational effectiveness over the conventional fix-rate counterparts. The respective dimension reduction ratios and classification accuracies, for the MPA and ABOA algorithms, are 29.59-times & 22.19-times and 98.38% & 98.86%
    Publisher
    Elsevier B.V.
    Journal title
    Biocybernetics and Biomedical Engineering
    DOI
    10.1016/j.bbe.2022.05.006
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.bbe.2022.05.006
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