Extraction of Linear and Non-Linear Features of Electrocardiogram Signal and Classification

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ECG signal for a private creature is totally different because of the distinctive heart structure. The ambition of feature extraction of electrocardiogram signal would permit productive detection of irregularities and economic projection due to any kind of heart confusion. Some dominant feature options are going to be extracted from ECG signals namely frequency, mean, median, skewness, kurtosis, standard deviation, different kinds of norms and so on. So, there is a need for strong and robust mathematical model to extract such helpful parameters. This research work is related to an associate degree reconciling mathematical analysis model i.e. Hilbert Huang Transform (HHT). The Hilbert-Huang transform technique is enforced to evaluate the nonlinear and non-stationary representation of the graphical signal. It is distinctive and totally disparate from the current ways of investigation and will not crave a prior function supporting information. The efficiency of the planned theme is confirmed through different classification techniques.

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Deb, S., Rabiul Islam, S. M., Johura, F. T., & Huang, X. (2018). Extraction of Linear and Non-Linear Features of Electrocardiogram Signal and Classification. In 2nd International Conference on Electrical and Electronic Engineering, ICEEE 2017 (pp. 1–4). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/CEEE.2017.8412857

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