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

3Citations
Citations of this article
3Readers
Mendeley users who have this article in their library.
Get full text

Abstract

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.

Author supplied keywords

Cite

CITATION STYLE

APA

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

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free