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Multiresolution Analysis in EEG Signal Feature Engineering for Epileptic Seizure Detection

by John Martin R., Sujatha S., Swapna S. L.
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 17
Year of Publication: 2018
Authors: John Martin R., Sujatha S., Swapna S. L.
10.5120/ijca2018916385

John Martin R., Sujatha S., Swapna S. L. . Multiresolution Analysis in EEG Signal Feature Engineering for Epileptic Seizure Detection. International Journal of Computer Applications. 180, 17 ( Feb 2018), 14-20. DOI=10.5120/ijca2018916385

@article{ 10.5120/ijca2018916385,
author = { John Martin R., Sujatha S., Swapna S. L. },
title = { Multiresolution Analysis in EEG Signal Feature Engineering for Epileptic Seizure Detection },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2018 },
volume = { 180 },
number = { 17 },
month = { Feb },
year = { 2018 },
issn = { 0975-8887 },
pages = { 14-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number17/29023-2018916385/ },
doi = { 10.5120/ijca2018916385 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:00:55.023710+05:30
%A John Martin R.
%A Sujatha S.
%A Swapna S. L.
%T Multiresolution Analysis in EEG Signal Feature Engineering for Epileptic Seizure Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 17
%P 14-20
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In biomedical engineering, many attempts are being reported over the years for automated diagnosis of various brain disorders by classifying EEG (Electroencephalography) signals. Various machine learning algorithms are adopted to address different scenarios in EEG classifications. Feature engineering is playing a vital role in order to enhance the classification efficiency particularly in signal processing applications. This paper elucidates multi-resolution analysis (MRA) of feature engineering and demonstrates how the distinctive features are being engineered in wavelet domain. The implementation results are placed in the form of feature distribution diagrams and provide clear indications in feature selection for epilepsy seizure detection through classification.

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Index Terms

Computer Science
Information Sciences

Keywords

Wavelets Feature Engineering Electroencephalography Discrete Wavelet Transform Multiresolution Analysis.

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