Detection of Epileptic Seizures from Logistic Model Trees

DOI

10.2991/978-94-6463-314-6_36How to use a DOI?

Keywords

Computer vision; LMT classification; feature extraction

Abstract

Electroencephalogram (EEG) signals from patients can be analyzed to identify the neurological condition known as epileptic seizures. Because to the large dimensionality of the data and the existence of noise and artifacts, detecting epileptic seizures from EEG signals is a complicated process. In this article, we present a novel method for epileptic seizure detection using principal component analysis (PCA) as the feature extraction method and logistic model trees (LMT) as the classification method. By converting the original features into a lower-dimensional space, PCA is a frequently used feature extraction approach that lowers the dimensionality of the data. LMT is a decision tree-based machine learning method with the advantage of being able to incorporate linear models into its decision tree structure. It can handle non-linear connections between variables. We used a publicly accessible dataset of EEG signals captured from epilepsy patients for our study. The most crucial elements from the EEG signals were then extracted using PCA. Then, using the extracted features to train an LMT classifier, we assessed the classifier’s performance using a variety of measures, including accuracy, precision, recall and f1_score. The proposed method has an accuracy of 95.33%, a precision of 93%, f1_score 92.5% and recall of 92% according to our experimental findings. These findings show how successfully the suggested method can identify epileptic seizures from EEG signals. The suggested method may be helpful for creating a real-time seizure detection system that will help in epilepsy diagnosis and care.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - V. Nageshwar
AU  - P. Venkateswara Rao
AU  - C. Sarika
AU  - K. Manusha
AU  - Y. Deepthi
PY  - 2023
DA  - 2023/12/21
TI  - Detection of Epileptic Seizures from Logistic Model Trees
BT  - Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)
PB  - Atlantis Press
SP  - 371
EP  - 382
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-314-6_36
DO  - 10.2991/978-94-6463-314-6_36
ID  - Nageshwar2023
ER  -