Detection Analysis of Epileptic EEG Using a Novel Random Forest Model Combined With Grid Search Optimization

Frontiers in Human Neuroscience 13:424082 (2019)
  Copy   BIBTEX

Abstract

In the automatic detection of epileptic seizures, the monitoring of critically ill patients with time varying EEG signals is an essential procedure in intensive care units. There is an increasing interest in using EEG analysis to detect seizure, and in this study we aim to get a better understanding of how to visualize the information in the EEG time-frequency feature, and design and train a novel random forest algorithm for EEG decoding, especially for multiple-levels of illness. Here, we propose an automatic detection framework for epileptic seizure based on multiple time-frequency analysis approaches; it involves a novel random forest model combined with grid search optimization. The short-time Fourier transformation visualizes seizure features after normalization. The dimensionality of features is reduced through principal component analysis before feeding them into the classification model. The training parameters are optimized using grid search optimization to improve detection performance and diagnostic accuracy by in the recognition of three different levels epileptic of conditions (healthy subjects, seizure-free intervals, seizure activity). Our proposed model was used to classify 500 samples of raw EEG data, and multiple cross-validations were adopted to boost the modeling accuracy. Experimental results were evaluated by an accuracy, a confusion matrix, a receiver operating characteristic curve and an area under the curve. The evaluations indicated that our model achieved the more effective classification than some previous typical methods. Such a scheme for computer-assisted clinical diagnosis of seizures has a potential guiding significance, which not only relieves the suffering of patient with epilepsy to improve quality of life, but also helps neurologists reduce their workload.

Other Versions

No versions found

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 99,210

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Similar books and articles

Multiresolution Analysis of EEG Signals.Marta Borowska & Natalia Białobłocka - 2016 - Studies in Logic, Grammar and Rhetoric 47 (1):21-31.
SVM-Enhanced Intrusion Detection System for Effective Cyber Attack Identification and Mitigation.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):397-403.

Analytics

Added to PP
2019-02-22

Downloads
21 (#880,809)

6 months
7 (#587,762)

Historical graph of downloads
How can I increase my downloads?

Author Profiles