Cargando…

Epileptic Seizure Prediction Based on Hybrid Seek Optimization Tuned Ensemble Classifier Using EEG Signals

Visual analysis of an electroencephalogram (EEG) by medical professionals is highly time-consuming and the information is difficult to process. To overcome these limitations, several automated seizure detection strategies have been introduced by combining signal processing and machine learning. This...

Descripción completa

Detalles Bibliográficos
Autores principales: Kapoor, Bhaskar, Nagpal, Bharti, Jain, Praphula Kumar, Abraham, Ajith, Gabralla, Lubna Abdelkareim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824897/
https://www.ncbi.nlm.nih.gov/pubmed/36617019
http://dx.doi.org/10.3390/s23010423
_version_ 1784866522899939328
author Kapoor, Bhaskar
Nagpal, Bharti
Jain, Praphula Kumar
Abraham, Ajith
Gabralla, Lubna Abdelkareim
author_facet Kapoor, Bhaskar
Nagpal, Bharti
Jain, Praphula Kumar
Abraham, Ajith
Gabralla, Lubna Abdelkareim
author_sort Kapoor, Bhaskar
collection PubMed
description Visual analysis of an electroencephalogram (EEG) by medical professionals is highly time-consuming and the information is difficult to process. To overcome these limitations, several automated seizure detection strategies have been introduced by combining signal processing and machine learning. This paper proposes a hybrid optimization-controlled ensemble classifier comprising the AdaBoost classifier, random forest (RF) classifier, and the decision tree (DT) classifier for the automatic analysis of an EEG signal dataset to predict an epileptic seizure. The EEG signal is pre-processed initially to make it suitable for feature selection. The feature selection process receives the alpha, beta, delta, theta, and gamma wave data from the EEG, where the significant features, such as statistical features, wavelet features, and entropy-based features, are extracted by the proposed hybrid seek optimization algorithm. These extracted features are fed forward to the proposed ensemble classifier that produces the predicted output. By the combination of corvid and gregarious search agent characteristics, the proposed hybrid seek optimization technique has been developed, and is used to evaluate the fusion parameters of the ensemble classifier. The suggested technique’s accuracy, sensitivity, and specificity are determined to be 96.6120%, 94.6736%, and 91.3684%, respectively, for the CHB-MIT database. This demonstrates the effectiveness of the suggested technique for early seizure prediction. The accuracy, sensitivity, and specificity of the proposed technique are 95.3090%, 93.1766%, and 90.0654%, respectively, for the Siena Scalp database, again demonstrating its efficacy in the early seizure prediction process.
format Online
Article
Text
id pubmed-9824897
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-98248972023-01-08 Epileptic Seizure Prediction Based on Hybrid Seek Optimization Tuned Ensemble Classifier Using EEG Signals Kapoor, Bhaskar Nagpal, Bharti Jain, Praphula Kumar Abraham, Ajith Gabralla, Lubna Abdelkareim Sensors (Basel) Article Visual analysis of an electroencephalogram (EEG) by medical professionals is highly time-consuming and the information is difficult to process. To overcome these limitations, several automated seizure detection strategies have been introduced by combining signal processing and machine learning. This paper proposes a hybrid optimization-controlled ensemble classifier comprising the AdaBoost classifier, random forest (RF) classifier, and the decision tree (DT) classifier for the automatic analysis of an EEG signal dataset to predict an epileptic seizure. The EEG signal is pre-processed initially to make it suitable for feature selection. The feature selection process receives the alpha, beta, delta, theta, and gamma wave data from the EEG, where the significant features, such as statistical features, wavelet features, and entropy-based features, are extracted by the proposed hybrid seek optimization algorithm. These extracted features are fed forward to the proposed ensemble classifier that produces the predicted output. By the combination of corvid and gregarious search agent characteristics, the proposed hybrid seek optimization technique has been developed, and is used to evaluate the fusion parameters of the ensemble classifier. The suggested technique’s accuracy, sensitivity, and specificity are determined to be 96.6120%, 94.6736%, and 91.3684%, respectively, for the CHB-MIT database. This demonstrates the effectiveness of the suggested technique for early seizure prediction. The accuracy, sensitivity, and specificity of the proposed technique are 95.3090%, 93.1766%, and 90.0654%, respectively, for the Siena Scalp database, again demonstrating its efficacy in the early seizure prediction process. MDPI 2022-12-30 /pmc/articles/PMC9824897/ /pubmed/36617019 http://dx.doi.org/10.3390/s23010423 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kapoor, Bhaskar
Nagpal, Bharti
Jain, Praphula Kumar
Abraham, Ajith
Gabralla, Lubna Abdelkareim
Epileptic Seizure Prediction Based on Hybrid Seek Optimization Tuned Ensemble Classifier Using EEG Signals
title Epileptic Seizure Prediction Based on Hybrid Seek Optimization Tuned Ensemble Classifier Using EEG Signals
title_full Epileptic Seizure Prediction Based on Hybrid Seek Optimization Tuned Ensemble Classifier Using EEG Signals
title_fullStr Epileptic Seizure Prediction Based on Hybrid Seek Optimization Tuned Ensemble Classifier Using EEG Signals
title_full_unstemmed Epileptic Seizure Prediction Based on Hybrid Seek Optimization Tuned Ensemble Classifier Using EEG Signals
title_short Epileptic Seizure Prediction Based on Hybrid Seek Optimization Tuned Ensemble Classifier Using EEG Signals
title_sort epileptic seizure prediction based on hybrid seek optimization tuned ensemble classifier using eeg signals
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824897/
https://www.ncbi.nlm.nih.gov/pubmed/36617019
http://dx.doi.org/10.3390/s23010423
work_keys_str_mv AT kapoorbhaskar epilepticseizurepredictionbasedonhybridseekoptimizationtunedensembleclassifierusingeegsignals
AT nagpalbharti epilepticseizurepredictionbasedonhybridseekoptimizationtunedensembleclassifierusingeegsignals
AT jainpraphulakumar epilepticseizurepredictionbasedonhybridseekoptimizationtunedensembleclassifierusingeegsignals
AT abrahamajith epilepticseizurepredictionbasedonhybridseekoptimizationtunedensembleclassifierusingeegsignals
AT gabrallalubnaabdelkareim epilepticseizurepredictionbasedonhybridseekoptimizationtunedensembleclassifierusingeegsignals