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Epileptic Patient Activity Recognition System Using Extreme Learning Machine Method

The Human Activity Recognition (HAR) system is the hottest research area in clinical research. The HAR plays a vital role in learning about a patient’s abnormal activities; based upon this information, the patient’s psychological state can be estimated. An epileptic seizure is a neurological disorde...

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Autores principales: Ayman, Ummara, Zia, Muhammad Sultan, Okon, Ofonime Dominic, Rehman, Najam-ur, Meraj, Talha, Ragab, Adham E., Rauf, Hafiz Tayyab
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10045857/
https://www.ncbi.nlm.nih.gov/pubmed/36979795
http://dx.doi.org/10.3390/biomedicines11030816
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author Ayman, Ummara
Zia, Muhammad Sultan
Okon, Ofonime Dominic
Rehman, Najam-ur
Meraj, Talha
Ragab, Adham E.
Rauf, Hafiz Tayyab
author_facet Ayman, Ummara
Zia, Muhammad Sultan
Okon, Ofonime Dominic
Rehman, Najam-ur
Meraj, Talha
Ragab, Adham E.
Rauf, Hafiz Tayyab
author_sort Ayman, Ummara
collection PubMed
description The Human Activity Recognition (HAR) system is the hottest research area in clinical research. The HAR plays a vital role in learning about a patient’s abnormal activities; based upon this information, the patient’s psychological state can be estimated. An epileptic seizure is a neurological disorder of the human brain and affects millions of people worldwide. If epilepsy is diagnosed correctly and in an early stage, then up to 70% of people can be seizure-free. There is a need for intelligent automatic HAR systems that help clinicians diagnose neurological disorders accurately. In this research, we proposed a Deep Learning (DL) model that enables the detection of epileptic seizures in an automated way, addressing a need in clinical research. To recognize epileptic seizures from brain activities, EEG is a raw but good source of information. In previous studies, many techniques used raw data from EEG to help recognize epileptic patient activities; however, the applied method of extracting features required much intensive expertise from clinical aspects such as radiology and clinical methods. The image data are also used to diagnose epileptic seizures, but applying Machine Learning (ML) methods could address the overfitting problem. In this research, we mainly focused on classifying epilepsy through physical epileptic activities instead of feature engineering and performed the detection of epileptic seizures in three steps. In the first step, we used the open-source numerical dataset of epilepsy of Bonn university from the UCI Machine Learning repository. In the second step, data were fed to the proposed ELM model for training in different training and testing ratios with a little bit of rescaling because the dataset was already pre-processed, normalized, and restructured. In the third step, epileptic and non-epileptic activity was recognized, and in this step, EEG signal feature extraction was automatically performed by a DL model named ELM; features were selected by a Feature Selection (FS) algorithm based on ELM and the final classification was performed using the ELM classifier. In our presented research, seven different ML algorithms were applied for the binary classification of epileptic activities, including K-Nearest Neighbor (KNN), Naïve Bayes (NB), Logistic Regression (LR), Stochastic Gradient Boosting Classifier (SGDC), Gradient Boosting Classifier (GB), Decision Trees (DT), and three deep learning models named Extreme Learning Machine (ELM), Long Short-Term Memory (LSTM), and Artificial Neural Network (ANN). After deep analysis, it is observed that the best results were obtained by our proposed DL model, Extreme Learning Machine (ELM), with an accuracy of 100% accuracy and a 0.99 AUC. Such high performance has not attained in previous research. The proposed model’s performance was checked with other models in terms of performance parameters, namely confusion matrix, accuracy, precision, recall, F1-score, specificity, sensitivity, and the ROC curve.
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spelling pubmed-100458572023-03-29 Epileptic Patient Activity Recognition System Using Extreme Learning Machine Method Ayman, Ummara Zia, Muhammad Sultan Okon, Ofonime Dominic Rehman, Najam-ur Meraj, Talha Ragab, Adham E. Rauf, Hafiz Tayyab Biomedicines Article The Human Activity Recognition (HAR) system is the hottest research area in clinical research. The HAR plays a vital role in learning about a patient’s abnormal activities; based upon this information, the patient’s psychological state can be estimated. An epileptic seizure is a neurological disorder of the human brain and affects millions of people worldwide. If epilepsy is diagnosed correctly and in an early stage, then up to 70% of people can be seizure-free. There is a need for intelligent automatic HAR systems that help clinicians diagnose neurological disorders accurately. In this research, we proposed a Deep Learning (DL) model that enables the detection of epileptic seizures in an automated way, addressing a need in clinical research. To recognize epileptic seizures from brain activities, EEG is a raw but good source of information. In previous studies, many techniques used raw data from EEG to help recognize epileptic patient activities; however, the applied method of extracting features required much intensive expertise from clinical aspects such as radiology and clinical methods. The image data are also used to diagnose epileptic seizures, but applying Machine Learning (ML) methods could address the overfitting problem. In this research, we mainly focused on classifying epilepsy through physical epileptic activities instead of feature engineering and performed the detection of epileptic seizures in three steps. In the first step, we used the open-source numerical dataset of epilepsy of Bonn university from the UCI Machine Learning repository. In the second step, data were fed to the proposed ELM model for training in different training and testing ratios with a little bit of rescaling because the dataset was already pre-processed, normalized, and restructured. In the third step, epileptic and non-epileptic activity was recognized, and in this step, EEG signal feature extraction was automatically performed by a DL model named ELM; features were selected by a Feature Selection (FS) algorithm based on ELM and the final classification was performed using the ELM classifier. In our presented research, seven different ML algorithms were applied for the binary classification of epileptic activities, including K-Nearest Neighbor (KNN), Naïve Bayes (NB), Logistic Regression (LR), Stochastic Gradient Boosting Classifier (SGDC), Gradient Boosting Classifier (GB), Decision Trees (DT), and three deep learning models named Extreme Learning Machine (ELM), Long Short-Term Memory (LSTM), and Artificial Neural Network (ANN). After deep analysis, it is observed that the best results were obtained by our proposed DL model, Extreme Learning Machine (ELM), with an accuracy of 100% accuracy and a 0.99 AUC. Such high performance has not attained in previous research. The proposed model’s performance was checked with other models in terms of performance parameters, namely confusion matrix, accuracy, precision, recall, F1-score, specificity, sensitivity, and the ROC curve. MDPI 2023-03-07 /pmc/articles/PMC10045857/ /pubmed/36979795 http://dx.doi.org/10.3390/biomedicines11030816 Text en © 2023 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
Ayman, Ummara
Zia, Muhammad Sultan
Okon, Ofonime Dominic
Rehman, Najam-ur
Meraj, Talha
Ragab, Adham E.
Rauf, Hafiz Tayyab
Epileptic Patient Activity Recognition System Using Extreme Learning Machine Method
title Epileptic Patient Activity Recognition System Using Extreme Learning Machine Method
title_full Epileptic Patient Activity Recognition System Using Extreme Learning Machine Method
title_fullStr Epileptic Patient Activity Recognition System Using Extreme Learning Machine Method
title_full_unstemmed Epileptic Patient Activity Recognition System Using Extreme Learning Machine Method
title_short Epileptic Patient Activity Recognition System Using Extreme Learning Machine Method
title_sort epileptic patient activity recognition system using extreme learning machine method
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10045857/
https://www.ncbi.nlm.nih.gov/pubmed/36979795
http://dx.doi.org/10.3390/biomedicines11030816
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