An approach to detect epileptic seizure using XAI and machine learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
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Brac University
2022
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| 在線閱讀: | http://hdl.handle.net/10361/17538 |
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10361-175382023-10-15T10:56:54Z An approach to detect epileptic seizure using XAI and machine learning Bijoy, Emam Hasan Rahman, Md. Hasibur Ahmed, Sabbir Laskor, Md. Shifat Hossain, Muhammad Iqbal Rahman, Rafeed Department of Computer Science and Engineering, Brac University Multi-class classification Binary classification K-Nearest Neighbor (KNN) Decision tree algorithm Random forest algorithm Multi-Layer Perceptron (MLP) Gradient boosting classifier Gaussian Naïve Bayes Complement Naïve Bayes SGD Classifier XAI LIME Algorithm Sudden Unexpected Death in Epilepsy (SUDEP) Epileptic Seizure (ES) Electroencephalogram (EEG) Machine Learning Computer algorithms This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 31-32). One of the most common neurological disorder in health sector is Epileptic Seizure (ES) which is occurred by sudden repeated seizures. Hitherto more than 50 million people in the whole world are suffering from Epileptic Seizures. The abnormal brain activity of the central nervous system often causes unusual behavior, losing awareness and psychological problems etc. Moreover, many risks associated with epileptic seizures include sudden unexpected death in epilepsy (SUDEP) which is really a concerning problem discussed in this article. For abstaining from adverse consequences of epileptic seizure-like this health sector focuses more on the early prediction and detection of epilepsy. The complex signals of brain activity are reflected as swift-passing exalted peaks in Electroencephalogram (EEG). Initially, the specialist inspects the EEG signals over a few weeks or months to identify the presence of epileptic seizures, which is a very time-consuming and challenging task. Hence, Machine learning (ML) based classifiers are capable to categorize EEG signals and detect seizures along with displaying related perceptible patterns by maintaining accuracy and efficiency. In order to detect epileptic seizures, EEGbased signal recognition algorithms had been shown in this paper by applying both Multi-Class Classification and Binary classification. The algorithms were Decision Tree Algorithm, Random Forest Algorithm, Multi-Layer Perceptron (MLP) and K-Nearest Neighbor (KNN), Gradient Boosting Classifier, Gaussian Na¨ıve Bayes, Complement Na¨ıve Bayes, SGD Classifier, Explainable Artificial Intelligence (XAI), LIME Algorithm etc. However, K-Nearest Neighbor appears with pretty higher accuracy in certain conditions. Emam Hasan Bijoy Md. Hasibur Rahman Sabbir Ahmed Md. Shifat Laskor B. Computer Science and Engineering 2022-10-26T05:54:34Z 2022-10-26T05:54:34Z 2022 2022-05 Thesis ID 18101516 ID 18101040 ID 21341057 ID 18101561 http://hdl.handle.net/10361/17538 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 32 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Multi-class classification Binary classification K-Nearest Neighbor (KNN) Decision tree algorithm Random forest algorithm Multi-Layer Perceptron (MLP) Gradient boosting classifier Gaussian Naïve Bayes Complement Naïve Bayes SGD Classifier XAI LIME Algorithm Sudden Unexpected Death in Epilepsy (SUDEP) Epileptic Seizure (ES) Electroencephalogram (EEG) Machine Learning Computer algorithms |
| spellingShingle |
Multi-class classification Binary classification K-Nearest Neighbor (KNN) Decision tree algorithm Random forest algorithm Multi-Layer Perceptron (MLP) Gradient boosting classifier Gaussian Naïve Bayes Complement Naïve Bayes SGD Classifier XAI LIME Algorithm Sudden Unexpected Death in Epilepsy (SUDEP) Epileptic Seizure (ES) Electroencephalogram (EEG) Machine Learning Computer algorithms Bijoy, Emam Hasan Rahman, Md. Hasibur Ahmed, Sabbir Laskor, Md. Shifat An approach to detect epileptic seizure using XAI and machine learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. |
| author2 |
Hossain, Muhammad Iqbal |
| author_facet |
Hossain, Muhammad Iqbal Bijoy, Emam Hasan Rahman, Md. Hasibur Ahmed, Sabbir Laskor, Md. Shifat |
| format |
Thesis |
| author |
Bijoy, Emam Hasan Rahman, Md. Hasibur Ahmed, Sabbir Laskor, Md. Shifat |
| author_sort |
Bijoy, Emam Hasan |
| title |
An approach to detect epileptic seizure using XAI and machine learning |
| title_short |
An approach to detect epileptic seizure using XAI and machine learning |
| title_full |
An approach to detect epileptic seizure using XAI and machine learning |
| title_fullStr |
An approach to detect epileptic seizure using XAI and machine learning |
| title_full_unstemmed |
An approach to detect epileptic seizure using XAI and machine learning |
| title_sort |
approach to detect epileptic seizure using xai and machine learning |
| publisher |
Brac University |
| publishDate |
2022 |
| url |
http://hdl.handle.net/10361/17538 |
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