Detection and prediction of epileptic seizure using different machine learning classifiers
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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| Dil: | English |
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Brac University
2023
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| Online Erişim: | http://hdl.handle.net/10361/21790 |
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10361-217902023-10-12T21:03:36Z Detection and prediction of epileptic seizure using different machine learning classifiers Crystal, Afnan Ahmed Tasnim, Nuna Jamal, Subha Sumaiya B Rahman, Md. Ashikur Chakrabarty, Amitabha Karim, Dewan Ziaul Department of Computer Science and Engineering, Brac University EEG Epilepsy Seizure Feature extraction Classification PCA Traumatic epilepsy Electroencephalography 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 72-78). Analyzing neural signals produced by neurons in the brain, epilepsy can be diagnosed. An electroencephalogram (EEG) measures brain electrical activity, and studying EEG data in order to detect epileptic seizures in their early phases is an important aspect of epilepsy research. Despite optimal medication management, around one-third of epileptic patients continue to experience seizures. As a result, detecting epileptic seizures has become increasingly important in the field of research in recent years. It has been observed that machine learning has a revolutionary effect on classifying EEG data, seizure detection, and identifying sensible patterns without performance deterioration. This study provides a comprehensive summary of works on automated epileptic seizure recognition utilizing a variety of machine learning techniques, including SVC, Logistic Regression, Decision Tree Classifier, Random Forest Classifier, Gradient Boosting, and Multilayer Perceptron (MLP). A dataset provided by the UCI Machine Learning Repository is used to train the model. We considered the F1 score to be our most important performance metric since it handles unbalanced data sets effectively by comparing both precision and recall. Our research found that the Random Forest Classifier achieved a higher F1 score of 97.8261% with a precision of 96.746% and an F1 score of 96.4402% com-pared to other classifiers when five groups of people were considered. Later, we implemented PCA and clustering to determine if we could improve the Random Forest Classifier’s performance. After performing PCA for dimension reduction and K-means for clustering, we compare the F1 scores and cannot find any significant difference, which implies that our data set does not need any further clustering. Hence, the findings suggest that classification performance remains the same after implementing dimension reduction and clustering. Afnan Ahmed Crystal Nuna Tasnim Subha Sumaiya B Jamal Md. Ashikur Rahman B.Sc. in Computer Science and Engineering 2023-10-12T08:51:41Z 2023-10-12T08:51:41Z ©2022 2022-05-29 Thesis ID 21141046 ID 18101512 ID 18101636 ID 18101074 http://hdl.handle.net/10361/21790 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. 90 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
EEG Epilepsy Seizure Feature extraction Classification PCA Traumatic epilepsy Electroencephalography |
| spellingShingle |
EEG Epilepsy Seizure Feature extraction Classification PCA Traumatic epilepsy Electroencephalography Crystal, Afnan Ahmed Tasnim, Nuna Jamal, Subha Sumaiya B Rahman, Md. Ashikur Detection and prediction of epileptic seizure using different machine learning classifiers |
| 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 |
Chakrabarty, Amitabha |
| author_facet |
Chakrabarty, Amitabha Crystal, Afnan Ahmed Tasnim, Nuna Jamal, Subha Sumaiya B Rahman, Md. Ashikur |
| format |
Thesis |
| author |
Crystal, Afnan Ahmed Tasnim, Nuna Jamal, Subha Sumaiya B Rahman, Md. Ashikur |
| author_sort |
Crystal, Afnan Ahmed |
| title |
Detection and prediction of epileptic seizure using different machine learning classifiers |
| title_short |
Detection and prediction of epileptic seizure using different machine learning classifiers |
| title_full |
Detection and prediction of epileptic seizure using different machine learning classifiers |
| title_fullStr |
Detection and prediction of epileptic seizure using different machine learning classifiers |
| title_full_unstemmed |
Detection and prediction of epileptic seizure using different machine learning classifiers |
| title_sort |
detection and prediction of epileptic seizure using different machine learning classifiers |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/21790 |
| work_keys_str_mv |
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1814307669017624576 |