DWT based transformed domain feature extraction approach for epileptic seizure detection
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
| Auteurs principaux: | , , , , |
|---|---|
| Autres auteurs: | |
| Format: | Thèse |
| Langue: | English |
| Publié: |
Brac University
2021
|
| Sujets: | |
| Accès en ligne: | http://hdl.handle.net/10361/15687 |
| id |
10361-15687 |
|---|---|
| record_format |
dspace |
| spelling |
10361-156872022-01-26T10:20:00Z DWT based transformed domain feature extraction approach for epileptic seizure detection Mostafa, Mahajabin Samin, Mohtasim Abrar Hassan, Nabila Bintey Nibras, Saiara Zerin Rahman, Samir Parvez, Mohammad Zavid Abrar, Mohammed Abid Department of Computer Science and Engineering, Brac University DWT Transformed domain EEG Feature extraction Classification Electroencephalography. Wavelets (Mathematics) Signal processing--Digital techniques--Mathematics This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 30-35). Epileptic seizure is a neurological disorder that is prevalent in both males and females of all age ranges. Detection of epileptic seizure serves as an important role for epileptic patients as it allows the initiation of systems to prevent injuries and limiting the possibilities of risk by providing targeted therapy by anticipating their onset prior to presentation. Electroencephalogram (EEG) plays an important role in seizure detection and is one of the most well-known techniques for determining stages of epilepsy. Since, EEG is a non-stationary signal it can be quite difficult to di↵erentiate amongst seizure activity and normal neural activity. In this paper we have proposed an epilepsy detection method based on five di↵erent feature extraction methods and followed by that the original domain of the extracted features were transformed using DiscreteWavelet Transform (DWT) and three di↵erent classifiers- Decision Tree, Random Forest and KNN to classify into seizure and nonseizure stages. Results demonstrated in this paper have outperformed the existing state-of-the-art methods with 97.22%, 100% and 83.33% for 2 class classification and 91.67%, 91.67% and 80.56% for 4 class classification for the aforementioned classification techniques accordingly. Mahajabin Mostafa Mohtasim Abrar Samin Nabila Bintey Hassan Saiara Zerin Nibras Samir Rahman B. Computer Science 2021-12-02T05:04:33Z 2021-12-02T05:04:33Z 2021 2021-09 Thesis ID 18101458 ID 18101094 ID 18101237 ID 18101251 ID 18101130 http://hdl.handle.net/10361/15687 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. 35 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
DWT Transformed domain EEG Feature extraction Classification Electroencephalography. Wavelets (Mathematics) Signal processing--Digital techniques--Mathematics |
| spellingShingle |
DWT Transformed domain EEG Feature extraction Classification Electroencephalography. Wavelets (Mathematics) Signal processing--Digital techniques--Mathematics Mostafa, Mahajabin Samin, Mohtasim Abrar Hassan, Nabila Bintey Nibras, Saiara Zerin Rahman, Samir DWT based transformed domain feature extraction approach for epileptic seizure detection |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Parvez, Mohammad Zavid |
| author_facet |
Parvez, Mohammad Zavid Mostafa, Mahajabin Samin, Mohtasim Abrar Hassan, Nabila Bintey Nibras, Saiara Zerin Rahman, Samir |
| format |
Thesis |
| author |
Mostafa, Mahajabin Samin, Mohtasim Abrar Hassan, Nabila Bintey Nibras, Saiara Zerin Rahman, Samir |
| author_sort |
Mostafa, Mahajabin |
| title |
DWT based transformed domain feature extraction approach for epileptic seizure detection |
| title_short |
DWT based transformed domain feature extraction approach for epileptic seizure detection |
| title_full |
DWT based transformed domain feature extraction approach for epileptic seizure detection |
| title_fullStr |
DWT based transformed domain feature extraction approach for epileptic seizure detection |
| title_full_unstemmed |
DWT based transformed domain feature extraction approach for epileptic seizure detection |
| title_sort |
dwt based transformed domain feature extraction approach for epileptic seizure detection |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/15687 |
| work_keys_str_mv |
AT mostafamahajabin dwtbasedtransformeddomainfeatureextractionapproachforepilepticseizuredetection AT saminmohtasimabrar dwtbasedtransformeddomainfeatureextractionapproachforepilepticseizuredetection AT hassannabilabintey dwtbasedtransformeddomainfeatureextractionapproachforepilepticseizuredetection AT nibrassaiarazerin dwtbasedtransformeddomainfeatureextractionapproachforepilepticseizuredetection AT rahmansamir dwtbasedtransformeddomainfeatureextractionapproachforepilepticseizuredetection |
| _version_ |
1814309124419092480 |