Abstract
Epileptic seizure detection is a challenging research topic. The objective of this research is to analyze the performance of various classification techniques while detecting the epileptic seizure in a shorter time. In this paper, we apply four different types of classifiers-two are black-box (SVM & KNN) and other two are non-black-box (Decision tree & Ensemble) on two epileptic patient seizure data sets. Our finding shows that non-black box classifiers, specifically ensemble classifiers, do better than other classifiers. The experimental results indicate that the ensemble classifier can assist for seizure detection in a shorter epoch length of time (i.e., 0.5 s) with high accuracy rate. Significantly in comparison to other classifiers the ensemble classifier provides high accuracy and less chance of false detection rate.
Original language | English |
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Title of host publication | Proceedings of the 13th International Conference of Advanced Data Mining and Applications (ADMA 2017) |
Editors | Gao Cong, Wen-Chih Peng, Wei Emma Zhang, Chengliang Li, Aixin Sun |
Publisher | Springer |
Pages | 386-398 |
Number of pages | 13 |
ISBN (Electronic) | 9783319691794 |
ISBN (Print) | 9783319691787 |
DOIs | |
Publication status | Published - 2017 |
Event | 13th International Conference on Advanced Data Mining and Applications: ADMA 2017 - Nanyang Technological University Alumni House, Singapore, Singapore Duration: 05 Nov 2017 → 06 Nov 2017 https://web.archive.org/web/20170826073420/http://www.adma2017.net/#0 (Conference website) https://www.springer.com/gp/book/9783319691787 (Conference proceedings) |
Publication series
Name | Lecture Notes in Artificial Intelligence |
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Publisher | Springer |
Volume | 10604 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 13th International Conference on Advanced Data Mining and Applications |
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Country | Singapore |
City | Singapore |
Period | 05/11/17 → 06/11/17 |
Internet address |
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