Analyzing Performance of Classification Techniques in Detecting Epileptic Seizure

Mohammad Khubeb Siddiqui, Md Zahidul Islam, Muhammad Ashad Kabir

Research output: Book chapter/Published conference paperConference paperpeer-review

6 Citations (Scopus)


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 languageEnglish
Title of host publicationProceedings of the 13th International Conference of Advanced Data Mining and Applications (ADMA 2017)
EditorsGao Cong, Wen-Chih Peng, Wei Emma Zhang, Chengliang Li, Aixin Sun
Number of pages13
ISBN (Electronic) 9783319691794
ISBN (Print)9783319691787
Publication statusPublished - 2017
Event13th International Conference on Advanced Data Mining and Applications: ADMA 2017 - Nanyang Technological University Alumni House, Singapore, Singapore
Duration: 05 Nov 201706 Nov 2017 (Conference website) (Conference proceedings)

Publication series

NameLecture Notes in Artificial Intelligence
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th International Conference on Advanced Data Mining and Applications
Internet address


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