Analyzing Performance of Classification Techniques in Detecting Epileptic Seizure

Mohammad Khubeb Siddiqui; Md Zahidul Islam; Muhammad Ashad Kabir

doi:10.1007/978-3-319-69179-4_27

Analyzing Performance of Classification Techniques in Detecting Epileptic Seizure

Mohammad Khubeb Siddiqui, Md Zahidul Islam, Muhammad Ashad Kabir

Research output: Book chapter/Published conference paper › Conference paper › peer-review

5 Citations (Scopus)

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
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	https://doi.org/10.1007/978-3-319-69179-4_27
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
Publisher	Springer
Volume	10604
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference on Advanced Data Mining and Applications
Country	Singapore
City	Singapore
Period	05/11/17 → 06/11/17
Internet address	https://web.archive.org/web/20170826073420/http://www.adma2017.net/#0 (Conference website) https://www.springer.com/gp/book/9783319691787 (Conference proceedings)

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Siddiqui, M. K., Islam, M. Z., & Kabir, M. A. (2017). Analyzing Performance of Classification Techniques in Detecting Epileptic Seizure. In G. Cong, W-C. Peng, W. E. Zhang, C. Li, & A. Sun (Eds.), Proceedings of the 13th International Conference of Advanced Data Mining and Applications (ADMA 2017) (pp. 386-398). (Lecture Notes in Artificial Intelligence; Vol. 10604). Springer. https://doi.org/10.1007/978-3-319-69179-4_27

Siddiqui, Mohammad Khubeb ; Islam, Md Zahidul ; Kabir, Muhammad Ashad. / Analyzing Performance of Classification Techniques in Detecting Epileptic Seizure. Proceedings of the 13th International Conference of Advanced Data Mining and Applications (ADMA 2017). editor / Gao Cong ; Wen-Chih Peng ; Wei Emma Zhang ; Chengliang Li ; Aixin Sun. Springer, 2017. pp. 386-398 (Lecture Notes in Artificial Intelligence).

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title = "Analyzing Performance of Classification Techniques in Detecting Epileptic Seizure",

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.",

author = "Siddiqui, {Mohammad Khubeb} and Islam, {Md Zahidul} and Kabir, {Muhammad Ashad}",

year = "2017",

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language = "English",

isbn = "9783319691787",

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pages = "386--398",

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booktitle = "Proceedings of the 13th International Conference of Advanced Data Mining and Applications (ADMA 2017)",

address = "United States",

note = "13th International Conference on Advanced Data Mining and Applications : ADMA 2017 ; Conference date: 05-11-2017 Through 06-11-2017",

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Siddiqui, MK, Islam, MZ & Kabir, MA 2017, Analyzing Performance of Classification Techniques in Detecting Epileptic Seizure. in G Cong, W-C Peng, WE Zhang, C Li & A Sun (eds), Proceedings of the 13th International Conference of Advanced Data Mining and Applications (ADMA 2017). Lecture Notes in Artificial Intelligence, vol. 10604, Springer, pp. 386-398, 13th International Conference on Advanced Data Mining and Applications, Singapore, Singapore, 05/11/17. https://doi.org/10.1007/978-3-319-69179-4_27

Analyzing Performance of Classification Techniques in Detecting Epileptic Seizure. / Siddiqui, Mohammad Khubeb; Islam, Md Zahidul ; Kabir, Muhammad Ashad.

Proceedings of the 13th International Conference of Advanced Data Mining and Applications (ADMA 2017). ed. / Gao Cong; Wen-Chih Peng; Wei Emma Zhang; Chengliang Li; Aixin Sun. Springer, 2017. p. 386-398 (Lecture Notes in Artificial Intelligence; Vol. 10604).

Research output: Book chapter/Published conference paper › Conference paper › peer-review

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N2 - 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.

AB - 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.

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Siddiqui MK, Islam MZ , Kabir MA. Analyzing Performance of Classification Techniques in Detecting Epileptic Seizure. In Cong G, Peng W-C, Zhang WE, Li C, Sun A, editors, Proceedings of the 13th International Conference of Advanced Data Mining and Applications (ADMA 2017). Springer. 2017. p. 386-398. (Lecture Notes in Artificial Intelligence). https://doi.org/10.1007/978-3-319-69179-4_27

Analyzing Performance of Classification Techniques in Detecting Epileptic Seizure

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