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

25 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	Advanced data mining and applications
Subtitle of host publication	13th international conference, ADMA 2017, proceedings
Editors	Gao Cong, Wen-Chih Peng, Wei Emma Zhang, Chengliang Li, Aixin Sun
Place of Publication	Cham, Switzerland
Publisher	Springer
Pages	386-398
Number of pages	13
Volume	10604
ISBN (Electronic)	9783319691794
ISBN (Print)	9783319691787
DOIs	https://doi.org/10.1007/978-3-319-69179-4_27
Publication status	Published - 2017
Event	The 13th International Conference on Advanced Data Mining and Applications (ADMA): ADMA 2017 - Nanyang Technological University Alumni House, Singapore, Singapore Duration: 05 Nov 2017 → 06 Nov 2017 http://www.adma2017.net/#0 (Conference website)

Publication series

Name	Lecture Notes in Artificial Intelligence
Publisher	Springer
Volume	10604
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	The 13th International Conference on Advanced Data Mining and Applications (ADMA)
Country/Territory	Singapore
City	Singapore
Period	05/11/17 → 06/11/17
Other	The year 2017 marks the 13th anniversary of the International Conference on Advanced Data Mining and Applications (ADMA 2017), which will be held in Singapore, 5—6 Nov 2016, co-located with ACM CIKM2017. It is our great pleasure to invite you to contribute papers and participate in this premier annual event on research and applications of data mining. A growing attention has been paid to the study, development, and application of data mining. As a result, there is an urgent need for sophisticated techniques and tools that can handle new subfields of data mining, e.g., smartphone and social network data mining, spatial data mining, streaming data mining, green computing data mining, biomedical data mining, the Internet of Things, and data mining for healthcare. Our expertise in data mining also has to be expanded to new applications.
Internet address	http://www.adma2017.net/#0 (Conference website)

Access to Document

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

25 Citations
1 Doctoral Thesis

Brain Data Mining for Epileptic Seizure-Detection
Siddiqui, M. K., 2018, 217 p.
Research output: Thesis › Doctoral Thesis

Open Access
File

Cite this

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.), Advanced data mining and applications: 13th international conference, ADMA 2017, proceedings (Vol. 10604, 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. Advanced data mining and applications: 13th international conference, ADMA 2017, proceedings. editor / Gao Cong ; Wen-Chih Peng ; Wei Emma Zhang ; Chengliang Li ; Aixin Sun. Vol. 10604 Cham, Switzerland : Springer, 2017. pp. 386-398 (Lecture Notes in Artificial Intelligence).

@inproceedings{8aebcd888c334872b1d524ed3a6a2777,

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.",

keywords = "Classification techniques, Black-box classifier, Non-black box classifier, Data mining, Seizure detection",

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

note = "Includes bibliographical references.; The 13th International Conference on Advanced Data Mining and Applications (ADMA) : ADMA 2017 ; Conference date: 05-11-2017 Through 06-11-2017",

year = "2017",

doi = "10.1007/978-3-319-69179-4\_27",

language = "English",

isbn = "9783319691787",

volume = "10604",

series = "Lecture Notes in Artificial Intelligence",

publisher = "Springer",

pages = "386--398",

editor = "Gao Cong and Wen-Chih Peng and Zhang, \{Wei Emma\} and Chengliang Li and Aixin Sun",

booktitle = "Advanced data mining and applications",

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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), Advanced data mining and applications: 13th international conference, ADMA 2017, proceedings. vol. 10604, Lecture Notes in Artificial Intelligence, vol. 10604, Springer, Cham, Switzerland, pp. 386-398, The 13th International Conference on Advanced Data Mining and Applications (ADMA), 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.
Advanced data mining and applications: 13th international conference, ADMA 2017, proceedings. ed. / Gao Cong; Wen-Chih Peng; Wei Emma Zhang; Chengliang Li; Aixin Sun. Vol. 10604 Cham, Switzerland: Springer, 2017. p. 386-398 (Lecture Notes in Artificial Intelligence; Vol. 10604).

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

TY - GEN

T1 - Analyzing performance of classification techniques in detecting epileptic seizure

AU - Siddiqui, Mohammad Khubeb

AU - Islam, Md Zahidul

AU - Kabir, Muhammad Ashad

N1 - Includes bibliographical references.

PY - 2017

Y1 - 2017

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.

KW - Classification techniques

KW - Black-box classifier

KW - Non-black box classifier

KW - Data mining

KW - Seizure detection

U2 - 10.1007/978-3-319-69179-4_27

DO - 10.1007/978-3-319-69179-4_27

M3 - Conference paper

SN - 9783319691787

VL - 10604

T3 - Lecture Notes in Artificial Intelligence

SP - 386

EP - 398

BT - Advanced data mining and applications

A2 - Cong, Gao

A2 - Peng, Wen-Chih

A2 - Zhang, Wei Emma

A2 - Li, Chengliang

A2 - Sun, Aixin

PB - Springer

CY - Cham, Switzerland

T2 - The 13th International Conference on Advanced Data Mining and Applications (ADMA)

Y2 - 5 November 2017 through 6 November 2017

ER -

Siddiqui MK, Islam MZ , Kabir MA. Analyzing performance of classification techniques in detecting epileptic seizure. In Cong G, Peng WC, Zhang WE, Li C, Sun A, editors, Advanced data mining and applications: 13th international conference, ADMA 2017, proceedings. Vol. 10604. Cham, Switzerland: Springer. 2017. p. 386-398. (Lecture Notes in Artificial Intelligence). Epub 2017 Oct 14. doi: 10.1007/978-3-319-69179-4_27

Analyzing performance of classification techniques in detecting epileptic seizure

Abstract

Publication series

Conference

Access to Document

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Research output

Brain Data Mining for Epileptic Seizure-Detection

Cite this