Classify epilepsy and normal Electroencephalogram (EEG) signal using wavelet transform and K-nearest neighbor

Dewi Rahmawati; N. R.Umy Chasanah; Riyanarto Sarno

doi:10.1109/ICSITech.2017.8257094

Classify epilepsy and normal Electroencephalogram (EEG) signal using wavelet transform and K-nearest neighbor

Dewi Rahmawati, N. R.Umy Chasanah, Riyanarto Sarno

Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Citations (Scopus)

Abstract

Epilepsy is a neurological disorder that cannot be predicted and studied. This study propose to classify epilepsy and normal Electroencephalogram (EEG) signal. Stages in the decision-making was done by using a feature extraction and combined with Wavelet Transform (WT). The result from features extraction was implemented dimension reduction method by using Principal Component Analysis (PCA) algorithm. K-Nearest Neighbor (KNN) was implemented using result from dimension reduction stages as features. In this work, 1000 data has been used as training data and 600 data has been used as a data testing. In this experiment, the dataset consist of two sets (A and E) from non-epileptic people and epileptic people. This experimental results also show that the sensitivity, accuracy and specificity of the results are 100%, 99.83% and 99.67%.

Original language	English
Title of host publication	Proceeding - 2017 3rd International Conference on Science in Information Technology
Subtitle of host publication	Theory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017
Editors	Lala Septem Riza, Andri Pranolo, Aji Prasetyo Wibawa, Enjun Junaeti, Yaya Wihardi, Ummi Raba'ah Hashim, Shi-Jinn Horng, Rafal Drezewski, Heui Seok Lim, Goutam Chakraborty, Leonel Hernandez, Shah Nazir
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	110-114
Number of pages	5
ISBN (Electronic)	9781509058662
DOIs	https://doi.org/10.1109/ICSITech.2017.8257094
Publication status	Published - 1 Jul 2017
Event	3rd International Conference on Science in Information Technology, ICSITech 2017 - Bandung, Indonesia Duration: 25 Oct 2017 → 26 Oct 2017

Publication series

Name	Proceeding - 2017 3rd International Conference on Science in Information Technology: Theory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017
Volume	2018-January

Conference

Conference	3rd International Conference on Science in Information Technology, ICSITech 2017
Country/Territory	Indonesia
City	Bandung
Period	25/10/17 → 26/10/17

Keywords

Electroencephalogram
KNN
PCA
Wavelet transform

Access to Document

10.1109/ICSITech.2017.8257094

Cite this

Rahmawati, D., Chasanah, N. R. U., & Sarno, R. (2017). Classify epilepsy and normal Electroencephalogram (EEG) signal using wavelet transform and K-nearest neighbor. In L. S. Riza, A. Pranolo, A. P. Wibawa, E. Junaeti, Y. Wihardi, U. R. Hashim, S.-J. Horng, R. Drezewski, H. S. Lim, G. Chakraborty, L. Hernandez, & S. Nazir (Eds.), Proceeding - 2017 3rd International Conference on Science in Information Technology: Theory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017 (pp. 110-114). (Proceeding - 2017 3rd International Conference on Science in Information Technology: Theory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSITech.2017.8257094

Rahmawati, Dewi ; Chasanah, N. R.Umy ; Sarno, Riyanarto. / Classify epilepsy and normal Electroencephalogram (EEG) signal using wavelet transform and K-nearest neighbor. Proceeding - 2017 3rd International Conference on Science in Information Technology: Theory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017. editor / Lala Septem Riza ; Andri Pranolo ; Aji Prasetyo Wibawa ; Enjun Junaeti ; Yaya Wihardi ; Ummi Raba'ah Hashim ; Shi-Jinn Horng ; Rafal Drezewski ; Heui Seok Lim ; Goutam Chakraborty ; Leonel Hernandez ; Shah Nazir. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 110-114 (Proceeding - 2017 3rd International Conference on Science in Information Technology: Theory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017).

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title = "Classify epilepsy and normal Electroencephalogram (EEG) signal using wavelet transform and K-nearest neighbor",

abstract = "Epilepsy is a neurological disorder that cannot be predicted and studied. This study propose to classify epilepsy and normal Electroencephalogram (EEG) signal. Stages in the decision-making was done by using a feature extraction and combined with Wavelet Transform (WT). The result from features extraction was implemented dimension reduction method by using Principal Component Analysis (PCA) algorithm. K-Nearest Neighbor (KNN) was implemented using result from dimension reduction stages as features. In this work, 1000 data has been used as training data and 600 data has been used as a data testing. In this experiment, the dataset consist of two sets (A and E) from non-epileptic people and epileptic people. This experimental results also show that the sensitivity, accuracy and specificity of the results are 100%, 99.83% and 99.67%.",

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editor = "Riza, {Lala Septem} and Andri Pranolo and Wibawa, {Aji Prasetyo} and Enjun Junaeti and Yaya Wihardi and Hashim, {Ummi Raba'ah} and Shi-Jinn Horng and Rafal Drezewski and Lim, {Heui Seok} and Goutam Chakraborty and Leonel Hernandez and Shah Nazir",

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Rahmawati, D, Chasanah, NRU & Sarno, R 2017, Classify epilepsy and normal Electroencephalogram (EEG) signal using wavelet transform and K-nearest neighbor. in LS Riza, A Pranolo, AP Wibawa, E Junaeti, Y Wihardi, UR Hashim, S-J Horng, R Drezewski, HS Lim, G Chakraborty, L Hernandez & S Nazir (eds), Proceeding - 2017 3rd International Conference on Science in Information Technology: Theory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017. Proceeding - 2017 3rd International Conference on Science in Information Technology: Theory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 110-114, 3rd International Conference on Science in Information Technology, ICSITech 2017, Bandung, Indonesia, 25/10/17. https://doi.org/10.1109/ICSITech.2017.8257094

Classify epilepsy and normal Electroencephalogram (EEG) signal using wavelet transform and K-nearest neighbor. / Rahmawati, Dewi; Chasanah, N. R.Umy; Sarno, Riyanarto.
Proceeding - 2017 3rd International Conference on Science in Information Technology: Theory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017. ed. / Lala Septem Riza; Andri Pranolo; Aji Prasetyo Wibawa; Enjun Junaeti; Yaya Wihardi; Ummi Raba'ah Hashim; Shi-Jinn Horng; Rafal Drezewski; Heui Seok Lim; Goutam Chakraborty; Leonel Hernandez; Shah Nazir. Institute of Electrical and Electronics Engineers Inc., 2017. p. 110-114 (Proceeding - 2017 3rd International Conference on Science in Information Technology: Theory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Classify epilepsy and normal Electroencephalogram (EEG) signal using wavelet transform and K-nearest neighbor

AU - Rahmawati, Dewi

AU - Chasanah, N. R.Umy

AU - Sarno, Riyanarto

PY - 2017/7/1

Y1 - 2017/7/1

N2 - Epilepsy is a neurological disorder that cannot be predicted and studied. This study propose to classify epilepsy and normal Electroencephalogram (EEG) signal. Stages in the decision-making was done by using a feature extraction and combined with Wavelet Transform (WT). The result from features extraction was implemented dimension reduction method by using Principal Component Analysis (PCA) algorithm. K-Nearest Neighbor (KNN) was implemented using result from dimension reduction stages as features. In this work, 1000 data has been used as training data and 600 data has been used as a data testing. In this experiment, the dataset consist of two sets (A and E) from non-epileptic people and epileptic people. This experimental results also show that the sensitivity, accuracy and specificity of the results are 100%, 99.83% and 99.67%.

AB - Epilepsy is a neurological disorder that cannot be predicted and studied. This study propose to classify epilepsy and normal Electroencephalogram (EEG) signal. Stages in the decision-making was done by using a feature extraction and combined with Wavelet Transform (WT). The result from features extraction was implemented dimension reduction method by using Principal Component Analysis (PCA) algorithm. K-Nearest Neighbor (KNN) was implemented using result from dimension reduction stages as features. In this work, 1000 data has been used as training data and 600 data has been used as a data testing. In this experiment, the dataset consist of two sets (A and E) from non-epileptic people and epileptic people. This experimental results also show that the sensitivity, accuracy and specificity of the results are 100%, 99.83% and 99.67%.

KW - Electroencephalogram

KW - KNN

KW - PCA

KW - Wavelet transform

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U2 - 10.1109/ICSITech.2017.8257094

DO - 10.1109/ICSITech.2017.8257094

M3 - Conference contribution

AN - SCOPUS:85046696977

T3 - Proceeding - 2017 3rd International Conference on Science in Information Technology: Theory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017

SP - 110

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BT - Proceeding - 2017 3rd International Conference on Science in Information Technology

A2 - Riza, Lala Septem

A2 - Pranolo, Andri

A2 - Wibawa, Aji Prasetyo

A2 - Junaeti, Enjun

A2 - Wihardi, Yaya

A2 - Hashim, Ummi Raba'ah

A2 - Horng, Shi-Jinn

A2 - Drezewski, Rafal

A2 - Lim, Heui Seok

A2 - Chakraborty, Goutam

A2 - Hernandez, Leonel

A2 - Nazir, Shah

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 3rd International Conference on Science in Information Technology, ICSITech 2017

Y2 - 25 October 2017 through 26 October 2017

ER -

Rahmawati D, Chasanah NRU, Sarno R. Classify epilepsy and normal Electroencephalogram (EEG) signal using wavelet transform and K-nearest neighbor. In Riza LS, Pranolo A, Wibawa AP, Junaeti E, Wihardi Y, Hashim UR, Horng SJ, Drezewski R, Lim HS, Chakraborty G, Hernandez L, Nazir S, editors, Proceeding - 2017 3rd International Conference on Science in Information Technology: Theory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 110-114. (Proceeding - 2017 3rd International Conference on Science in Information Technology: Theory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017). doi: 10.1109/ICSITech.2017.8257094

Classify epilepsy and normal Electroencephalogram (EEG) signal using wavelet transform and K-nearest neighbor

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