Complexity analysis in focal epilepsy using entropy methods

Prajna Upadhyaya, G. Muralidhar Bairy, Tohru Yagi

Research output: Contribution to journalArticle

Abstract

Epilepsy is a neurological disorder characterized by abnormal electrical activity of neurons affecting a small or large area of the brain. Epilepsy is categorized into focal epilepsy and generalized epilepsy. Focal epilepsy is due to abnormal electrical activity that effects the smaller brain sections, whereas in generalized epilepsy the larger section of the brain is affected. Epilepsy affects the patient's personality, behavior, and emotions, making daily routines difficult. Early detection is therefore essential. In this study, we use electroencephalogram (EEG) signals to detect the focal epilepsy. However, due to the random and non-stationary characteristics it is difficult to analyze the subtle changes in the EEG by visual inspection. Hence, in this study we proposed a system that classifies focal epileptic and normal signals accurately. In order to analyze the difference in the randomness of the focal epileptic and the normal signals we have used fuzzy approximation entropy and spectral entropy. The system first decomposes the signal, and then features are extracted using fuzzy approximation entropy and spectral entropy. We obtain classification accuracies of 95% and 85% using 4-nearest neighbor and support vector machine classifiers, respectively.

Original languageEnglish
Pages (from-to)1027-1032
Number of pages6
JournalIEEJ Transactions on Electronics, Information and Systems
Volume139
Issue number9
DOIs
Publication statusPublished - 01-01-2019

Fingerprint

Entropy
Brain
Electroencephalography
Neurons
Support vector machines
Classifiers
Inspection

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

@article{c33c3a2f24bc417ca6a53051880bc849,
title = "Complexity analysis in focal epilepsy using entropy methods",
abstract = "Epilepsy is a neurological disorder characterized by abnormal electrical activity of neurons affecting a small or large area of the brain. Epilepsy is categorized into focal epilepsy and generalized epilepsy. Focal epilepsy is due to abnormal electrical activity that effects the smaller brain sections, whereas in generalized epilepsy the larger section of the brain is affected. Epilepsy affects the patient's personality, behavior, and emotions, making daily routines difficult. Early detection is therefore essential. In this study, we use electroencephalogram (EEG) signals to detect the focal epilepsy. However, due to the random and non-stationary characteristics it is difficult to analyze the subtle changes in the EEG by visual inspection. Hence, in this study we proposed a system that classifies focal epileptic and normal signals accurately. In order to analyze the difference in the randomness of the focal epileptic and the normal signals we have used fuzzy approximation entropy and spectral entropy. The system first decomposes the signal, and then features are extracted using fuzzy approximation entropy and spectral entropy. We obtain classification accuracies of 95{\%} and 85{\%} using 4-nearest neighbor and support vector machine classifiers, respectively.",
author = "Prajna Upadhyaya and {Muralidhar Bairy}, G. and Tohru Yagi",
year = "2019",
month = "1",
day = "1",
doi = "10.1541/ieejeiss.139.1027",
language = "English",
volume = "139",
pages = "1027--1032",
journal = "IEEJ Transactions on Electronics, Information and Systems",
issn = "0385-4221",
publisher = "The Institute of Electrical Engineers of Japan",
number = "9",

}

Complexity analysis in focal epilepsy using entropy methods. / Upadhyaya, Prajna; Muralidhar Bairy, G.; Yagi, Tohru.

In: IEEJ Transactions on Electronics, Information and Systems, Vol. 139, No. 9, 01.01.2019, p. 1027-1032.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Complexity analysis in focal epilepsy using entropy methods

AU - Upadhyaya, Prajna

AU - Muralidhar Bairy, G.

AU - Yagi, Tohru

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Epilepsy is a neurological disorder characterized by abnormal electrical activity of neurons affecting a small or large area of the brain. Epilepsy is categorized into focal epilepsy and generalized epilepsy. Focal epilepsy is due to abnormal electrical activity that effects the smaller brain sections, whereas in generalized epilepsy the larger section of the brain is affected. Epilepsy affects the patient's personality, behavior, and emotions, making daily routines difficult. Early detection is therefore essential. In this study, we use electroencephalogram (EEG) signals to detect the focal epilepsy. However, due to the random and non-stationary characteristics it is difficult to analyze the subtle changes in the EEG by visual inspection. Hence, in this study we proposed a system that classifies focal epileptic and normal signals accurately. In order to analyze the difference in the randomness of the focal epileptic and the normal signals we have used fuzzy approximation entropy and spectral entropy. The system first decomposes the signal, and then features are extracted using fuzzy approximation entropy and spectral entropy. We obtain classification accuracies of 95% and 85% using 4-nearest neighbor and support vector machine classifiers, respectively.

AB - Epilepsy is a neurological disorder characterized by abnormal electrical activity of neurons affecting a small or large area of the brain. Epilepsy is categorized into focal epilepsy and generalized epilepsy. Focal epilepsy is due to abnormal electrical activity that effects the smaller brain sections, whereas in generalized epilepsy the larger section of the brain is affected. Epilepsy affects the patient's personality, behavior, and emotions, making daily routines difficult. Early detection is therefore essential. In this study, we use electroencephalogram (EEG) signals to detect the focal epilepsy. However, due to the random and non-stationary characteristics it is difficult to analyze the subtle changes in the EEG by visual inspection. Hence, in this study we proposed a system that classifies focal epileptic and normal signals accurately. In order to analyze the difference in the randomness of the focal epileptic and the normal signals we have used fuzzy approximation entropy and spectral entropy. The system first decomposes the signal, and then features are extracted using fuzzy approximation entropy and spectral entropy. We obtain classification accuracies of 95% and 85% using 4-nearest neighbor and support vector machine classifiers, respectively.

UR - http://www.scopus.com/inward/record.url?scp=85071930766&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85071930766&partnerID=8YFLogxK

U2 - 10.1541/ieejeiss.139.1027

DO - 10.1541/ieejeiss.139.1027

M3 - Article

AN - SCOPUS:85071930766

VL - 139

SP - 1027

EP - 1032

JO - IEEJ Transactions on Electronics, Information and Systems

JF - IEEJ Transactions on Electronics, Information and Systems

SN - 0385-4221

IS - 9

ER -