TY - GEN
T1 - A Comparative Assessment of Machine Learning Techniques for Epilepsy Detection using EEG Signal
AU - Dhanka, Balan
AU - Vijayvargiya, Ankit
AU - Kumar, Rajesh
AU - Singh, Ghanshyam
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/7
Y1 - 2020/11/7
N2 - Epilepsy is a psychological issue that causes ridiculous, repetitive seizures. A seizure is an unexpected surge of electrical activity in the cerebrum. Since traditional time or recurrence, area examination is discovered insufficient to portray the qualities of non-fixed signals, for example, electroencephalography (EEG) signal. In this paper, we propose to change the EEG information utilizing Detrended Fluctuation Analysis, Singular Value Decomposition Entropy, Higuchi Fractal Dimension, and Petrosian Fractal Dimension works and plan vectors. These vectors are utilized as highlights for ten distinctive Machine Learning classifiers: Logistic Regression, Support Vector Classifier, Linear Support Vector Classifier, K Nearest Neighbors Classifier, Naive Bayes Classifier, Random Forest Classifier, Decision Tree Classifier, Stochastic Gradient Descent Classifier, Multilayer Perceptron Classifier, Gaussian Process Classifier, to group epileptic seizure starting from various parts and condition of the cerebrum. Our outcomes show that the proposed strategy gives better exactness, accuracy, and review in contrast with the current strategies for multiclass Epileptic Seizure Classification.
AB - Epilepsy is a psychological issue that causes ridiculous, repetitive seizures. A seizure is an unexpected surge of electrical activity in the cerebrum. Since traditional time or recurrence, area examination is discovered insufficient to portray the qualities of non-fixed signals, for example, electroencephalography (EEG) signal. In this paper, we propose to change the EEG information utilizing Detrended Fluctuation Analysis, Singular Value Decomposition Entropy, Higuchi Fractal Dimension, and Petrosian Fractal Dimension works and plan vectors. These vectors are utilized as highlights for ten distinctive Machine Learning classifiers: Logistic Regression, Support Vector Classifier, Linear Support Vector Classifier, K Nearest Neighbors Classifier, Naive Bayes Classifier, Random Forest Classifier, Decision Tree Classifier, Stochastic Gradient Descent Classifier, Multilayer Perceptron Classifier, Gaussian Process Classifier, to group epileptic seizure starting from various parts and condition of the cerebrum. Our outcomes show that the proposed strategy gives better exactness, accuracy, and review in contrast with the current strategies for multiclass Epileptic Seizure Classification.
KW - Detrended Fluctuation Analysis
KW - EEG
KW - Epileptic Seizure
KW - Higuchi Fractal Dimension and Petrosian Fractal Dimension
KW - Singular Value Decomposition Entropy
UR - http://www.scopus.com/inward/record.url?scp=85103685069&partnerID=8YFLogxK
U2 - 10.1109/UPCON50219.2020.9376567
DO - 10.1109/UPCON50219.2020.9376567
M3 - Conference contribution
AN - SCOPUS:85103685069
T3 - 7th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2020
BT - 7th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2020
Y2 - 27 November 2020 through 29 November 2020
ER -