NDLI: Exploring Hermite transformation in brain signal analysis for the detection of epileptic seizure

Content Provider	IET Digital Library
Author	Siuly, Siuly Alcin, Omer F. Bajaj, Varun Sengur, Abdulkadir Zhang, Yanchun
Abstract	Automatic detection of epileptic seizure from brain signal data (e.g. electroencephalogram (EEG)) is very crucial due to dynamic and complex nature of EEG signal (e.g. non-stationarity, aperiodic and chaotic). Owing to these natures, manual interpretation and detection of epileptic seizure is not reliable and efficient process. Hence, this study is intended to develop a new computer-aided detection system that can automatically and efficiently identify epileptic seizure from huge amount EEG data. In this study, Hermite Transform is introduced for extracting discriminating information from EEG data for the detection of epileptic seizure. The analysis is performed in three stages: EEG signal transformation into a new form by Hermite Transform; computation of three types of features, namely permutation entropy, histogram feature and statistical feature; and classification of obtained features by least square support vector machine. The classification outcomes reveal the presence of epileptic seizure. The proposed method is evaluated on a benchmark Epileptic EEG database (Bonn University data) and the performance of this method is compared with several state-of-art algorithms for the same database. The experimental results demonstrate that the proposed scheme has the ability to efficiently detect epileptic seizure from EEG data outperforming competing techniques in terms of overall classification accuracy.
Starting Page	35
Ending Page	41
Page Count	7
ISSN	17518822
Volume Number	13
e-ISSN	17518830
Issue Number	Issue 1, Jan (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-smt/13/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-smt.2018.5358
Journal	IET Science, Measurement & Technology
Publisher Date	2018-08-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automatic Detection Benchmark Epileptic EEG Database Bioelectric Signal Biology And Medical Computing Bonn University EEG Data Brain Signal Analysis Brain Signal Data Classification Accuracy Computer-aided Detection System Digital Signal Processing EEG Signal Transformation Electrical Activity in Neurophysiological Processes Electrodiagnostics And Other Electrical Measurement Technique Electroencephalogram Electroencephalography Entropy Epileptic Seizure Detection Feature Extraction Hermite Transformation Histogram Feature Integral Transforms in Numerical Analysis Interpolation And Function Approximation Knowledge Engineering Technique Least Square Support Vector Machine Least Squares Approximation Medical Disorders Medical Signal Detection Medical Signal Processing Numerical Analysis Permutation Entropy Probability Theory Signal Classification Statistical Analysis Statistical Feature Statistics Stochastic Linearised SCUC Support Vector Machine Transform
Content Type	Text
Resource Type	Article
Subject	Atomic and Molecular Physics, and Optics Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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