An Empirical Study ofSignal Transformation Techniques onEpileptic Seizures Using EEG Data

Title

An Empirical Study ofSignal Transformation Techniques onEpileptic Seizures Using EEG Data

Creator

Umme Salma M.; Najmusseher

Description

Signal processing may be a mathematical approach to manipulate the signals for varied applications. A mathematical relation that changes the signal from one kind to a different is named a transformation technique in the signal process. Digital processing of electroencephalography (EEG) signals plays a significant role in a highly multiple application, e.g., seizure detection, prediction, and classification. In these applications, the transformation techniques play an essential role. Signal transformation techniques are acquainted with improved transmission, storage potency, and subjective quality and collectively emphasize or discover components of interest in an extremely measured EEG signal.The transformed signals result in better classification. This article provides a study on some of the important techniques used for transformation of EEG data. During this work, we have studied six signal transformation techniques like linear regression, logistic regression, discrete wavelet transform, wavelet transform, fast Fourier transform, and principal component analysis with Eigen vector to envision their impact on the classification of epileptic seizures. Linear regression, logistic regression, and discrete wavelet transform provides high accuracy of 100%, and wavelet transform produced an accuracy of 96.35%. The proposed work is an empirical study whose main aim is to discuss some typical EEG signal transformation methods, examine their performances for epileptic seizure prediction, and eventually recommend the foremost acceptable technique for signal transformation supported by the performance. This work also highlights the advantages and disadvantages of all seven transformation techniques providing a precise comparative analysis in conjunction with the accuracy. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Source

Lecture Notes on Data Engineering and Communications Technologies, Vol-111, pp. 797-806.

Date

2022-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Brain disorder; EEG; Epileptic seizures; Feature selection; Signal transformation techniques

Coverage

Umme Salma M., Assistant Professor Department of Computer Science, Christ (Deemed to be University), Hosur Road, Bangalore, India; Najmusseher, Research Scholar Department of Computer Science, Christ (Deemed to be University), Hosur Road, Bangalore, India

Rights

Restricted Access

Relation

ISSN: 23674512

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1007/978-981-16-9113-3_58

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133549247&doi=10.1007%2f978-981-16-9113-3_58&partnerID=40&md5=7108ff4d6c1b6129f3438b59ab2358bb