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Realization of Bandpass Filter Based on Spoof Surface Plasmon Polariton Technique at Microwave Frequency Read chapter Read first chapter

2019 | OriginalPaper | Chapter

EEG Waveform Classification Using Transform Domain Features and SVM

Authors : Hemprasad Y. Patil, Priyanka B. Patil, Seema R. Baji, Rohini S. Darade

Published in: Computing, Communication and Signal Processing

Publisher: Springer Singapore

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Abstract

Electroencephalogram (EEG) waveforms are fluctuations in brain-recorded utilizing anodes set on the scalp. Albeit a few strategies for the evaluation of working of brain, for example, MEG, PET, CT scan, and MRI have been presented, the EEG waveform is as yet an important biological signal for checking the brain signal variations because of its moderately ease and being helpful for the patient. We have presented an approach to classify the EEG waveforms into two classes, viz. epileptic and normal. The algorithm fuses the features extracted using discrete wavelet transform, discrete cosine transform, and stationary wavelet transform. The fused features are subjected to support vector machine (SVM) classifier.

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Metadata
Title
EEG Waveform Classification Using Transform Domain Features and SVM
Authors
Hemprasad Y. Patil
Priyanka B. Patil
Seema R. Baji
Rohini S. Darade
Copyright Year
2019
Publisher
Springer Singapore
Book
Computing, Communication and Signal Processing

Print ISBN: 978-981-13-1512-1

Electronic ISBN: 978-981-13-1513-8

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-981-13-1513-8_80