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The Journal of Supercomputing
Erschienen in: The Journal of Supercomputing 6/2022

12.01.2022

Epileptic seizure endorsement technique using DWT power spectrum

verfasst von: Anand Ghuli, Damodar Reddy Edla, João Manuel R. S. Tavares

Erschienen in: The Journal of Supercomputing | Ausgabe 6/2022

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Abstract

EEG signals play significant role in the study of mental disorders. Epilepsy is one of the major mental disorders and need significant technological support in the treatment. A method proposed here is an endorsement technique for epileptic seizures using electroencephalogram (EEG) signals captured using non-invasive method. The method uses power spectrum density and discrete wavelet transformation (DWT). The impact of power spectral analysis along with the usage of EEG characteristics in endorsement of epilepsy is addressed here. A publicly available EEG epileptic dataset is processed using FIR filters along with DWT. The power spectrum density and its average were compared with specific spectrum to get the results and were compared against the standard EEG signal frequency range. It is found that the usage of DWT is more accurate and reliable to process and classify the EEG data for epilepsy endorsement.
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Metadaten
Titel
Epileptic seizure endorsement technique using DWT power spectrum
verfasst von
Anand Ghuli
Damodar Reddy Edla
João Manuel R. S. Tavares
Publikationsdatum
12.01.2022
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 6/2022
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-021-04196-3

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