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http://hdl.handle.net/123456789/161
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DC Field | Value | Language |
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dc.contributor.author | Ghuli, Anand | - |
dc.contributor.author | Reddy Edla, Damodar | - |
dc.contributor.author | Tavares, João Manuel R. S. | - |
dc.date.accessioned | 2022-01-13T06:35:26Z | - |
dc.date.available | 2022-01-13T06:35:26Z | - |
dc.date.issued | 2021-01 | - |
dc.identifier.uri | http://hdl.handle.net/123456789/161 | - |
dc.description.abstract | EEG signals play signifcant role in the study of mental disorders. Epilepsy is one of the major mental disorders and need signifcant 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 flters along with DWT. The power spectrum density and its average were compared with specifc 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. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | Springer | en_US |
dc.subject | Electroencephalogram · Brain · Signal analysis · Power spectrum · DWT | en_US |
dc.title | Epileptic seizure endorsement technique using DWT power spectrum | en_US |
dc.type | Article | en_US |
Appears in Collections: | F P |
Files in This Item:
File | Description | Size | Format | |
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Ghuli2022_Article_EpilepticSeizureEndorsementTec.pdf | 1.81 MB | Adobe PDF | View/Open |
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