Multiscale Hjorth Descriptor on Epileptic EEG Classification
The electroencephalogram (EEG) examination provides information on the brain’s electricity, especially in cases of epilepsy. Since the characteristics of EEG signals are nonlinear and nonstationary, visual inspection becomes very difficult. To overcome this problem, digital EEG signal processing was...
Saved in:
| Main Authors: | Achmad Rizal, Sugondo Hadiyoso, Suci Aulia, Inung Wijayanto, null Triwiyanto, Ziani Said |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
|
| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2023/4961637 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Sample Entropy on Multidistance Signal Level Difference for Epileptic EEG Classification
by: Achmad Rizal, et al.
Published: (2018-01-01) -
A New Hjorth Distribution in Its Discrete Version
by: Hanan Haj Ahmad, et al.
Published: (2025-03-01) -
Explainable AI for Bipolar Disorder Diagnosis Using Hjorth Parameters
by: Mehrnaz Saghab Torbati, et al.
Published: (2025-01-01) -
Deep Learning with Dual-Channel Feature Fusion for Epileptic EEG Signal Classification
by: Bingbing Yu, et al.
Published: (2025-07-01) -
Multiscale Convolutional Transformer for EEG Classification of Mental Imagery in Different Modalities
by: Hyung-Ju Ahn, et al.
Published: (2023-01-01)