2022
DOI: 10.1016/j.dsp.2021.103349
|View full text |Cite
|
Sign up to set email alerts
|

Weighted 1D-local binary pattern features and Taylor-Henry gas solubility optimization based Deep Maxout network for discovering epileptic seizure using EEG

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
5
1

Relationship

0
6

Authors

Journals

citations
Cited by 8 publications
(2 citation statements)
references
References 32 publications
0
2
0
Order By: Relevance
“…Jaffino et al [90] proposed an optimization-aware deep learning model for epilepsy detection using EEG signals. The study employs feature extraction techniques such as tonal power ratio, MKMFCC, power spectral density, logarithmic band power, spectral entropy, and relative amplitude.…”
Section: ) Others Strategiesmentioning
confidence: 99%
“…Jaffino et al [90] proposed an optimization-aware deep learning model for epilepsy detection using EEG signals. The study employs feature extraction techniques such as tonal power ratio, MKMFCC, power spectral density, logarithmic band power, spectral entropy, and relative amplitude.…”
Section: ) Others Strategiesmentioning
confidence: 99%
“…In last two decades local descriptors [1][2][3] have shown significant improvements with respect to the application they were developed. Local descriptors have gone from strength to strength during the passing years.…”
Section: Introductionmentioning
confidence: 99%