Epilepsy detection in 121 patient populations using hypercube pattern from EEG signals

I Tasci, B Tasci, PD Barua, S Dogan, T Tuncer… - Information …, 2023 - Elsevier
Background Epilepsy is one of the most commonly seen neurologic disorders worldwide
and has generally caused seizures. Electroencephalography (EEG) is widely used in …

EEG signal processing for epilepsy seizure detection using 5-level Db4 discrete wavelet transform, GA-based feature selection and ANN/SVM classifiers

M Omidvar, A Zahedi, H Bakhshi - Journal of ambient intelligence and …, 2021 - Springer
Epilepsy is a neurobiological disease caused by abnormal electrical activity of the human
brain. It is important to detect the epileptic seizures to help the epileptic patients. Using brain …

Automatic epileptic EEG detection using DT-CWT-based non-linear features

M Li, W Chen, T Zhang - Biomedical Signal Processing and Control, 2017 - Elsevier
The epilepsy is a type of common neurological disorder plaguing many people around the
world. A novel method based on the dual-tree complex wavelet transform (DT-CWT), in this …

Classification of focal and non focal epileptic seizures using multi-features and SVM classifier

N Sriraam, S Raghu - Journal of medical systems, 2017 - Springer
Identifying epileptogenic zones prior to surgery is an essential and crucial step in treating
patients having pharmacoresistant focal epilepsy. Electroencephalogram (EEG) is a …

Automatic epilepsy detection using wavelet-based nonlinear analysis and optimized SVM

M Li, W Chen, T Zhang - Biocybernetics and biomedical engineering, 2016 - Elsevier
Aiming at the problems of low accuracy, poor universality and functional singleness for
seizure detection, an effective approach using wavelet-based non-linear analysis and …

MMSFL-OWFB: A novel class of orthogonal wavelet filters for epileptic seizure detection

M Sharma, AA Bhurane, UR Acharya - Knowledge-Based Systems, 2018 - Elsevier
The optimal filters with minimal bandwidth are highly desirable in many applications such as
communication and biomedical signal processing. In this study, we design optimally …

[HTML][HTML] EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms

IB Slimen, L Boubchir, Z Mbarki… - Journal of biomedical …, 2020 - ncbi.nlm.nih.gov
The visual analysis of common neurological disorders such as epileptic seizures in
electroencephalography (EEG) is an oversensitive operation and prone to errors, which has …

A Tunable-Q wavelet transform and quadruple symmetric pattern based EEG signal classification method

E Aydemir, T Tuncer, S Dogan - Medical hypotheses, 2020 - Elsevier
Electroencephalography (EEG) signals have been widely used to diagnose brain diseases
for instance epilepsy, Parkinson's Disease (PD), Multiple Skleroz (MS), and many machine …

On the use of wavelet domain and machine learning for the analysis of epileptic seizure detection from EEG signals

KVN Kavitha, S Ashok, AL Imoize, S Ojo… - Journal of …, 2022 - Wiley Online Library
Epileptic patients suffer from an epileptic brain seizure caused by the temporary and
unpredicted electrical interruption. Conventionally, the electroencephalogram (EEG) signals …

Channel based epilepsy seizure type detection from electroencephalography (EEG) signals with machine learning techniques

E Tuncer, ED Bolat - Biocybernetics and Biomedical Engineering, 2022 - Elsevier
Epileptic seizures result from disturbances in the electrical activity of the brain, classified as
focal, generalized, or unknown. Failure to correctly classify epileptic seizures may result in …