Machine learning algorithms for epilepsy detection based on published EEG databases: A systematic review
Epilepsy is the only neurological condition for which electroencephalography (EEG) is the
primary diagnostic and important prognostic clinical tool. However, the manual inspection of …
primary diagnostic and important prognostic clinical tool. However, the manual inspection of …
A comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals
Epilepsy, a brain disease generally associated with seizures, has tremendous effects on
people's quality of life. Diagnosis of epileptic seizures is commonly performed on …
people's quality of life. Diagnosis of epileptic seizures is commonly performed on …
Review of machine and deep learning techniques in epileptic seizure detection using physiological signals and sentiment analysis
Epilepsy is one of the significant neurological disorders affecting nearly 65 million people
worldwide. The repeated seizure is characterized as epilepsy. Different algorithms were …
worldwide. The repeated seizure is characterized as epilepsy. Different algorithms were …
EEG signal processing for epilepsy seizure detection using 5-level Db4 discrete wavelet transform, GA-based feature selection and ANN/SVM classifiers
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 …
brain. It is important to detect the epileptic seizures to help the epileptic patients. Using brain …
Epileptic seizures detection in EEG signals using fusion handcrafted and deep learning features
Epilepsy is a brain disorder disease that affects people's quality of life.
Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper …
Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper …
Machine learning approach for epileptic seizure detection using the tunable-Q wavelet transform based time–frequency features
Epilepsy is a prevalent neurological disorder among numerous neurons degenerative
diseases after brain stroke. During a seizure event, there are bursts of electrical activity in …
diseases after brain stroke. During a seizure event, there are bursts of electrical activity in …
Epileptic patient activity recognition system using extreme learning machine method
The Human Activity Recognition (HAR) system is the hottest research area in clinical
research. The HAR plays a vital role in learning about a patient's abnormal activities; based …
research. The HAR plays a vital role in learning about a patient's abnormal activities; based …
Epileptic seizures detection in EEG signals using TQWT and ensemble learning
In this paper, a new scheme for diagnosis of epileptic seizures in EEG signals using Tunable-
Q wavelet transform (TQWT) framework is proposed and benchmarked with Bonn dataset …
Q wavelet transform (TQWT) framework is proposed and benchmarked with Bonn dataset …
[HTML][HTML] A multi-dimensional hybrid CNN-BiLSTM framework for epileptic seizure detection using electroencephalogram signal scrutiny
AB KR, S Srinivasan, SK Mathivanan… - Systems and Soft …, 2023 - Elsevier
The proposed hybrid CNN-BiLSTM architecture aims to address the challenge of detecting
epileptic seizures systematically from EEG signal analysis. The system consists of several …
epileptic seizures systematically from EEG signal analysis. The system consists of several …
Bio-inspired Red Fox-Sine cosine optimization for the feature selection of SSVEP-based EEG signals for BCI applications
Abstract Background Advancements in Brain-Computer Interface (BCI) have led to the
development of various neuro-dysfunctional human assistive tools. Despite having various …
development of various neuro-dysfunctional human assistive tools. Despite having various …