1D-local binary pattern based feature extraction for classification of epileptic EEG signals
In this paper, an effective approach for the feature extraction of raw Electroencephalogram
(EEG) signals by means of one-dimensional local binary pattern (1D-LBP) was presented …
(EEG) signals by means of one-dimensional local binary pattern (1D-LBP) was presented …
Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks
About 1% of the people in the world suffer from epilepsy. The main characteristic of epilepsy
is the recurrent seizures. Careful analysis of the electroencephalogram (EEG) recordings …
is the recurrent seizures. Careful analysis of the electroencephalogram (EEG) recordings …
Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks
Epilepsy is the most prevalent neurological disorder in humans after stroke. Recurrent
seizure is the main characteristic of the epilepsy. Electroencephalogram (EEG) is the …
seizure is the main characteristic of the epilepsy. Electroencephalogram (EEG) is the …
Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network
There are numerous neurological disorders such as dementia, headache, traumatic brain
injuries, stroke, and epilepsy. Out of these epilepsy is the most prevalent neurological …
injuries, stroke, and epilepsy. Out of these epilepsy is the most prevalent neurological …
Automatic feature extraction using genetic programming: An application to epileptic EEG classification
This paper applies genetic programming (GP) to perform automatic feature extraction from
original feature database with the aim of improving the discriminatory performance of a …
original feature database with the aim of improving the discriminatory performance of a …
Emotion recognition using effective connectivity and pre-trained convolutional neural networks in EEG signals
S Bagherzadeh, K Maghooli, A Shalbaf… - Cognitive …, 2022 - Springer
Abstract Convolutional Neural Networks (CNN) have recently made considerable advances
in the field of biomedical signal processing. These methodologies can assist in emotion …
in the field of biomedical signal processing. These methodologies can assist in emotion …
Neuro-detect: A machine learning-based fast and accurate seizure detection system in the IoMT
Epilepsy, which is characterized by recurrent spontaneous seizures, has a considerably
negative impact on both the quality and the expectancy of life of the patient. Approximately …
negative impact on both the quality and the expectancy of life of the patient. Approximately …
Hierarchical multi-class SVM with ELM kernel for epileptic EEG signal classification
ASM Murugavel, S Ramakrishnan - Medical & biological engineering & …, 2016 - Springer
In this paper, a novel hierarchical multi-class SVM (H-MSVM) with extreme learning machine
(ELM) as kernel is proposed to classify electroencephalogram (EEG) signals for epileptic …
(ELM) as kernel is proposed to classify electroencephalogram (EEG) signals for epileptic …
Patient-specific seizure detection method using hybrid classifier with optimized electrodes
RS Selvakumari, M Mahalakshmi… - Journal of medical …, 2019 - Springer
In this paper the EEG signal is analyzed by reconstructing the time series EEG signal in High
dimensional Phase Space. The computational complexity in higher dimension is reduced by …
dimensional Phase Space. The computational complexity in higher dimension is reduced by …
Seizure detection in EEG signals: A comparison of different approaches
HR Mohseni, A Maghsoudi… - … conference of the IEEE …, 2006 - ieeexplore.ieee.org
In this paper, the performance of traditional variance-based method for detection of epileptic
seizures in EEG signals are compared with various methods based on nonlinear time series …
seizures in EEG signals are compared with various methods based on nonlinear time series …