A review for dynamics in neuron and neuronal network
J Ma, J Tang - Nonlinear Dynamics, 2017 - Springer
Abstract The biological Hodgkin–Huxley model and its simplified versions have confirmed its
effectiveness for recognizing and understanding the electrical activities in neurons, and …
effectiveness for recognizing and understanding the electrical activities in neurons, and …
Artificial intelligence techniques for automated diagnosis of neurological disorders
Background: Authors have been advocating the research ideology that a computer-aided
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
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 …
A novel multi-class EEG-based sleep stage classification system
Sleep stage classification is one of the most critical steps in effective diagnosis and the
treatment of sleep-related disorders. Visual inspection undertaken by sleep experts is a time …
treatment of sleep-related disorders. Visual inspection undertaken by sleep experts is a time …
Automatic seizure detection using orthogonal matching pursuit, discrete wavelet transform, and entropy based features of EEG signals
Background and objective Epilepsy is a prevalent disorder that affects the central nervous
system, causing seizures. In the current study, a novel algorithm is developed using …
system, causing seizures. In the current study, a novel algorithm is developed using …
Machine learning and deep learning approach for medical image analysis: diagnosis to detection
Computer-aided detection using Deep Learning (DL) and Machine Learning (ML) shows
tremendous growth in the medical field. Medical images are considered as the actual origin …
tremendous growth in the medical field. Medical images are considered as the actual origin …
Machine learning-based EEG signals classification model for epileptic seizure detection
The detection of epileptic seizures by classifying electroencephalography (EEG) signals into
ictal and interictal classes is a demanding challenge, because it identifies the seizure and …
ictal and interictal classes is a demanding challenge, because it identifies the seizure and …
A machine learning approach to epileptic seizure prediction using Electroencephalogram (EEG) Signal
This study investigates the properties of the brain electrical activity from different recording
regions and physiological states for seizure detection. Neurophysiologists will find the work …
regions and physiological states for seizure detection. Neurophysiologists will find the work …
Tunable-Q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals
This paper analyzes the underlying complexity and non-linearity of electroencephalogram
(EEG) signals by computing a novel multi-scale entropy measure for the classification of …
(EEG) signals by computing a novel multi-scale entropy measure for the classification of …
Schizophrenia detection using MultivariateEmpirical Mode Decomposition and entropy measures from multichannel EEG signal
Multivariate analysis of the EEG signal for the detection of Schizophrenia condition is
proposed here. Multivariate Empirical Mode Decomposition (MEMD) is used to decompose …
proposed here. Multivariate Empirical Mode Decomposition (MEMD) is used to decompose …