Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain
AB Das, MIH Bhuiyan - biomedical signal processing and control, 2016 - Elsevier
In this paper, a comprehensive analysis of focal and non-focal electroencephalography is
carried out in the empirical mode decomposition and discrete wavelet transform domains. A …
carried out in the empirical mode decomposition and discrete wavelet transform domains. A …
Seizure suppression efficacy of closed-loop versus open-loop deep brain stimulation in a rodent model of epilepsy
MT Salam, JLP Velazquez… - IEEE Transactions on …, 2015 - ieeexplore.ieee.org
We assess and compare the effects of both closed-loop and open-loop neurostimulation of
the rat hippocampus by means of a custom low-power programmable therapeutic …
the rat hippocampus by means of a custom low-power programmable therapeutic …
Classification of focal and non-focal EEG signals in VMD-DWT domain using ensemble stacking
MM Rahman, MIH Bhuiyan, AB Das - Biomedical Signal Processing and …, 2019 - Elsevier
Classification of focal and non-focal Electroencephalogram (EEG) signals is an important
problem especially for the identification of epileptogenic sites in the brain. However, the …
problem especially for the identification of epileptogenic sites in the brain. However, the …
Epileptic seizures classification based on long-term EEG signal wavelet analysis
Epilepsy is a complex neurological disorder recognized by abnormal synchronization of
cerebral neurons, named seizures. During the last decades, significant progress has been …
cerebral neurons, named seizures. During the last decades, significant progress has been …
An ultra-low power smart headband for real-time epileptic seizure detection
In this paper, the design of a smart headband for epileptic seizure detection is presented.
The proposed headband consists of four key components: 1) an analog front-end circuitry; 2) …
The proposed headband consists of four key components: 1) an analog front-end circuitry; 2) …
Boundary effects for EMD-based algorithms
YH Wang, SH Cheng - IEEE Signal Processing Letters, 2022 - ieeexplore.ieee.org
Empirical mode decomposition (EMD) and its improved EMD-based algorithms are adaptive
and nonlinear methods that decompose a nonstationary signal into several intrinsic mode …
and nonlinear methods that decompose a nonstationary signal into several intrinsic mode …
Classification and discrimination of focal and non-focal EEG signals based on deep neural network
In this paper, a new model of focal and non-focal electroencephalography classification is
carried out using a deep neural network (DNN). The Convolution Architecture For Feature …
carried out using a deep neural network (DNN). The Convolution Architecture For Feature …
Automated detection and classification of high frequency oscillations (HFOs) in human intracereberal EEG
Discrete high-frequency oscillations (HFOs) in the range of 80–500 Hz have previously been
recorded from human epileptic brains using intracereberal EEG and seem to be a reliable …
recorded from human epileptic brains using intracereberal EEG and seem to be a reliable …
A review of automated methodologies for the detection of epileptic episodes using long-term EEG signals
Epilepsy is a chronic neurological condition caused by abnormal electrical activity of the
human brain that affects up to 1% of the global population. Since seizures may occur at any …
human brain that affects up to 1% of the global population. Since seizures may occur at any …
Rapid brief feedback intracerebral stimulation based on real‐time desynchronization detection preceding seizures stops the generation of convulsive paroxysms
Objective To investigate the abortion of seizure generation using “minimal” intervention in
hippocampi using two rat models of human temporal lobe epilepsy. Methods The recording …
hippocampi using two rat models of human temporal lobe epilepsy. Methods The recording …