EEG based multi-class seizure type classification using convolutional neural network and transfer learning
Recognition of epileptic seizure type is essential for the neurosurgeon to understand the
cortical connectivity of the brain. Though automated early recognition of seizures from …
cortical connectivity of the brain. Though automated early recognition of seizures from …
Deep learning techniques for EEG signal applications–a review
D Merlin Praveena, D Angelin Sarah… - IETE journal of …, 2022 - Taylor & Francis
Electroencephalogram (EEG) can track the brain waves which contain the neural activity of
the brain. EEG signals help to understand the physiological and functional details and …
the brain. EEG signals help to understand the physiological and functional details and …
Classification of focal and non-focal EEG signals using neighborhood component analysis and machine learning algorithms
Background: Classification and localization of focal epileptic seizures provide a proper
diagnostic procedure for epilepsy patients. Visual identification of seizure activity from long …
diagnostic procedure for epilepsy patients. Visual identification of seizure activity from long …
A deep convolutional neural network method to detect seizures and characteristic frequencies using epileptic electroencephalogram (EEG) data
Background: Diagnosing epileptic seizures using electroencephalogram (EEG) in
combination with deep learning computational methods has received much attention in …
combination with deep learning computational methods has received much attention in …
Epileptic seizure identification using entropy of FBSE based EEG rhythms
V Gupta, RB Pachori - Biomedical Signal Processing and Control, 2019 - Elsevier
This paper has proposed a new method for classification of epileptic seizures based on
weighted multiscale Renyi permutation entropy (WMRPE) and rhythms obtained with Fourier …
weighted multiscale Renyi permutation entropy (WMRPE) and rhythms obtained with Fourier …
Performance evaluation of DWT based sigmoid entropy in time and frequency domains for automated detection of epileptic seizures using SVM classifier
The electroencephalogram (EEG) signal contains useful information on physiological states
of the brain and has proven to be a potential biomarker to realize the complex dynamic …
of the brain and has proven to be a potential biomarker to realize the complex dynamic …
A novel approach for classification of epileptic seizures using matrix determinant
Objective: An epileptic seizure is recognized as a neurological disorder caused by transient
and unexpected disturbance resulting from the excessive synchronous activity of the …
and unexpected disturbance resulting from the excessive synchronous activity of the …
Hybrid machine learning algorithms to predict condensate viscosity in the near wellbore regions of gas condensate reservoirs
ARB Abad, S Mousavi, N Mohamadian… - Journal of Natural Gas …, 2021 - Elsevier
Gas condensate reservoirs display unique phase behavior and are highly sensitive to
reservoir pressure changes. This makes it difficult to determine their PVT characteristics …
reservoir pressure changes. This makes it difficult to determine their PVT characteristics …
Landslide susceptibility mapping and driving mechanisms in a vulnerable region based on multiple machine learning models
H Yu, W Pei, J Zhang, G Chen - Remote Sensing, 2023 - mdpi.com
Landslides can cause severe damage to both the environment and society, and many
statistical, index-based, and inventory-based methods have been developed to assess …
statistical, index-based, and inventory-based methods have been developed to assess …
Accurate classification of seizure and seizure-free intervals of intracranial EEG signals from epileptic patients
Electroencephalogram (EEG) signals are widely used to detect epileptic seizures in a
patient's neuronal activity. Since visual inspection and interpretation of EEG signal are time …
patient's neuronal activity. Since visual inspection and interpretation of EEG signal are time …