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 …
Intelligent fault detection scheme for microgrids with wavelet-based deep neural networks
Fault detection is essential in microgrid control and operation, as it enables the system to
perform fast fault isolation and recovery. The adoption of inverter-interfaced distributed …
perform fast fault isolation and recovery. The adoption of inverter-interfaced distributed …
Online false data injection attack detection with wavelet transform and deep neural networks
State estimation is critical to the operation and control of modern power systems. However,
many cyber-attacks, such as false data injection attacks, can circumvent conventional …
many cyber-attacks, such as false data injection attacks, can circumvent conventional …
Artificial intelligence in epilepsy—applications and pathways to the clinic
Artificial intelligence (AI) is rapidly transforming health care, and its applications in epilepsy
have increased exponentially over the past decade. Integration of AI into epilepsy …
have increased exponentially over the past decade. Integration of AI into epilepsy …
Epileptic seizure focus detection from interictal electroencephalogram: a survey
MR Islam, X Zhao, Y Miao, H Sugano… - Cognitive neurodynamics, 2023 - Springer
Electroencephalogram (EEG) is one of most effective clinical diagnosis modalities for the
localization of epileptic focus. Most current AI solutions use this modality to analyze the EEG …
localization of epileptic focus. Most current AI solutions use this modality to analyze the EEG …
Time-frequency domain deep convolutional neural network for the classification of focal and non-focal EEG signals
S Madhavan, RK Tripathy, RB Pachori - IEEE Sensors Journal, 2019 - ieeexplore.ieee.org
The neurological disease such as the epilepsy is diagnosed using the analysis of
electroencephalogram (EEG) recordings. The areas of the brain associated with the …
electroencephalogram (EEG) recordings. The areas of the brain associated with the …
Exploring the applicability of transfer learning and feature engineering in epilepsy prediction using hybrid transformer model
Objective: Epilepsy prediction algorithms offer patients with drug-resistant epilepsy a way to
reduce unintended harm from sudden seizures. The purpose of this study is to investigate …
reduce unintended harm from sudden seizures. The purpose of this study is to investigate …
Classification of epileptic EEG signals using PSO based artificial neural network and tunable-Q wavelet transform
Epilepsy is a widely spread neurological disorder caused due to the abnormal excessive
neural activity which can be diagnosed by inspecting the electroencephalography (EEG) …
neural activity which can be diagnosed by inspecting the electroencephalography (EEG) …
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 unified framework and method for EEG-based early epileptic seizure detection and epilepsy diagnosis
Z Chen, G Lu, Z Xie, W Shang - IEEE Access, 2020 - ieeexplore.ieee.org
Electroencephalogram (EEG) contains important physiological information that can reflect
the activity of human brain, making it useful for epileptic seizure detection and epilepsy …
the activity of human brain, making it useful for epileptic seizure detection and epilepsy …