Tool condition monitoring for high-performance machining systems—A review
In the era of the “Industry 4.0” revolution, self-adjusting and unmanned machining systems
have gained considerable interest in high-value manufacturing industries to cope with the …
have gained considerable interest in high-value manufacturing industries to cope with the …
EEG seizure detection: concepts, techniques, challenges, and future trends
AA Ein Shoka, MM Dessouky, A El-Sayed… - Multimedia Tools and …, 2023 - Springer
A central nervous system disorder is usually referred to as epilepsy. In epilepsy brain activity
becomes abnormal, leading to times of abnormal behavior or seizures, and at times loss of …
becomes abnormal, leading to times of abnormal behavior or seizures, and at times loss of …
Multivariate variational mode decomposition
N ur Rehman, H Aftab - IEEE Transactions on signal …, 2019 - ieeexplore.ieee.org
We present a generic extension of variational mode decomposition (VMD) algorithm to
multivariate or multichannel data. The proposed method utilizes a model for multivariate …
multivariate or multichannel data. The proposed method utilizes a model for multivariate …
A machine learning approach to epileptic seizure prediction using Electroencephalogram (EEG) Signal
M Savadkoohi, T Oladunni, L Thompson - Biocybernetics and Biomedical …, 2020 - Elsevier
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 …
Epilepsy detection by using scalogram based convolutional neural network from EEG signals
Ö Türk, MS Özerdem - Brain sciences, 2019 - mdpi.com
The studies implemented with Electroencephalogram (EEG) signals are progressing very
rapidly and brain computer interfaces (BCI) and disease determinations are carried out at …
rapidly and brain computer interfaces (BCI) and disease determinations are carried out at …
Epileptic-seizure classification using phase-space representation of FBSE-EWT based EEG sub-band signals and ensemble learners
Electroencephalogram (EEG) signals are non-linear and non-stationary in nature. The
phase-space representation (PSR) method is useful for analysing the non-linear …
phase-space representation (PSR) method is useful for analysing the non-linear …
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 …
A novel deep neural network for robust detection of seizures using EEG signals
The detection of recorded epileptic seizure activity in electroencephalogram (EEG)
segments is crucial for the classification of seizures. Manual recognition is a time …
segments is crucial for the classification of seizures. Manual recognition is a time …
Schizophrenia detection using MultivariateEmpirical Mode Decomposition and entropy measures from multichannel EEG signal
PT Krishnan, ANJ Raj, P Balasubramanian… - Biocybernetics and …, 2020 - Elsevier
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 …
Epileptic eeg classification by using time-frequency images for deep learning
Epilepsy is one of the most common brain disorders worldwide. The most frequently used
clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings …
clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings …