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Automatic epileptic seizure detection in EEG signals using multi-domain feature extraction and nonlinear analysis
Epileptic seizure detection is commonly implemented by expert clinicians with visual
observation of electroencephalography (EEG) signals, which tends to be time consuming …
observation of electroencephalography (EEG) signals, which tends to be time consuming …
Epileptic seizure detection: A deep learning approach
Epilepsy is the second most common brain disorder after migraine. Automatic detection of
epileptic seizures can considerably improve the patients' quality of life. Current …
epileptic seizures can considerably improve the patients' quality of life. Current …
Hilbert marginal spectrum analysis for automatic seizure detection in EEG signals
K Fu, J Qu, Y Chai, T Zou - Biomedical Signal Processing and Control, 2015 - Elsevier
In this paper, we present a new technique for automatic seizure detection in
electroencephalogram (EEG) signals by using Hilbert marginal spectrum (HMS) analysis. As …
electroencephalogram (EEG) signals by using Hilbert marginal spectrum (HMS) analysis. As …
A survey on mobile crowd-sensing and its applications in the IoT era
Mobile crowd-sensing (MCS) is a new sensing paradigm that takes advantage of the
extensive use of mobile phones that collect data efficiently and enable several significant …
extensive use of mobile phones that collect data efficiently and enable several significant …
Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter …
L Hussain - Cognitive neurodynamics, 2018 - Springer
Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the
brain. The research reveals that brain activity is monitored through electroencephalogram …
brain. The research reveals that brain activity is monitored through electroencephalogram …
Ensemble classifier for epileptic seizure detection for imperfect EEG data
Brain status information is captured by physiological electroencephalogram (EEG) signals,
which are extensively used to study different brain activities. This study investigates the use …
which are extensively used to study different brain activities. This study investigates the use …
Regression analysis for detecting epileptic seizure with different feature extracting strategies
Due to the excitability of neurons in the brain, a neurological disorder is produced known as
epilepsy. The brain activity of patients suffering from epilepsy is monitored through …
epilepsy. The brain activity of patients suffering from epilepsy is monitored through …
EEG signals analysis for epileptic seizures detection using polynomial transforms, linear discriminant analysis and support vector machines
LCD Nkengfack, D Tchiotsop, R Atangana… - … Signal Processing and …, 2020 - Elsevier
Electroencephalogram (EEG) signals are useful in understanding the human brain diseases
like epilepsy which is characterized by an enduring predisposition to generate epileptic …
like epilepsy which is characterized by an enduring predisposition to generate epileptic …
Robust detection of epileptic seizures based on L1-penalized robust regression of EEG signals
Epilepsy is the second common brain disorder affecting 70 million people worldwide.
Electroencephalogram (EEG) has been widely used for the diagnosis of epileptic seizures …
Electroencephalogram (EEG) has been widely used for the diagnosis of epileptic seizures …
A comprehensive survey of the feature extraction methods in the EEG research
This survey paper categories, compares, and summaries from published technical and
review articles in feature extraction methods in Electroence-phalography research and …
review articles in feature extraction methods in Electroence-phalography research and …