Recent advances in the use of focused ultrasound as a treatment for epilepsy
E Lescrauwaet, K Vonck, M Sprengers… - Frontiers in …, 2022 - frontiersin.org
Epilepsy affects about 1% of the population. Approximately one third of patients with
epilepsy are drug-resistant (DRE). Resective surgery is an effective treatment for DRE, yet …
epilepsy are drug-resistant (DRE). Resective surgery is an effective treatment for DRE, yet …
Machine learning algorithms for epilepsy detection based on published EEG databases: A systematic review
Epilepsy is the only neurological condition for which electroencephalography (EEG) is the
primary diagnostic and important prognostic clinical tool. However, the manual inspection of …
primary diagnostic and important prognostic clinical tool. However, the manual inspection of …
Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies
A Shoeibi, N Ghassemi, M Khodatars… - … Signal Processing and …, 2022 - Elsevier
Epileptic seizures are one of the most crucial neurological disorders, and their early
diagnosis will help the clinicians to provide accurate treatment for the patients. The …
diagnosis will help the clinicians to provide accurate treatment for the patients. The …
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 …
Personalized real-time federated learning for epileptic seizure detection
Epilepsy is one of the most prevalent paroxystic neurological disorders. It is characterized by
the occurrence of spontaneous seizures. About 1 out of 3 patients have drug-resistant …
the occurrence of spontaneous seizures. About 1 out of 3 patients have drug-resistant …
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 …
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 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 …
Automated detection of epileptic seizures using successive decomposition index and support vector machine classifier in long-term EEG
Epilepsy is a commonly observed long-term neurological disorder that impairs nerve cell
activity in the brain and has a severe impact on people's daily lives. Accurate seizure …
activity in the brain and has a severe impact on people's daily lives. Accurate seizure …