A recent investigation on detection and classification of epileptic seizure techniques using EEG signal
The benefits of early detection and classification of epileptic seizures in analysis, monitoring
and diagnosis for the realization and actualization of computer-aided devices and recent …
and diagnosis for the realization and actualization of computer-aided devices and recent …
EEG seizure detection: concepts, techniques, challenges, and future trends
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 …
Self-supervised learning for electroencephalography
Decades of research have shown machine learning superiority in discovering highly
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies
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 …
Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning
M Varlı, H Yılmaz - Journal of Computational Science, 2023 - Elsevier
Epilepsy stands out as one of the common neurological diseases. The neural activity of the
brain is observed using electroencephalography (EEG), which allows the diagnosis of …
brain is observed using electroencephalography (EEG), which allows the diagnosis of …
A review of the role of machine learning techniques towards brain–computer interface applications
S Rasheed - Machine Learning and Knowledge Extraction, 2021 - mdpi.com
This review article provides a deep insight into the Brain–Computer Interface (BCI) and the
application of Machine Learning (ML) technology in BCIs. It investigates the various types of …
application of Machine Learning (ML) technology in BCIs. It investigates the various types of …
Smart neurocare approach for detection of epileptic seizures using deep learning based temporal analysis of EEG patterns
Epilepsy is a psychosocial neurological disorder, which emerges as a major threat to public
health. In this age of the internet of things, the smart diagnosis of epilepsy has gained huge …
health. In this age of the internet of things, the smart diagnosis of epilepsy has gained huge …
Epileptic seizures detection in EEG signals using fusion handcrafted and deep learning features
Epilepsy is a brain disorder disease that affects people's quality of life.
Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper …
Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper …
Epileptic seizure detection using a hybrid 1D CNN‐machine learning approach from EEG data
Electroencephalography (EEG) is a widely used technique for the detection of epileptic
seizures. It can be recorded in a noninvasive manner to present the electrical activity of the …
seizures. It can be recorded in a noninvasive manner to present the electrical activity of the …
Automatic epileptic seizure detection in EEG signals using sparse common spatial pattern and adaptive short-time Fourier transform-based synchrosqueezing …
Epilepsy can now be diagnosed more accurately and quickly due to computer-aided seizure
detection utilizing Electroencephalography (EEG) recordings. In this work, a novel method …
detection utilizing Electroencephalography (EEG) recordings. In this work, a novel method …