[HTML][HTML] Epileptic multi-seizure type classification using electroencephalogram signals from the Temple University Hospital Seizure Corpus: A review
Epilepsy is one of the most paramount neurological diseases, affecting about 1% of the
world's population. Seizure detection and classification are difficult tasks and are ongoing …
world's population. Seizure detection and classification are difficult tasks and are ongoing …
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 …
Affect recognition from scalp-EEG using channel-wise encoder networks coupled with geometric deep learning and multi-channel feature fusion
The expression of human emotions is a complex process that often manifests through
physiological and psychological traits and results in spatio-temporal brain activity. The brain …
physiological and psychological traits and results in spatio-temporal brain activity. The brain …
MP-SeizNet: A multi-path CNN Bi-LSTM Network for seizure-type classification using EEG
Seizure type identification is essential for the treatment and management of epileptic
patients. However, it is a difficult process known to be time consuming and labor intensive …
patients. However, it is a difficult process known to be time consuming and labor intensive …
Optimization of epilepsy detection method based on dynamic EEG channel screening
Y Song, C Fan, X Mao - Neural Networks, 2024 - Elsevier
To decrease the interference in the process of epileptic feature extraction caused by
insufficient detection capability in partial channels of focal epilepsy, this paper proposes a …
insufficient detection capability in partial channels of focal epilepsy, this paper proposes a …
Cosine convolutional neural network and its application for seizure detection
Traditional convolutional neural networks (CNNs) often suffer from high memory
consumption and redundancy in their kernel representations, leading to overfitting problems …
consumption and redundancy in their kernel representations, leading to overfitting problems …
Supervised machine learning and deep learning techniques for epileptic seizure recognition using EEG signals—A systematic literature review
Electroencephalography (EEG) is a complicated, non-stationary signal that requires
extensive preprocessing and feature extraction approaches to be accurately analyzed. In …
extensive preprocessing and feature extraction approaches to be accurately analyzed. In …
Real-time seizure detection using EEG: a comprehensive comparison of recent approaches under a realistic setting
Electroencephalogram (EEG) is an important diagnostic test that physicians use to record
brain activity and detect seizures by monitoring the signals. There have been several …
brain activity and detect seizures by monitoring the signals. There have been several …
A review on software and hardware developments in automatic epilepsy diagnosis using EEG datasets
Epilepsy is a common non‐communicable, group of neurological disorders affecting more
than 50 million individuals worldwide. Different approaches of basic, clinical, and …
than 50 million individuals worldwide. Different approaches of basic, clinical, and …
SMARTSeiz: deep learning with attention mechanism for accurate seizure recognition in iot healthcare devices
The Internet of Things (IoT) is capable of controlling the healthcare monitoring system for
remote-based patients. Epilepsy, a chronic brain syndrome characterized by recurrent …
remote-based patients. Epilepsy, a chronic brain syndrome characterized by recurrent …