In‐hospital and home‐based long‐term monitoring of focal epilepsy with a wearable electroencephalographic device: Diagnostic yield and user experience
Objective The aim is to report the performance of an electroencephalogram (EEG) seizure‐
detector algorithm on data obtained with a wearable device (WD) in patients with focal …
detector algorithm on data obtained with a wearable device (WD) in patients with focal …
Approximate zero-crossing: a new interpretable, highly discriminative and low-complexity feature for EEG and iEEG seizure detection
Objective. Long-term monitoring of people with epilepsy based on electroencephalography
(EEG) and intracranial EEG (iEEG) has the potential to deliver key clinical information for …
(EEG) and intracranial EEG (iEEG) has the potential to deliver key clinical information for …
Software advancements in automatic epilepsy diagnosis and seizure detection: 10-year review
Epilepsy is a chronic neurological disorder that may be diagnosed and monitored using
routine diagnostic tests like Electroencephalography (EEG). However, manual introspection …
routine diagnostic tests like Electroencephalography (EEG). However, manual introspection …
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 …
[HTML][HTML] Combining general and personal models for epilepsy detection with hyperdimensional computing
Epilepsy is a highly prevalent chronic neurological disorder with great negative impact on
patients' daily lives. Despite this there is still no adequate technological support to enable …
patients' daily lives. Despite this there is still no adequate technological support to enable …
[PDF][PDF] Feature Selection with Deep Belief Network for Epileptic Seizure Detection on EEG Signals.
S Cherukuvada, R Kayalvizhi - Computers, Materials & Continua, 2023 - cdn.techscience.cn
The term Epilepsy refers to a most commonly occurring brain disorder after a migraine. Early
identification of incoming seizures significantly impacts the lives of people with Epilepsy …
identification of incoming seizures significantly impacts the lives of people with Epilepsy …
Optimizing electrode configurations for wearable EEG seizure detection using machine learning
H Gelbard-Sagiv, S Pardo, N Getter, M Guendelman… - Sensors, 2023 - mdpi.com
Epilepsy, a prevalent neurological disorder, profoundly affects patients' quality of life due to
the unpredictable nature of seizures. The development of a reliable and user-friendly …
the unpredictable nature of seizures. The development of a reliable and user-friendly …
Epilepsynet: Interpretable self-supervised seizure detection for low-power wearable systems
Epilepsy is one of the most common neurological disorders that is characterized by recurrent
and unpredictable seizures. Wearable systems can be used to detect the onset of a seizure …
and unpredictable seizures. Wearable systems can be used to detect the onset of a seizure …
Learning Robust Representations of Tonic-Clonic Seizures with Cyclic Transformer
J Zhang, L Swinnen, C Chatzichristos… - IEEE Journal of …, 2024 - ieeexplore.ieee.org
Tonic-clonic seizures (TCSs) pose a significant risk for sudden unexpected death in epilepsy
(SUDEP). Previous research has highlighted the potential of multimodal wearable seizure …
(SUDEP). Previous research has highlighted the potential of multimodal wearable seizure …
Combining General and Personalized Models for Epilepsy Detection with Hyperdimensional Computing
Epilepsy is a chronic neurological disorder with a significant prevalence. However, there is
still no adequate technological support to enable epilepsy detection and continuous …
still no adequate technological support to enable epilepsy detection and continuous …