Mathematical biosciences and engineering : MBE
39949163
Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identification for optimal care. Nevertheless, the intricate characteristics of electroencephalography (EEG) signals, noise, and the want for real-time analysis re...
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
40031444
Closed-loop electricalstimulation of brain structures is one of the most promising techniques to suppress epileptic seizures in drug-resistant refractory patients who are also ineligible to ablative neurosurgery. In this work, an intelligent controll...
BACKGROUND AND OBJECTIVES: Epilepsy is a neurological disorder characterized by recurrent epileptic seizures, whose neurophysiological signature is altered electroencephalographic (EEG) activity. The use of artificial intelligence (AI) methods on EEG...
INTRODUCTION: To investigate the potential of using artificial intelligence (AI), specifically large language models (LLMs), for synthesizing information in a simulated randomized clinical trial (RCT) for an anti-seizure medication, cenobamate, demon...
Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
39987762
BACKGROUND: To (a) evaluate the effect of a machine learning algorithm in the identification of patients suitable for epilepsy surgery evaluation, and (b) examine the performance of a large language model (LLM) in the collation of key pieces of infor...
In order to detect epileptic spikes, this paper suggests a deep learning architecture that blends 1D residual convolutional neural networks (1D-ResCNN) with a hybrid optimization strategy. The Layer-wise Adaptive Moments (LAMB) and AdamW algorithms h...
EEG is the most powerful tool for epilepsy discharge detection in brain. Visual evaluation is hard in long term monitoring EEG data as huge amount of data needs to be inspected. Considering the fast and efficient results from deep learning networks e...
IEEE journal of biomedical and health informatics
40030550
Despite numerous studies aimed at improving accuracy, the accurate prediction of epileptic seizures remains a challenge in clinical practice due to the high computational cost, poor real-time performance, and over-reliance on labelled data. To addres...
IEEE transactions on biomedical circuits and systems
40030347
This work presents the development of on-chip machine learning (ML) classifiers for implantable neuromodulation system-on-chips (SoCs), aimed at detecting epileptic seizures for closed-loop neuromodulation applications. Tree-based classifiers have ga...
IEEE journal of biomedical and health informatics
40031739
Magnetoencephalography (MEG) is a vital non-invasive tool for epilepsy analysis, as it captures high-resolution signals that reflect changes in brain activity over time. The automated detection of epileptic spikes within these signals can significant...