AIMC Topic: Epilepsy
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Epilepsy surgery candidate identification with artificial intelligence: An implementation study.
Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Feb 22, 2025
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...
Unsupervised learning from EEG data for epilepsy: A systematic literature review.
Artificial intelligence in medicine
Feb 21, 2025
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...
A hybrid optimization-enhanced 1D-ResCNN framework for epileptic spike detection in scalp EEG signals.
Scientific reports
Feb 17, 2025
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...
Low-Power and Low-Cost AI Processor With Distributed-Aggregated Classification Architecture for Wearable Epilepsy Seizure Detection.
IEEE transactions on biomedical circuits and systems
Feb 11, 2025
Wearable devices with continuous monitoring capabilities are critical for the daily detection of epileptic seizures, as they provide users with accurate and comprehensible analytical results. However, current AI classifiers rely on a two-stage recogn...
A multi-domain feature fusion epilepsy seizure detection method based on spike matching and PLV functional networks.
Journal of neural engineering
Feb 5, 2025
The identification of spikes, as a typical characteristic wave of epilepsy, is crucial for diagnosing and locating the epileptogenic region. The traditional seizure detection methods lack spike features and have low sample richness. This paper propos...
Deep learning based tractography with TractSeg in patients with hemispherotomy: Evaluation and refinement.
NeuroImage. Clinical
Jan 28, 2025
Deep learning-based tractography implicitly learns anatomical prior knowledge that is required to resolve ambiguities inherent in traditional streamline tractography. TractSeg is a particularly widely used example of such an approach. Even though it ...
Utilizing machine learning techniques for EEG assessment in the diagnosis of epileptic seizures in the brain: A systematic review and meta-analysis.
Seizure
Jan 27, 2025
PURPOSE: Advancements in Machine Learning (ML) techniques have revolutionized diagnosing and monitoring epileptic seizures using Electroencephalogram (EEG) signals. This analysis aims to determine the effectiveness of ML techniques in recognizing pat...
Prediction Trough Concentrations of Valproic Acid Among Chinese Adult Patients with Epilepsy Using Machine Learning Techniques.
Pharmaceutical research
Jan 22, 2025
OBJECTIVE: This study aimed to establish an optimal model based on machine learning (ML) to predict Valproic acid (VPA) trough concentrations in Chinese adult epilepsy patients.
Deep Clustering for Epileptic Seizure Detection.
IEEE transactions on bio-medical engineering
Jan 21, 2025
UNLABELLED: Epilepsy is a neurological disorder characterized by recurrent epileptic seizures, which are often unpredictable and increase mortality and morbidity risks.
Diagnosing Epilepsy with Normal Interictal EEG Using Dynamic Network Models.
Annals of neurology
Jan 16, 2025
OBJECTIVE: Whereas a scalp electroencephalogram (EEG) is important for diagnosing epilepsy, a single routine EEG is limited in its diagnostic value. Only a small percentage of routine EEGs show interictal epileptiform discharges (IEDs) and overall mi...
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