AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Epilepsy

Showing 71 to 80 of 372 articles

Clear Filters

SMARTSeiz: Deep Learning With Attention Mechanism for Accurate Seizure Recognition in IoT Healthcare Devices.

IEEE journal of biomedical and health informatics
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, unpredictable attacks, affects individuals of all ages. IoT-based seizur...

Predicting efficacy of antiseizure medication treatment with machine learning algorithms in North Indian population.

Epilepsy research
PURPOSE: This study aimed to develop a classifier using supervised machine learning to effectively assess the impact of clinical, demographical, and biochemical factors in accurately predicting the antiseizure medications (ASMs) treatment response in...

Development and validation of an interpretable machine learning model for predicting post-stroke epilepsy.

Epilepsy research
BACKGROUND: Epilepsy is a serious complication after an ischemic stroke. Although two studies have developed prediction model for post-stroke epilepsy (PSE), their accuracy remains insufficient, and their applicability to different populations is unc...

Speech decoding from stereo-electroencephalography (sEEG) signals using advanced deep learning methods.

Journal of neural engineering
Brain-computer interfaces (BCIs) are technologies that bypass damaged or disrupted neural pathways and directly decode brain signals to perform intended actions. BCIs for speech have the potential to restore communication by decoding the intended spe...

Development and validation of an automatic machine learning model to predict abnormal increase of transaminase in valproic acid-treated epilepsy.

Archives of toxicology
Valproic acid (VPA) is a primary medication for epilepsy, yet its hepatotoxicity consistently raises concerns among individuals. This study aims to establish an automated machine learning (autoML) model for forecasting the risk of abnormal increase o...

Epilepsy detection based on multi-head self-attention mechanism.

PloS one
CNN has demonstrated remarkable performance in EEG signal detection, yet it still faces limitations in terms of global perception. Additionally, due to individual differences in EEG signals, the generalization ability of epilepsy detection models is ...

Automatic detection of epilepsy from EEGs using a temporal convolutional network with a self-attention layer.

Biomedical engineering online
BACKGROUND: Over 60% of epilepsy patients globally are children, whose early diagnosis and treatment are critical for their development and can substantially reduce the disease's burden on both families and society. Numerous algorithms for automated ...

A computational clinical decision-supporting system to suggest effective anti-epileptic drugs for pediatric epilepsy patients based on deep learning models using patient's medical history.

BMC medical informatics and decision making
BACKGROUND: Epilepsy, a chronic brain disorder characterized by abnormal brain activity that causes seizures and other symptoms, is typically treated using anti-epileptic drugs (AEDs) as the first-line therapy. However, due to the variations in their...

Exploring the potential of pretrained CNNs and time-frequency methods for accurate epileptic EEG classification: a comparative study.

Biomedical physics & engineering express
Prompt diagnosis of epilepsy relies on accurate classification of automated electroencephalogram (EEG) signals. Several approaches have been developed to characterize epileptic EEG data; however, none of them have exploited time-frequency data to eva...