Neurology

Seizures

Latest AI and machine learning research in seizures for healthcare professionals.

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Unlocking new frontiers in epilepsy through AI: From seizure prediction to personalized medicine.

Artificial intelligence (AI) is revolutionizing epilepsy care by advancing seizure detection, enhanc...

An Efficient Approach for Detection of Various Epileptic Waves Having Diverse Forms in Long Term EEG Based on Deep Learning.

EEG is the most powerful tool for epilepsy discharge detection in brain. Visual evaluation is hard i...

SeizyML: An Application for Semi-Automated Seizure Detection Using Interpretable Machine Learning Models.

Despite the vast number of publications reporting seizures and the reliance of the field on accurate...

A hybrid network based on multi-scale convolutional neural network and bidirectional gated recurrent unit for EEG denoising.

Electroencephalogram (EEG) signals are time series data containing abundant brain information. Howev...

Machine learning analysis of cortical activity in visual associative learning tasks with differing stimulus complexity.

Associative learning tests are cognitive assessments that evaluate the ability of individuals to lea...

Of Pilots and Copilots: The Evolving Role of Artificial Intelligence in Clinical Neurophysiology.

Artificial intelligence (AI) is revolutionizing clinical neurophysiology (CNP), particularly in its ...

Hybrid CNN-GRU Models for Improved EEG Motor Imagery Classification.

Brain-computer interfaces (BCIs) based on electroencephalography (EEG) enable neural activity interp...

Inductive reasoning with large language models: A simulated randomized controlled trial for epilepsy.

INTRODUCTION: To investigate the potential of using artificial intelligence (AI), specifically large...

Electroencephalogram (EEG) Based Fuzzy Logic and Spiking Neural Networks (FLSNN) for Advanced Multiple Neurological Disorder Diagnosis.

Neurological disorders are a major global health concern that have a substantial impact on death rat...

Epilepsy surgery candidate identification with artificial intelligence: An implementation study.

BACKGROUND: To (a) evaluate the effect of a machine learning algorithm in the identification of pati...

Unsupervised learning from EEG data for epilepsy: A systematic literature review.

BACKGROUND AND OBJECTIVES: Epilepsy is a neurological disorder characterized by recurrent epileptic ...

Machine learning based seizure classification and digital biosignal analysis of ECT seizures.

While artificial intelligence has received considerable attention in various medical fields, its app...

Integrating manual preprocessing with automated feature extraction for improved rodent seizure classification.

HYPOTHESIS/OBJECTIVE: Rodent models of epilepsy can help with the search for more effective drug can...

Geometric neural network based on phase space for BCI-EEG decoding.

The integration of Deep Learning (DL) algorithms on brain signal analysis is still in its nascent st...

A novel deep learning model combining 3DCNN-CapsNet and hierarchical attention mechanism for EEG emotion recognition.

Emotion recognition plays a key role in the field of human-computer interaction. Classifying and pre...

Machine learning classification of active viewing of pain and non-pain images using EEG does not exceed chance in external validation samples.

Previous research has demonstrated that machine learning (ML) could not effectively decode passive o...

Explaining electroencephalogram channel and subband sensitivity for alcoholism detection.

Alcoholism, a progressive loss of control over alcohol consumption, deteriorates mental and physical...

A systematic literature review of machine learning techniques for the detection of attention-deficit/hyperactivity disorder using MRI and/or EEG data.

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental condition common in teenager...

A hybrid optimization-enhanced 1D-ResCNN framework for epileptic spike detection in scalp EEG signals.

In order to detect epileptic spikes, this paper suggests a deep learning architecture that blends 1D...

Efficient Neural Network Classification of Parkinson's Disease and Schizophrenia Using Resting-State EEG Data.

Timely identification of Parkinson's disease and schizophrenia is crucial for the effective manageme...

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