Seizures

DCSENets: Interpretable deep learning for patient-independent seizure classification using enhanced EEG-based spectrogram visualization.

Neurologists often face challenges in identifying epileptic activities within multichannel EEG recordings, requiring extensive hours of analysis. Computer-aided diagnosis systems have been proposed to reduce manual inspection of EEG signals by neurologists. However, direct analysis of EEG signals is difficult due to their complex and dynamic nature, with variation across multiple patients. Therefo...

Neuropathology of focal epilepsy: the promise of artificial intelligence and digital Neuropathology 3.0.

Focal lesions of the human neocortex often cause drug-resistant epilepsy, yet ​surgical resection of the epileptogenic region has been proven as a successful strategy to control seizures in a carefully selected patient cohort. Continuous efforts to study neurosurgically resected brain samples at the microscopic level, i.e., Neuropathology 1.0, unravelled a comprehensive description of the spectrum...

Enhancing Deep-Learning Classification for Remote Motor Imagery Rehabilitation Using Multi-Subject Transfer Learning in IoT Environment.

One of the most promising applications for electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is motor rehabilitation through motor ima...

Annotated interictal discharges in intracranial EEG sleep data and related machine learning detection scheme.

Interictal epileptiform discharges (IEDs) such as spikes and sharp waves represent pathological electrophysiological activities occurring in epilepsy ...

Monitoring of the trough concentration of valproic acid in pediatric epilepsy patients: a machine learning-based ensemble model.

AIMS: Few personalized monitoring models for valproic acid (VPA) in pediatric epilepsy patients (PEPs) incorporate machine learning (ML) algorithms. T...

Improving the Performance of Electrotactile Brain-Computer Interface Using Machine Learning Methods on Multi-Channel Features of Somatosensory Event-Related Potentials.

Traditional tactile brain-computer interfaces (BCIs), particularly those based on steady-state somatosensory-evoked potentials, face challenges such a...

EEG channel and feature investigation in binary and multiple motor imagery task predictions.

INTRODUCTION: Motor Imagery (MI) Electroencephalography (EEG) signals are non-stationary and dynamic physiological signals which have low signal-to-no...

Identification of autism spectrum disorder using electroencephalography and machine learning: a review.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by communication barriers, societal disengagement, and monotonous actio...

Detecting fast-ripples on both micro- and macro-electrodes in epilepsy: A wavelet-based CNN detector.

BACKGROUND: Fast-ripples (FR) are short (∼10 ms) high-frequency oscillations (HFO) between 200 and 600 Hz that are helpful in epilepsy to identify the...

How accurate are machine learning models in predicting anti-seizure medication responses: A systematic review.

IMPORTANCE: Current epilepsy management protocols often depend on anti-seizure medication (ASM) trials and assessment of clinical response. This may d...

Enhancing motor imagery EEG signal decoding through machine learning: A systematic review of recent progress.

This systematic literature review explores the intersection of neuroscience and deep learning in the context of decoding motor imagery Electroencephal...

A novel ECG-based approach for classifying psychiatric disorders: Leveraging wavelet scattering networks.

Individuals with neuropsychiatric disorders experience both physical and mental difficulties, hindering their ability to live healthy lives and partic...

Humanity Test-EEG Data Mediated Artificial Intelligence Multi-Person Interactive System.

Artificial intelligence (AI) systems are widely applied in various industries and everyday life, particularly in fields such as virtual assistants, he...

Digital Twin for EEG seizure prediction using time reassigned Multisynchrosqueezing transform-based CNN-BiLSTM-Attention mechanism model.

The prediction of epileptic seizures is a classical research problem, representing one of the most challenging tasks in the analysis of brain disorder...

Accuracy of Machine Learning in Detecting Pediatric Epileptic Seizures: Systematic Review and Meta-Analysis.

BACKGROUND: Real-time monitoring of pediatric epileptic seizures poses a significant challenge in clinical practice. In recent years, machine learning...

Unlocking Security for Comprehensive Electroencephalogram-Based User Authentication Systems.

With recent significant advancements in artificial intelligence, the necessity for more reliable recognition systems has rapidly increased to safeguar...

Enhancing automatic sleep stage classification with cerebellar EEG and machine learning techniques.

Sleep disorders have become a significant health concern in modern society. To investigate and diagnose sleep disorders, sleep analysis has emerged as...

3D convolutional neural network based on spatial-spectral feature pictures learning for decoding motor imagery EEG signal.

Non-invasive brain-computer interfaces (BCI) hold great promise in the field of neurorehabilitation. They are easy to use and do not require surgery, ...

Real-Time Postural Disturbance Detection Through Sensor Fusion of EEG and Motion Data Using Machine Learning.

Millions of people around the globe are impacted by falls annually, making it a significant public health concern. Falls are particularly challenging ...

CareSleepNet: A Hybrid Deep Learning Network for Automatic Sleep Staging.

Sleep staging is essential for sleep assessment and plays an important role in disease diagnosis, which refers to the classification of sleep epochs i...