Seizures

Time-Frequency Representation and Convolutional Neural Network-Based Emotion Recognition.

Emotions composed of cognizant logical reactions toward various situations. Such mental responses stem from physiological, cognitive, and behavioral changes. Electroencephalogram (EEG) signals provide a noninvasive and nonradioactive solution for emotion identification. Accurate and automatic classification of emotions can boost the development of human-computer interface. This article proposes au...

Artificial Intelligence Analysis of EEG Amplitude in Intensive Heart Care.

This article first studied the morphological characteristics of the EEG for intensive cardiac care; that is, based on the analysis of the mechanism of disease diagnosis and treatment, a signal processing and machine learning model was constructed. Then, the methods of signal preprocessing, signal feature extraction, new neural network model structure, training mechanism, optimization algorithm, an...

A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG.

Autism spectrum disorder (ASD) is a developmental disability characterized by persistent impairments in social interaction, speech and nonverbal commu...

Universum based Lagrangian twin bounded support vector machine to classify EEG signals.

BACKGROUND AND OBJECTIVE: The detection of brain-related problems and neurological disorders like epilepsy, sleep disorder, and so on is done by using...

Epileptic Seizure Detection on an Ultra-Low-Power Embedded RISC-V Processor Using a Convolutional Neural Network.

The treatment of refractory epilepsy via closed-loop implantable devices that act on seizures either by drug release or electrostimulation is a highly...

Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals.

The emergence of an aging society is inevitable due to the continued increases in life expectancy and decreases in birth rate. These social changes re...

Deep learning of resting-state electroencephalogram signals for three-class classification of Alzheimer's disease, mild cognitive impairment and healthy ageing.

This study aimed to produce a novel deep learning (DL) model for the classification of subjects with Alzheimer's disease (AD), mild cognitive impairme...

Benefits of deep learning classification of continuous noninvasive brain-computer interface control.

. Noninvasive brain-computer interfaces (BCIs) assist paralyzed patients by providing access to the world without requiring surgical intervention. Pri...

Design of MRI structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals.

This paper proposes a novel method and algorithms for the design of MRI structured personalized 3D spiking neural network models (MRI-SNN) for a bette...

Machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant EEG.

Currently there are no reliable means of identifying infants at-risk for later language disorders. Infant neural responses to rhythmic stimuli may off...

Depression Diagnosis Modeling With Advanced Computational Methods: Frequency-Domain eMVAR and Deep Learning.

Electroencephalogram (EEG)-based automated depression diagnosis systems have been suggested for early and accurate detection of mood disorders. EEG si...

Effects of spectral features of EEG signals recorded with different channels and recording statuses on ADHD classification with deep learning.

Early diagnosis of attention deficit and hyperactivity disorder (ADHD) by experts is difficult. Some solutions using electroencephalography (EEG) sign...

Epileptic Seizures Detection Using Deep Learning Techniques: A Review.

A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging...

Epileptic EEG Classification by Using Time-Frequency Images for Deep Learning.

Epilepsy is one of the most common brain disorders worldwide. The most frequently used clinical tool to detect epileptic events and monitor epilepsy p...

Expression-EEG Bimodal Fusion Emotion Recognition Method Based on Deep Learning.

As one of the key issues in the field of emotional computing, emotion recognition has rich application scenarios and important research value. However...

Delayed brain development of Rolandic epilepsy profiled by deep learning-based neuroanatomic imaging.

OBJECTIVES: Although Rolandic epilepsy (RE) has been regarded as a brain developmental disorder, neuroimaging studies have not yet ascertained whether...

A CNN identified by reinforcement learning-based optimization framework for EEG-based state evaluation.

Electroencephalogram (EEG) data, as a kind of complex time-series, is one of the most widely-used information measurements for evaluating human psycho...

EEG microstate features for schizophrenia classification.

Electroencephalography (EEG) microstate analysis is a method wherein spontaneous EEG activity is segmented at sub-second levels to analyze quasi-stabl...

Deep learning applied to electroencephalogram data in mental disorders: A systematic review.

In recent medical research, tremendous progress has been made in the application of deep learning (DL) techniques. This article systematically reviews...

Detection of k-complexes in EEG signals using a multi-domain feature extraction coupled with a least square support vector machine classifier.

Sleep scoring is one of the primary tasks for the classification of sleep stages using electroencephalogram (EEG) signals. It is one of the most impor...