Seizures

Inference of Brain States Under Anesthesia With Meta Learning Based Deep Learning Models.

Monitoring the depth of unconsciousness during anesthesia is beneficial in both clinical settings and neuroscience investigations to understand brain mechanisms. Electroencephalogram (EEG) has been used as an objective means of characterizing brain altered arousal and/or cognition states induced by anesthetics in real-time. Different general anesthetics affect cerebral electrical activities in dif...

Detection of schizophrenia using hybrid of deep learning and brain effective connectivity image from electroencephalogram signal.

Detection of mental disorders such as schizophrenia (SZ) through investigating brain activities recorded via Electroencephalogram (EEG) signals is a promising field in neuroscience. This study presents a hybrid brain effective connectivity and deep learning framework for SZ detection on multichannel EEG signals. First, the effective connectivity matrix is measured based on the Transfer Entropy (TE...

Stress Classification Using Brain Signals Based on LSTM Network.

The early diagnosis of stress symptoms is essential for preventing various mental disorder such as depression. Electroencephalography (EEG) signals ar...

Task-State EEG Signal Classification for Spatial Cognitive Evaluation Based on Multiscale High-Density Convolutional Neural Network.

In this study, a multi-scale high-density convolutional neural network (MHCNN) classification method for spatial cognitive ability assessment was prop...

Predicting Neurological Outcome From Electroencephalogram Dynamics in Comatose Patients After Cardiac Arrest With Deep Learning.

OBJECTIVE: Most cardiac arrest patients who are successfully resuscitated are initially comatose due to hypoxic-ischemic brain injury. Quantitative el...

SCC-MPGCN: self-attention coherence clustering based on multi-pooling graph convolutional network for EEG emotion recognition.

The emotion recognition with electroencephalography (EEG) has been widely studied using the deep learning methods, but the topology of EEG channels is...

Key Feature Extraction Method of Electroencephalogram Signal by Independent Component Analysis for Athlete Selection and Training.

Emotion is an important expression generated by human beings to external stimuli in the process of interaction with the external environment. It affec...

Time-Frequency Analysis of Scalp EEG With Hilbert-Huang Transform and Deep Learning.

Electroencephalography (EEG) is a brain imaging approach that has been widely used in neuroscience and clinical settings. The conventional EEG analyse...

Deep Learning-Based Approach for Emotion Recognition Using Electroencephalography (EEG) Signals Using Bi-Directional Long Short-Term Memory (Bi-LSTM).

Emotions are an essential part of daily human communication. The emotional states and dynamics of the brain can be linked by electroencephalography (E...

Psychosocial Factors and Psychological Characteristics of Personality of Patients with Chronic Diseases Using Artificial Intelligence Data Mining Technology and Wireless Network Cloud Service Platform.

It was to explore the application value of health cloud service platform based on data mining algorithm and wireless network in the analysis of psycho...

Danger, high voltage! Using EEG and EOG measurements for cognitive overload detection in a simulated industrial context.

Industrial settings will be characterized by far-reaching production automation brought about by advancements in robotics and artificial intelligence....

Deep Learning Enabled Diagnosis of Children's ADHD Based on the Big Data of Video Screen Long-Range EEG.

Attention-deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children. At the same time, ADHD is prone to coexist with o...

Deep Learning Methods for Multi-Channel EEG-Based Emotion Recognition.

Currently, Fourier-based, wavelet-based, and Hilbert-based time-frequency techniques have generated considerable interest in classification studies fo...

Deep learning resting state functional magnetic resonance imaging lateralization of temporal lobe epilepsy.

OBJECTIVE: Localization of focal epilepsy is critical for surgical treatment of refractory seizures. There remains a great need for noninvasive techni...

Engineering nonlinear epileptic biomarkers using deep learning and Benford's law.

In this study, we designed two deep neural networks to encode 16 features for early seizure detection in intracranial EEG and compared them and their ...

EEG Feature Extraction and Data Augmentation in Emotion Recognition.

Emotion recognition is a challenging problem in Brain-Computer Interaction (BCI). Electroencephalogram (EEG) gives unique information about brain acti...

Artificial Neural Network vs. Pharmacometric Model for Population Prediction of Plasma Concentration in Real-World Data: A Case Study on Valproic Acid.

We compared the predictive performance of an artificial neural network to traditional pharmacometric modeling for population prediction of plasma conc...

Both Cross-Patient and Patient-Specific Seizure Detection Based on Self-Organizing Fuzzy Logic.

Automatic epilepsy detection is of great significance for the diagnosis and treatment of patients. Most detection methods are based on patient-specifi...

Neuronal Apoptosis in Patients with Liver Cirrhosis and Neuronal Epileptiform Discharge Model Based upon Multi-Modal Fusion Deep Learning.

Neurons refer to nerve cells. Each neuron is connected with thousands of other neurons to form a corresponding functional area and carry out complex c...

Prediction Value of Epilepsy Secondary to Inferior Cavity Hemorrhage Based on Scalp EEG Wave Pattern in Deep Learning.

OBJECTIVE: To search the predictive value of epilepsy secondary to acute subarachnoid hemorrhage (aSAH) based on EEG wave pattern in deep learning.