Seizures

A fast machine learning approach to facilitate the detection of interictal epileptiform discharges in the scalp electroencephalogram.

BACKGROUND: Finding interictal epileptiform discharges (IEDs) in the EEG is a part of diagnosing epilepsy. Automated software for annotating EEGs of patients with suspected epilepsy can therefore help with reaching a diagnosis. A large amount of data is required for training and evaluating an effective IED detection system. IEDs occur infrequently in the most patients' EEG, therefore, interictal E...

The "MS-ROM/IFAST" Model, a Novel Parallel Nonlinear EEG Analysis Technique, Distinguishes ASD Subjects From Children Affected With Other Neuropsychiatric Disorders With High Degree of Accuracy.

. In a previous study, we showed a new EEG processing methodology called Multi-Scale Ranked Organizing Map/Implicit Function As Squashing Time (MS-ROM/IFAST) performing an almost perfect distinction between computerized EEG of Italian children with autism spectrum disorder (ASD) and typically developing children. In this study, we assessed this system in distinguishing ASD subjects from children a...

Prediction of epileptic seizures with convolutional neural networks and functional near-infrared spectroscopy signals.

There have been different efforts to predict epileptic seizures and most of them are based on the analysis of electroencephalography (EEG) signals; ho...

Identifying signal-dependent information about the preictal state: A comparison across ECoG, EEG and EKG using deep learning.

BACKGROUND: The inability to reliably assess seizure risk is a major burden for epilepsy patients and prevents developing better treatments. Recent ad...

Automated spectrographic seizure detection using convolutional neural networks.

PURPOSE: Non-convulsive seizures are common in critically ill patients, and delays in diagnosis contribute to increased morbidity and mortality. Many ...

Exploring Douglas-Peucker Algorithm in the Detection of Epileptic Seizure from Multicategory EEG Signals.

Discovering the concealed patterns of Electroencephalogram (EEG) signals is a crucial part in efficient detection of epileptic seizures. This study de...

Bypassing the volume conduction effect by multilayer neural network for effective connectivity estimation.

Differentiation of real interactions between different brain regions from spurious ones has been a challenge in neuroimaging researches. While using e...

Learning-based classification of valence emotion from electroencephalography.

The neuroimaging research field has been revolutionized with the development of human cognitive functions without the use of brain pathways. To assist...

Comparison of machine learning models for seizure prediction in hospitalized patients.

OBJECTIVE: To compare machine learning methods for predicting inpatient seizures risk and determine the feasibility of 1-h screening EEG to identify l...

Dynamics reconstruction and classification via Koopman features.

Knowledge discovery and information extraction of large and complex datasets has attracted great attention in wide-ranging areas from statistics and b...

Brain wave classification using long short-term memory network based OPTICAL predictor.

Brain-computer interface (BCI) systems having the ability to classify brain waves with greater accuracy are highly desirable. To this end, a number of...

Deep convolutional neural network for classification of sleep stages from single-channel EEG signals.

Using a smart method for automatic diagnosis in medical applications, such as sleep stage classification is considered as one of the important challen...

Design and Implementation of a Machine Learning Based EEG Processor for Accurate Estimation of Depth of Anesthesia.

Accurate monitoring of the depth of anesthesia (DoA) is essential for intraoperative and postoperative patient's health. Commercially available electr...

Autoencoding of long-term scalp electroencephalogram to detect epileptic seizure for diagnosis support system.

INTRODUCTION: Epileptologists could benefit from a diagnosis support system that automatically detects seizures because visual inspection of long-term...

Effects of Transcranial Direct Current Stimulation (tDCS) Combined With Wrist Robot-Assisted Rehabilitation on Motor Recovery in Subacute Stroke Patients: A Randomized Controlled Trial.

Both transcranial direct current stimulation (tDCS) and wrist robot-assisted training have demonstrated to be promising approaches for stroke rehabili...

Arrangements of Resting State Electroencephalography as the Input to Convolutional Neural Network for Biometric Identification.

Biometric is an important field that enables identification of an individual to access their sensitive information and asset. In recent years, electro...

Use of Multiple EEG Features and Artificial Neural Network to Monitor the Depth of Anesthesia.

The electroencephalogram (EEG) can reflect brain activity and contains abundant information of different anesthetic states of the brain. It has been w...

Prediction of rTMS treatment response in major depressive disorder using machine learning techniques and nonlinear features of EEG signal.

BACKGROUND: Prediction of therapeutic outcome of repetitive transcranial magnetic stimulation (rTMS) treatment is an important purpose that eliminates...

Class discrepancy-guided sub-band filter-based common spatial pattern for motor imagery classification.

BACKGROUND: Motor imagery classification, an important branch of brain-computer interface (BCI), recognizes the intention of subjects to control exter...

Performance evaluation of DWT based sigmoid entropy in time and frequency domains for automated detection of epileptic seizures using SVM classifier.

The electroencephalogram (EEG) signal contains useful information on physiological states of the brain and has proven to be a potential biomarker to r...