Seizures

Automated detection of sudden unexpected death in epilepsy risk factors in electronic medical records using natural language processing.

OBJECTIVE: Sudden unexpected death in epilepsy (SUDEP) is an important cause of mortality in epilepsy. However, there is a gap in how often providers counsel patients about SUDEP. One potential solution is to electronically prompt clinicians to provide counseling via automated detection of risk factors in electronic medical records (EMRs). We evaluated (1) the feasibility and generalizability of u...

Prediction of antiepileptic drug treatment outcomes of patients with newly diagnosed epilepsy by machine learning.

OBJECTIVE: The objective of this study was to build a supervised machine learning-based classifier, which can accurately predict the outcomes of antiepileptic drug (AED) treatment of patients with newly diagnosed epilepsy.

Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia.

Early defibrillation by an automated external defibrillator (AED) is key for the survival of out-of-hospital cardiac arrest (OHCA) patients. ECG featu...

A Unified Novel Neural Network Approach and a Prototype Hardware Implementation for Ultra-Low Power EEG Classification.

This paper introduces a novel electroencephalogram (EEG) data classification scheme together with its implementation in hardware using an innovative a...

Emotion Recognition from Multiband EEG Signals Using CapsNet.

Emotion recognition based on multi-channel electroencephalograph (EEG) signals is becoming increasingly attractive. However, the conventional methods ...

A Channel-Projection Mixed-Scale Convolutional Neural Network for Motor Imagery EEG Decoding.

Motor imagery electroencephalography (EEG) decoding is an essential part of brain-computer interfaces (BCIs) which help motor-disabled patients to com...

SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach.

Electroencephalogram (EEG) is a common base signal used to monitor brain activities and diagnose sleep disorders. Manual sleep stage scoring is a time...

Localizing epileptogenic regions using high-frequency oscillations and machine learning.

Pathological high frequency oscillations (HFOs) are putative neurophysiological biomarkers of epileptogenic brain tissue. Utilizing HFOs for epilepsy ...

Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders.

To estimate the reliability and cognitive states of operator performance in a human-machine collaborative environment, we propose a novel human mental...

Spectral and Temporal Feature Learning With Two-Stream Neural Networks for Mental Workload Assessment.

People's mental workload profoundly affects their work efficiency and health. Mental workload assessment can be used to effectively avoid serious acci...

An unsupervised EEG decoding system for human emotion recognition.

Emotion plays a vital role in human health and many aspects of life, including relationships, behaviors and decision-making. An intelligent emotion re...

Multiclass Support Matrix Machines by Maximizing the Inter-Class Margin for Single Trial EEG Classification.

Accurate classification of Electroencephalogram (EEG) signals plays an important role in diagnoses of different type of mental activities. One of the ...

Classification of epileptic EEG recordings using signal transforms and convolutional neural networks.

This paper describes the analysis of a deep neural network for the classification of epileptic EEG signals. The deep learning architecture is made up ...

Making Sense of Spatio-Temporal Preserving Representations for EEG-Based Human Intention Recognition.

Brain-computer interface (BCI) is a system empowering humans to communicate with or control the outside world with exclusively brain intentions. Elect...

Spiking Neural Network Modelling Approach Reveals How Mindfulness Training Rewires the Brain.

There has been substantial interest in Mindfulness Training (MT) to understand how it can benefit healthy individuals as well as people with a broad r...

EIQ: EEG based IQ test using wavelet packet transform and hierarchical extreme learning machine.

BACKGROUND: The use of electroencephalography has been perpetually incrementing and has numerous applications such as clinical and psychiatric studies...

SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species.

Understanding sleep and its perturbation by environment, mutation, or medication remains a central problem in biomedical research. Its examination in ...

Deep-learning for seizure forecasting in canines with epilepsy.

OBJECTIVE: This paper introduces a fully automated, subject-specific deep-learning convolutional neural network (CNN) system for forecasting seizures ...

On the Vulnerability of CNN Classifiers in EEG-Based BCIs.

Deep learning has been successfully used in numerous applications because of its outstanding performance and the ability to avoid manual feature engin...

DOSED: A deep learning approach to detect multiple sleep micro-events in EEG signal.

BACKGROUND: Electroencephalography (EEG) monitors brain activity during sleep and is used to identify sleep disorders. In sleep medicine, clinicians i...