Seizures

Assessment of obstructive sleep apnea-related sleep fragmentation utilizing deep learning-based sleep staging from photoplethysmography.

STUDY OBJECTIVES: To assess the relationship between obstructive sleep apnea (OSA) severity and sleep fragmentation, accurate differentiation between sleep and wakefulness is needed. Sleep staging is usually performed manually using electroencephalography (EEG). This is time-consuming due to complexity of EEG setup and the amount of work in manual scoring. In this study, we aimed to develop an aut...

Towards Interpretable Machine Learning in EEG Analysis.

In this paper a machine learning model for automatic detection of abnormalities in electroencephalography (EEG) is dissected into parts, so that the influence of each part on the classification accuracy score can be examined. The most successful setup of several shallow artificial neural networks aggregated via voting results in accuracy of 81%. Stepwise simplification of the model shows the expec...

An end-to-end heterogeneous graph representation learning-based framework for drug-target interaction prediction.

Accurately identifying potential drug-target interactions (DTIs) is a key step in drug discovery. Although many related experimental studies have been...

Application of bi-modal signal in the classification and recognition of drug addiction degree based on machine learning.

Most studies on drug addiction degree are made based on statistical scales, addicts' account, and subjective judgement of rehabilitation doctors. No o...

Histograms of Frequency-Intensity Distribution Deep Learning to Predict the Seizure Liability of Drugs in Electroencephalography.

Detection of seizures as well as that of seizure auras is effective in improving the predictive accuracy of seizure liability of drugs. Whereas electr...

[Using electroencephalogram for emotion recognition based on filter-bank long short-term memory networks].

Emotion plays an important role in people's cognition and communication. By analyzing electroencephalogram (EEG) signals to identify internal emotions...

[Prediction of epilepsy based on common spatial model algorithm and support vector machine double classification].

At present the prediction method of epilepsy patients is very time-consuming and vulnerable to subjective factors, so this paper presented an automati...

The Human Phenotype Ontology in 2021.

The Human Phenotype Ontology (HPO, https://hpo.jax.org) was launched in 2008 to provide a comprehensive logical standard to describe and computational...

A Review of Automated Techniques for Assisting the Early Detection of Alzheimer's Disease with a Focus on EEG.

In this paper, we review state-of-the-art approaches that apply signal processing (SP) and machine learning (ML) to automate the detection of Alzheime...

A deep learning-based algorithm for detection of cortical arousal during sleep.

STUDY OBJECTIVES: The frequency of cortical arousals is an indicator of sleep quality. Additionally, cortical arousals are used to identify hypopneic ...

Voxel-based morphometry analysis and machine learning based classification in pediatric mesial temporal lobe epilepsy with hippocampal sclerosis.

Mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE-HS) is a common type of pediatric epilepsy. We sought to evaluate whether the combinati...

The human brain reveals resting state activity patterns that are predictive of biases in attitudes toward robots.

The increasing presence of robots in society necessitates a deeper understanding into what attitudes people have toward robots. People may treat robot...

[Simultaneous determination of twelve antiepileptic drugs in serum by ultra high performance liquid chromatography-tandem mass spectrometry].

A sensitive, high-throughput method was established for the simultaneous determination of 12 antiepileptics in serum by ultra high performance liquid ...

Automated human mind reading using EEG signals for seizure detection.

Epilepsy is one of the most occurring neurological disease globally emerged back in 4000 BC. It is affecting around 50 million people of all ages thes...

Partial directed coherence based graph convolutional neural networks for driving fatigue detection.

The mental state of a driver can be accurately and reliably evaluated by detecting the driver's electroencephalogram (EEG) signals. However, tradition...

Automatic Identification of Brain Independent Components in Electroencephalography Data Collected while Standing in a Virtually Immersive Environment - A Deep Learning-Based Approach.

Electroencephalography (EEG) is a commonly used method for monitoring brain activity. Automating an EEG signal processing pipeline is imperative to th...

Deep transfer learning for improving single-EEG arousal detection.

Datasets in sleep science present challenges for machine learning algorithms due to differences in recording setups across clinics. We investigate two...

Emotion Recognition with Refined Labels for Deep Learning.

The traditional emotion classification framework usually fits all the features segments of the same trial to a fixed annotation. Considering the fact ...

Machine Learning with Imbalanced EEG Datasets using Outlier-based Sampling.

Epilepsy is a neurological disorder which causes seizures in over 65 million people worldwide. Recently developed implantable therapeutic devices aim ...

EEG-based Depression Detection Using Convolutional Neural Network with Demographic Attention Mechanism.

Electroencephalography (EEG)-based depression detection has become a hot topic in the development of biomedical engineering. However, the complexity a...