Seizures

Automatic detection of artifacts in EEG by combining deep learning and histogram contour processing.

This paper introduces a simple approach combining deep learning and histogram contour processing for automatic detection of various types of artifact contaminating the raw electroencephalogram (EEG). The proposed method considers both spatial and temporal information of raw EEG, without additional need for reference signals like ECG or EOG. The proposed method was evaluated with data including 785...

Automatic Sleep Stage Classification using Marginal Hilbert Spectrum Features and a Convolutional Neural Network.

In this paper, we propose a novel method of automatic sleep stage classification based on single-channel electroencephalography (EEG). First, we use marginal Hilbert spectrum (MHS) to depict time-frequency domain features of five sleep stages of 30-second (30s) EEG epochs. Second, the extracted MHSs features are input to a convolutional neural network (CNN) as multi-channel sequences for the sleep...

TinySleepNet: An Efficient Deep Learning Model for Sleep Stage Scoring based on Raw Single-Channel EEG.

Deep learning has become popular for automatic sleep stage scoring due to its capability to extract useful features from raw signals. Most of the exis...

Wavelet Spectral Deep-training of Convolutional Neural Networks for Accurate Identification of High-Frequency Micro-Scale Spike Transients in the Post-Hypoxic-Ischemic EEG of Preterm Sheep.

Early diagnosis and prognosis of babies with signs of hypoxic-ischemic encephalopathy (HIE) is currently limited and requires reliable prognostic biom...

Deep Convolutional Neural Network and Reverse Biorthogonal Wavelet Scalograms for Automatic Identification of High Frequency Micro-Scale Spike Transients in the Post-Hypoxic-Ischemic EEG.

Diagnosis of hypoxic-ischemic encephalopathy (HIE) is currently limited and prognostic biological markers are required for early identification of at ...

Wavelet Spectral Time-Frequency Training of Deep Convolutional Neural Networks for Accurate Identification of Micro-Scale Sharp Wave Biomarkers in the Post-Hypoxic-Ischemic EEG of Preterm Sheep.

Neonatal hypoxic-ischemic encephalopathy (HIE) evolves over different phases of time during recovery. Some neuroprotection treatments are only effecti...

Anxiety and Depression Diagnosis Method Based on Brain Networks and Convolutional Neural Networks.

At present, only professional doctors can use the professional scales to diagnose depression and anxiety in clinical practice. In recent years, the pr...

Patient-Specific Robot-Assisted Stroke Rehabilitation Guided by EEG - A Feasibility Study.

Multi-session robot-assisted stroke rehabilitation program requires patients to perform repetitive tasks. It is challenging for the patient to maintai...

Classifying cross-frequency coupling pattern in epileptogenic tissues by convolutional neural network.

The phase-amplitude coupling in EEG signal of different frequencies is considered as a useful biomarker in delineating epileptogenic tissues, but some...

Deep Learning for Interictal Epileptiform Spike Detection from scalp EEG frequency sub bands.

Epilepsy diagnosis through visual examination of interictal epileptiform discharges (IEDs) in scalp electroencephalogram (EEG) signals is a challengin...

Identifying tracé alternant activity in neonatal EEG using an inter-burst detection approach.

Electroencephalography (EEG) is an important clinical tool for reviewing sleep-wake cycling in neonates in intensive care. Tracé alternant (TA)-a char...

Porthole and Stormcloud: Tools for Visualisation of Spatiotemporal M/EEG Statistics.

Electro- and magneto-encephalography are functional neuroimaging modalities characterised by their ability to quantify dynamic spatiotemporal activity...

Predicting Deep Hypnotic State From Sleep Brain Rhythms Using Deep Learning: A Data-Repurposing Approach.

BACKGROUND: Brain monitors tracking quantitative brain activities from electroencephalogram (EEG) to predict hypnotic levels have been proposed as a l...

Robot-assisted stereoelectroencephalography exploration of the limbic thalamus in human focal epilepsy: implantation technique and complications in the first 24 patients.

OBJECTIVE: Despite numerous imaging studies highlighting the importance of the thalamus in a patient's surgical prognosis, human electrophysiological ...

Epileptic seizure detection: a comparative study between deep and traditional machine learning techniques.

Electroencephalography is the recording of brain electrical activities that can be used to diagnose brain seizure disorders. By identifying brain acti...

Robot-Assisted Insular Depth Electrode Implantation Through Oblique Trajectories: 3-Dimensional Anatomical Nuances, Technique, Accuracy, and Safety.

BACKGROUND: The insula is a deep cortical structure that has renewed interest in epilepsy investigation. Invasive EEG recordings of this region have b...

Motor imagery classification using geodesic filtering common spatial pattern and filter-bank feature weighted support vector machine.

In recent years, Brain Computer Interface (BCI) based on motor imagery has been widely used in the fields of medicine, active safe systems for automob...

Robotic treatment of the upper limb in chronic stroke and cerebral neuroplasticity: a systematic review.

Stroke is the second cause of mortality and the third cause of long-term disability worldwide. Deficits in upper limb (UL) capacity persist at 6 month...

Development of Expert-Level Automated Detection of Epileptiform Discharges During Electroencephalogram Interpretation.

IMPORTANCE: Interictal epileptiform discharges (IEDs) in electroencephalograms (EEGs) are a biomarker of epilepsy, seizure risk, and clinical decline....

Classification of Alzheimer's Disease with Respect to Physiological Aging with Innovative EEG Biomarkers in a Machine Learning Implementation.

BACKGROUND: Several studies investigated clinical and instrumental differences to make diagnosis of dementia in general and in Alzheimer's disease (AD...