AIMC Topic: Electroencephalography

Clear Filters Showing 1981 to 1990 of 2150 articles

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

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Epilepsy diagnosis through visual examination of interictal epileptiform discharges (IEDs) in scalp electroencephalogram (EEG) signals is a challenging problem. Deep learning methods can be an automated way to perform this task. In this work, we pres...

Temporal dependency in automatic sleep scoring via deep learning based architectures: An empirical study.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The present study evaluates how effectively a deep learning based sleep scoring system does encode the temporal dependency from raw polysomnography signals. An exhaustive range of neural networks, including state of the art architecture, have been us...

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

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The phase-amplitude coupling in EEG signal of different frequencies is considered as a useful biomarker in delineating epileptogenic tissues, but some physiological processes can also generate phase-amplitude coupling pattern, such as memory process....

Four-Way Classification of EEG Responses To Virtual Robot Navigation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Studies have shown the possibility of using brain signals that are automatically generated while observing a navigation task as feedback for semi-autonomous control of a robot. This allows the robot to learn quasi-optimal routes to intended targets. ...

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

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Multi-session robot-assisted stroke rehabilitation program requires patients to perform repetitive tasks. It is challenging for the patient to maintain concentration during training sessions. A novel intervention strategy using Electroencephalography...

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

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
At present, only professional doctors can use the professional scales to diagnose depression and anxiety in clinical practice. In recent years, the problems of detecting the presence of anxiety or depression using Electroencephalography (EEG) has rec...

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.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Neonatal hypoxic-ischemic encephalopathy (HIE) evolves over different phases of time during recovery. Some neuroprotection treatments are only effective for specific, short windows of time during this evolution of injury. Clinically, we often do not ...

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.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Diagnosis of hypoxic-ischemic encephalopathy (HIE) is currently limited and prognostic biological markers are required for early identification of at risk infants at birth. Using pre-clinical data from our fetal sheep models, we have shown that micro...

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.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Early diagnosis and prognosis of babies with signs of hypoxic-ischemic encephalopathy (HIE) is currently limited and requires reliable prognostic biomarkers to identify at risk infants. Using our pre-clinical fetal sheep models, we have demonstrated ...

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

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Deep learning has become popular for automatic sleep stage scoring due to its capability to extract useful features from raw signals. Most of the existing models, however, have been overengineered to consist of many layers or have introduced addition...