AIMC Topic: Scalp
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Artificial neural networks allow response prediction in squamous cell carcinoma of the scalp treated with radiotherapy.
Journal of the European Academy of Dermatology and Venereology : JEADV
Mar 4, 2020
BACKGROUND: Epithelial neoplasms of the scalp account for approximately 2% of all skin cancers and for about 10-20% of the tumours affecting the head and neck area. Radiotherapy is suggested for localized cutaneous squamous cell carcinomas (cSCC) wit...
Seizure Prediction in Scalp EEG Using 3D Convolutional Neural Networks With an Image-Based Approach.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sep 25, 2019
Epileptic seizures occur as a result of a process that develops over time and space in epileptic networks. In this study, we aim at developing a generalizable method for patient-specific seizure prediction by evaluating the spatio-temporal correlatio...
Autoencoding of long-term scalp electroencephalogram to detect epileptic seizure for diagnosis support system.
Computers in biology and medicine
Jun 5, 2019
INTRODUCTION: Epileptologists could benefit from a diagnosis support system that automatically detects seizures because visual inspection of long-term electroencephalograms (EEGs) is extremely time-consuming. However, the diversity of seizures among ...
Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images.
NeuroImage. Clinical
Jan 22, 2019
We hypothesized that expert epileptologists can detect seizures directly by visually analyzing EEG plot images, unlike automated methods that analyze spectro-temporal features or complex, non-stationary features of EEG signals. If so, seizure detecti...
High-Frequency Oscillations in the Scalp Electroencephalogram: Mission Impossible without Computational Intelligence.
Computational intelligence and neuroscience
Aug 7, 2018
High-frequency oscillations (HFOs) in the electroencephalogram (EEG) are thought to be a promising marker for epileptogenicity. A number of automated detection algorithms have been developed for reliable analysis of invasively recorded HFOs. However,...
A glimpse into the future of clinical neurophysiology: Can we use machines to interpret EEG?
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Jul 29, 2018
Machine Learning EEG to Predict Cognitive Functioning and Processing Speed Over a 2-Year Period in Multiple Sclerosis Patients and Controls.
Brain topography
Jan 29, 2018
Event-related potentials (ERPs) show promise to be objective indicators of cognitive functioning. The aim of the study was to examine if ERPs recorded during an oddball task would predict cognitive functioning and information processing speed in Mult...
Classification of Pre-Clinical Seizure States Using Scalp EEG Cross-Frequency Coupling Features.
IEEE transactions on bio-medical engineering
Jan 25, 2018
OBJECTIVE: This work proposes a machine-learning based system for a scalp EEG that flags an alarm in advance of a clinical seizure onset.
Focal Onset Seizure Prediction Using Convolutional Networks.
IEEE transactions on bio-medical engineering
Dec 25, 2017
OBJECTIVE: This paper investigates the hypothesis that focal seizures can be predicted using scalp electroencephalogram (EEG) data. Our first aim is to learn features that distinguish between the interictal and preictal regions. The second aim is to ...
A Fuzzy-Based Fusion Method of Multimodal Sensor-Based Measurements for the Quantitative Evaluation of Eye Fatigue on 3D Displays.
Sensors (Basel, Switzerland)
May 7, 2015
With the rapid increase of 3-dimensional (3D) content, considerable research related to the 3D human factor has been undertaken for quantitatively evaluating visual discomfort, including eye fatigue and dizziness, caused by viewing 3D content. Variou...
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