[PDF] Top 20 Deep Learning for EEG Analysis in Epilepsy
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Deep Learning for EEG Analysis in Epilepsy
... channel EEG segments of ...5 epilepsy patients in seizure free intervals in the epileptogenetic zone (D) and hippocampal formation of the opposite side of the brain (C); set E contained ... See full document
144
Deep learning for identification of pathogenic genetic mutations
... the analysis of the results, is important to know in what conditions they have been obtained, ...points in each neighborhood are weighted ...previously in chapter 4, Naive Bayes uses the ... See full document
132
EEG spike source localization before and after surgery for temporal lobe epilepsy: a BOLD EEG-fMRI and independent component analysis study
... of EEG-functional magnetic resonance imaging (fMRI) combine the high temporal resolution of EEG with the distinctive spatial resolution of ...this EEG-fMRI study was to search for hemodynamic ... See full document
6
Spontaneous Slow Fluctuation of EEG Alpha Rhythm Reflects Activity in Deep-Brain Structures: A Simultaneous EEG-fMRI Study.
... regressor in the general linear model (GLM) for fMRI analysis to explore the brain regions whose activity specifically correlated with the original ...series analysis [25], such as APTS and BOLD ... See full document
12
Detection of abnormalities in ECG using Deep Learning
... detection in ECG has been ongoing for many years and, consequently, many approaches have been developed to tackle this chal- ...machine learning algorithms, scientists have considerations in terms of ... See full document
80
Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis
... primarily in order to overcome the shortcomings of visual analysis of EEG data such as time consumption while analyzing and disagreements among certain neurologists on ictal and inter-ictal ...Pandey ... See full document
3
Contributions on Deep Transfer Learning
... High-content Analysis has revolutionized cancer drug-discovery by identifying substances that alter the phenotype of a cell which prevent tumor growth and ...tumor. In this Chapter, we are particularly ... See full document
141
Deep Learning in Melanoma Detection
... machine learning workflow, from data management to hyperparameter training to de- ployment solutions" [ 12 ...with deep learning due to being user-friendly, having quality APIs and some ... See full document
77
A Live IDE for Deep Learning Architectures
... questionnaires. In order to make a statistical analysis of this data, four different statistical tests were taken into account in order to check if there exists a significant difference between the ... See full document
105
Modality effects in implicit artificial grammar learning: An EEG study
... frequency analysis, we considered only TEST1 (baseline) and we locked the EEG to the onset of each ...interested in capturing the activity related to the whole ...shorter in the auditory ... See full document
10
A Deep Learning Approach for Red Lesions Detection in Video Capsule Endoscopies
... 10. Spada, C., Hassan, C., Munoz-Navas, M., Neuhaus, H., Deviere, J., Fockens, P., Coron, E., Gay, G., Toth, E., Riccioni, M.E., Carretero, C., Charton, J.P., Van Gossum, A., Wientjes, C.A., Sacher-Huvelin, S., Delvaux, ... See full document
9
Uma abordagem deep learning para reconhecimento de expressões faciais.
... changes in facial muscles in response to the internal emotional state of a ...pervasive in various fields, such as education, entertainment, psychology, human-computer interaction, behavior ... See full document
82
Sleep Stage Classification: A Deep Learning Approach
... subjects’ EEG, EOG and EMG ...consists in defining the tree with its binary branching, ...cluster analysis method that seeks to build a hierarchy of ... See full document
187
Deep learning for multi-class skin lesion diagnosis
... Store in March ...fractal analysis). SkinVision in particular classifies lesions as either low, medium or high risk of skin cancer by using a risk assessment algorithm based on gray-scale images of ... See full document
144
Quantification of the TMS-EEG response in epilepsy
... moment, EEG interpretation in a clinical setting has been based on visual ...This analysis usually includes speculative formulation which serves as a guide for investigating the EEG signal and ... See full document
105
Deep Learning for genomic data analysis
... investments in research, genome sequencing technologies and techniques have been im- proving at a fast rate, resulting in a cheaper yet faster genome ...detailed analysis, which leads to advances ... See full document
101
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
... The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or ... See full document
16
Deep learning for cardiac MR images analysis
... structures. In the 90s ML approaches started to become popular, specifically the supervised techniques, ...described in terms of forces and its fields rather than energy functions like snakes ...developed ... See full document
80
Interictal Spike EEG Source Analysis in Hypothalamic Hamartoma Epilepsy
... spikes, in non-averaged raw EEG, with a peak in frontal (I), right (II) and left (III) temporal ...hemispheres in a dipolar distribution. Electrodes are represented in green; (c) ... See full document
9
Early EEG Predicts Poststroke Epilepsy
... first EEG periodic discharges were an inde- pendent predictor of epileptiform activity (interictal and/or ictal) during hospital stay, reinforcing the notion that peri- odic discharges are in the continuum ... See full document
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