2 research outputs found
Deep Learning for scalp High Frequency Oscillations Identification
Since last 2 decades, High Frequency Oscillations (HFOs) are studied as a
promising biomarker to localize the epileptogenic zone of patients with
refractory focal epilepsy. As HFOs visual detection is time consuming and
subjective, automatization of HFO detection is required. Most HFO detectors
were developed on invasive electroencephalograms (iEEG) whereas scalp
electroencephalograms (EEG) are used in clinical routine. In order HFO
detection can benefit to more patients, scalp HFO detectors has to be
developed. However, HFOs identification in scalp EEG is more challenging than
in iEEG since scalp HFOs are of lower rate, lower amplitude and more likely to
be corrupted by several sources of artifacts than iEEG HFOs. The main goal of
this study is to explore the ability of deep learning architecture to identify
scalp HFOs from the remaining EEG signal. Hence, a binary classification
Convolutional Neural Network (CNN) is learned to analyze High Density
Electroencephalograms (HD-EEG). EEG signals are first mapped into a 2D
time-frequency image, several color definitions are then used as an input for
the CNN. Experimental results show that deep learning allows simple end-to-end
learning of preprocessing, feature extraction and classification modules while
reaching competitive performance
A Bibliographic View on Constrained Clustering
A keyword search on constrained clustering on Web-of-Science returned just
under 3,000 documents. We ran automatic analyses of those, and compiled our own
bibliography of 183 papers which we analysed in more detail based on their
topic and experimental study, if any. This paper presents general trends of the
area and its sub-topics by Pareto analysis, using citation count and year of
publication. We list available software and analyse the experimental sections
of our reference collection. We found a notable lack of large comparison
experiments. Among the topics we reviewed, applications studies were most
abundant recently, alongside deep learning, active learning and ensemble
learning.Comment: 18 pages, 11 figures, 177 reference