We developed a machine learning methodology for automatic sleep stage scoring. Our time-frequency analysis-based feature extraction is fine-tuned to capture sleep stage-specific signal features as described in the American Academy of Sleep Medicine m...
PURPOSE: Due to the high recurrence risk of nonmuscle invasive urothelial carcinoma it is crucial to distinguish patients at high risk from those with indolent disease. In this study we used a machine learning algorithm to identify the genes in patie...
INTRODUCTION: We developed a machine learning model to predict whether or not a cochlear implant (CI) candidate will develop effective language skills within 2 years after the CI surgery by using the pre-implant brain fMRI data from the candidate.
The world journal of biological psychiatry : the official journal of the World Federation of Societies of Biological Psychiatry
Oct 9, 2015
OBJECTIVES: This study investigates whether abnormal neural oscillations, which have been shown to precede the onset of frank psychosis, could be used towards the individualised prediction of psychosis in clinical high-risk patients.
International journal of neural systems
Oct 9, 2015
Achieving recognizable visual event-related potentials plays an important role in improving the success rate in telepresence control of a humanoid robot via N200 or P300 potentials. The aim of this research is to intensively investigate ways to induc...
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