Incorporating structural information from the multichannel EEG improves patient-specific seizure detection.

Objective: A novel patient-specific seizure detection algorithm is presented. As the spatial distribution of the ictal pattern is characteristic for a patient's seizures, this work incorporates such information into the data representation and provides a learning algorithm exploiting it.

Methods: The proposed training algorithm uses nuclear norm regularization to convey structural information of the channel-feature matrices extracted from the EEG.

A kernel-based framework to tensorial data analysis.

Tensor-based techniques for learning allow one to exploit the structure of carefully chosen representations of data. This is a desirable feature in particular when the number of training patterns is small which is often the case in areas such as biosignal processing and chemometrics. However, the class of tensor-based models is somewhat restricted and might suffer from limited discriminative power.

Improved microarray-based decision support with graph encoded interactome data.

In the past, microarray studies have been criticized due to noise and the limited overlap between gene signatures. Prior biological knowledge should therefore be incorporated as side information in models based on gene expression data to improve the accuracy of diagnosis and prognosis in cancer. As prior knowledge, we investigated interaction and pathway information from the human interactome on different aspects of biological systems.