PSGLab Matlab toolbox for polysomnographic data processing: Development and practical application

In this paper, we present a Matlab toolbox for processing of multichannel biomedical data, especially polysomnographic signals. It implements signal preprocessing, feature extraction, supervised and unsupervised classification methods and high-level data visualization techniques. These methods allow finding important information hidden in biomedical signals and its suitable interpretation. We investigated the possibility of applying the combination of segmentation, various data representation methods, clustering and classification to the field of sleep, comatose and neonatal EEG. All datasets were provided by cooperating medical partners. The most important step in the whole process is feature extraction and feature selection. In this process we used visualization as an additional tool that helped us to decide which features to select. Proper selection of features may significantly influence the success rate of the classification.