A single-trial toolbox for advanced sleep polysomnographic preprocessing

The application of polysomnographic (PSG) studies for monitoring sleep activity is a multi-parametric practice that involves a diverse group of biological signals. A suitable preprocessing of such signals assures a more profitable feature extraction and classification operations. Therefore, the proposed preprocessing toolbox performs segmentation, filtering, denoising, whitening and artefact removal tasks upon multi-channel PSG recordings. In order to assess toolbox´s efficiency, clinical experiments are conducted, as well as, quantitative and qualitative metrics are discussed. Our findings reveal outperforming efficiency by artefacts and noise rejection after single-trial and multi-stage preprocessing.