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.
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