Marta Iovino
Automated LSTM-based sleep stage classification using polysomnographic signal processing techniques.
Rel. Filippo Molinari, Nicola Michielli. Politecnico di Torino, Master of science program in Biomedical Engineering, 2022
Abstract
Sleep is a fundamental human physiological activity. It is essential for physical and mental health and adsorbs a significant part of human life. Therefore, the sleep classification into different stages can help to detect disorders related to sleep and it is of great significance for sleep research. Sleep scoring is generally carried out by using polysomnographic (PSG) recordings following the rules of the American Academy of Sleep Medicine. Usually, the analysis of PSG signals is done by experienced doctors. It is carried out by looking at the characteristics of multiple signals (EEG, EOG and EMG) collected from the patient. The objective of this study is to develop a fully automatic method of classification, which replicates the work done by the experts.
The approach aims to save doctors time, but above all, it would reduce the intra- and inter-operator variability in stage classification
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