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Snoring is a widely occurring problem in our society and it is highly associated with pathologies like Obstructive Sleep Apnea Syndrome (OSAS) being, usually, one of the first symptoms to appear. Economically, OSAS has a great impact since sleep disorders affect the daily performance of people in their professional activities. The extensive study of snoring evidences may be useful to improve the knowledge of associated pathologies, such as OSAS or others, at an early state. In this work, we study full night sound recordings of patients undergoing polysomnography (PSG) procedures. Recordings are offline processed to characterize time series of snoring events through the record length and correlated with the PSG data. The main goal of the proposed algorithms is to understand the behaviour of the full night sound recording and to identify snoring event patterns that may help and refine the diagnostics process. To achieve this goal, the relationship between the energy and the entropy was studied, for each respiratory event, in both snoring and non-snoring cases. Recordings are offline processed to characterize time series of snoring events through the record length and correlated with the PSG data. In the future, the relationship between these two physical variables can be used to predict the clinical evolution between a simple snorer patient and a patient with OSAS.

Snoring Analysis on Full Night Recordings based in the Energy and Entropy in PSG Basal Studies