The International Classification of Sleep Disorders lists 90 disorders. Manifestations, such as snoring, are important signs in the diagnosis of the Obstructive Sleep Apnea Syndrome; they are also socially undesirable.
Objective: The aim of this paper was to present and evaluate a computerized tool that automatically identifies snoring and highlights the importance of establishing the duration of each snoring event in OSA patients.
Material and methods: The low-sampling (200 Hz) electrical signal that indicates snoring was measured during polysomnography. The snoring sound of 31 patients was automatically classified by the software. The Kappa approach was applied to measure agreement between the automatic detection software and a trained observer. Student's T test was applied to evaluate differences in the duration of snoring episodes among simple snorers and OSA snorers.
Results: Of a total 43,976 snoring episodes, the software sensitivity was 99. 26%, the specificity was 97. 35%, and Kappa was 0. 96. We found a statistically significant difference (p <0. 0001) in the duration of snoring episodes (simple snoring x OSA snorers).
Conclusions: This computer software makes it easier to generate quantitative reports of snoring, thereby reducing manual labor.