A new portable device for the snore/non-snore classification

Snoring is widely known as a disease. The aim of this paper is to introduce and validate our newly developed snoring detection device to identify automatically snore and non-snore sounds using a nonlinear analysis technique. The developed device can analyze chaotic features of a snore related sounds such as entropy, Largest Lyapunov Exponents (LLEs) and also has the data classification ability depending on the feature values. We report that the developed snoring detection device with proposed automatic classification method could achieve an accuracy of 94.38% for experiment I and 82.02 for experiment II when analyzing snore and non-snore sounds from 22 subjects. This study revealed the efficacy of our newly developed snoring detection device and indicated that it may be used at home an alternative to diagnose snore related sounds. It is anticipated that our findings will contribute to the development of an automated snore analysis system to be used in sleep studies.