Automatic Detection of Snore Events from Full Night Audio Recordings

Abstract

One of the most common applications of snore sound analysis is to find out its relationship with obstructive sleep apnoea (OSA). The present work is aimed at developing a method for processing full night snore recordings obtained from a simple recording system that can be used both for clinical and home application. Automatic extraction of snore sounds only could in fact allow for saving much time usually required for manual analysis

The analysis system consists of three steps: pre-processing, automatic segmentation, extraction of features and classification.

The automatic segmentation is based on short-term energy measures: Otsu thresholding is applied to the histogram of the audio signal energy to detect the starting and ending points of sound events in the whole recording.

Once all the sound events from the signal are obtained, they have to be classified as snore, breath or “other”. For a reliable analysis of OSA, only snore events must be detected. To this aim, we present a new classification system based on two Artificial Neural Networks applied to four features: length, energy, standard deviation and maximum amplitude, for each extracted event.

Audio data from 24 patients were used to test the method; on the dataset, a sensitivity of 86.2% and a specificity of 86.3% were obtained. Future work will be devoted to enhancing the procedure and to defining a reliable method for the identification of post-apnoeic events from the detected snore sounds.