Automatic breath and snore sounds classification from tracheal and ambient sounds recordings.

In this study respiratory sound signals were recorded from 23 patients suspect of obstructive sleep apnea, who were referred for the full-night sleep lab study. The sounds were recorded with two microphones simultaneously: one placed over trachea and one hung in the air in the vicinity of the patient. During recording the sound signals, patients' Polysomnography (PSG) data were also recorded simultaneously. An automatic method was developed to classify breath and snore sound segments based on their energy, zero crossing rate and formants of the sound signals. For every sound segment, the number of zero crossings, logarithm of the signal's energy and the first formant were calculated. Fischer Linear Discriminant was implemented to transform the 3-dimensional (3D) feature set to a 1-dimensional (1D) space and the Bayesian threshold was applied on the transformed features to classify the sound segments into either snore or breath classes. Three sets of experiments were implemented to investigate the method's performance for different training and test data sets extracted from different neck positions. The overall accuracy of all experiments for tracheal recordings were found to be more than 90% in classifying breath and snore sounds segments regardless of the neck position. This implies the method's accuracy is insensitive to patient's position; hence, simplifying data analysis for an entire night recording. The classification was also performed on sounds signals recorded simultaneously with an ambient microphone and the results were compared with those of the tracheal recording.