Automatic Classification of Respiratory Sounds During Sleep

Abstract

Sounds like snoring, coughing, sneezing, whistling, which have different acoustic properties, can emerge involuntarily during the sleep. These sounds may affect negatively the sleep quality of the other people in the same environment, just as it may affect directly the sleep quality. To increase the sleep quality, these sounds should be recorded and evaluated by a sleep expert. This is an expertise required process that can be time-consuming and subjective results. In this study, it has been aimed that developing a computer-aided diagnosing algorithm which will classify the sounds emerging during the sleep automatically with high accuracy by analyzing the records in a fast and effective way to help the sleep expert to diagnose. The mathematical features have been obtained in frequency and time domains by applying continuous wavelet transform for the different type of sounds. Support vector machine used as a classifier. 390 and 449 segments were used for training and testing respectively. As a result of the study, six different parameters which are exhalation, simple snoring, high frequency duplex snoring, low frequency duplex snoring, triplex snoring and coughing were classified with 96.44% accuracy rate.