Obstructive Sleep Apnea Classification Using Snore Sounds Based on Deep Learning

Abstract

Early screening for the Obstructive Sleep Apnea (OSA), especially the first grade of Apnea-Hypopnea Index (AHI), can reduce risk and improve the effectiveness of timely treatment. The current gold standard technique for OSA diagnosis is Polysomnography (PSG), but the technique must be performed in a specialized laboratory with an expert and requires many sensors attached to a patient. Hence, it is costly and may not be convenient for a self-test by the patient. The characteristic of snore sounds has recently been used to screen the OSA and more likely to identify the abnormality of breathing conditions. Therefore, this study proposes a deep learning model to classify the OSA based on snore sounds. The snore sound data of 5 OSA patients were selected from the opened-source PSG- Audio data by the Sleep Study Unit of the Sismanoglio-Amalia Fleming General Hospital of Athens [1]. 2,439 snoring and breathing-related sound segments were extracted and divided into 3 groups of 1,020 normal snore sounds, 1,185 apnea or hypopnea snore sounds, and 234 non-snore sounds. All sound segments were separated into 60% training, 20% validation, and 20% test sets, respectively. The mean of Mel-Frequency Cepstral Coefficients (MFCC) of a sound segment were computed as the feature inputs of the deep learning model. Three fully connected layers were used in this deep learning model to classify into three groups as (1) normal snore sounds, (2) abnormal (apnea or hypopnea) snore sounds, and (3) non-snore sounds. The result showed that the model was able to correctly classify 85.2459%. Therefore, the model is promising to use snore sounds for screening OSA.