Obstructive Sleep Apnea Detection Using Difference in Feature and Modified Minimum Distance Classifier

The current gold standard of Obstructive Sleep Apnea (OSA) diagnosis involves the use of a Polysomnography (PSG) system which requires the patient to stay in the hospital for overnight recording. The process is uncomfortable for the patient and it disturbs the patient's sleep pattern. On the other hand, it is well known that some acoustic features of the snoring sounds are good indicators of the presence of OSA, and a variety of acoustic OSA detection algorithms have been reported in the literature. Typically, these algorithms use multiple features and a relatively complex classifier, which are not ideal for handling the huge over-night data. In this paper, we propose an algorithm that uses a single feature and a relatively simple classifier. The proposed feature is the difference between two carefully selected Mel-frequency cepstral coefficients (MFCCs) of the snoring sound samples. The proposed classifier is derived based on a modified minimum distance criterion. The proposed algorithm has been tested with patient data. The results show that the proposed algorithm outperforms existing algorithms and is able to achieve up to 97.1% detection accuracy.