Proposal for Sleep Apnea Syndrome discriminating method and discovery process pre-SAS.

Abstract

This paper presents a discrimination method of Sleep Apnea Syndrome (SAS) and early detection of potential Sleep Apnea Syndrome (Pre-SAS) using sleep breath sound, by which each subject can take the required data for the diagnosis at home only with a voice recorder. Sleep breath sound of around two hours is analyzed statistically for detecting SAS patients. Long silence sections in the sleep breath sound and the biggest sound pressure typically occurred after them are compared for distinguishing Pre-SAS, SAS patients and non-patients. The k-means method is applied for classifying the above sound data. The proposed method drastically reduces the time and effort required for SAS diagnosis compared to the current medical approaches. Experimental results show the effectiveness of the proposed method, by which 100 % accuracy of discrimination is achieved.

Keywords

• sound analysis
• multivariable analysis
• discriminant analysis
• clustering
• sleep apnea syndrome