A snoring detector for OSAHS based on patient´s individual personality

A conventional diagnostic tool for assessing Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) is polysomnography (PSG), which is expensive and uncomfortable for patients. It is an important and urgent topic to find a non-invasive and low-cost diagnostic approach for OSAHS detection. Recently, the snore signal analysis receives much attention due to its potential capability for OSAHS detection. In this paper, we propose a novel method for diagnosing OSAHS based on patient´s individual personality. First, the first formant frequencies of each snorer are classified into two clusters by K-means clustering. And then, using the first cluster center of each snorer, we set a personalized threshold to distinguish the hypopneic snores from the normal ones. Since the proposed threshold varies with each individual, the patient´s individual personality can be overcome effectively. Experimental results show the validity of the proposed detector. In the experiments, the sensitivity of our method can achieve 90% and the specificity can achieve 91.67%.