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

摘要

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%.