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 s...

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Bibliographic Details
Main Authors: Yuxia Zhao, Haixiu Zhang, Wenlong Liu, Shuxue Ding
Format: Conference Proceeding
Language:English
Subjects:
Argon
Atmospheric modeling
Computational modeling
Educational institutions
Formant
Frequency estimation
High definition video
K-means
OSAHS
Personalized Threshold
Snore
Online Access:Request full text
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Summary: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%.
ISSN:2325-5986
2325-5994
DOI:10.1109/ICAwST.2011.6163089