TY - GEN
T1 - Analysis of long duration snore related signals based on formant features
AU - Wu, Yaqi
AU - Zhao, Zhao
AU - Qian, Kun
AU - Xu, Zhiyong
AU - Xu, Huijie
PY - 2013
Y1 - 2013
N2 - Snoring is a typical symptom of Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) patients, which has motivated numerous researchers focusing on how to diagnose this disorder by acoustic signal analysis methods. As a non-invasive approach, acoustic diagnosis brings a much more comfortable and convenient experience to subjects than the gold standard, polysomnography (PSG). However, there is a more demanding need from doctors to find the variations of the upper airway (UA) during a long duration for OSAHS patients. Formant features have a good performance on indicating the structure variations of UA, which can be regarded as a resonance in the snoring generation model. In this paper, we proposed a long duration analysis method of snore related signals (SRS) method based on formant features. The first three formant frequencies (F1, F2 and F3) are extracted to group the long duration SRS data into different states with the help of K-means method. Each state of SRS data represents a degree of collapse in UA. We found that formant features have distinguished values in different states and the transition possibility calculated by Hidden Markov Models (HMM) between each state is helpful for analysis of long duration SRS data. This method could be effective in analysis of variations in UA for OSAHS patients and establishment of long duration SRS database.
AB - Snoring is a typical symptom of Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) patients, which has motivated numerous researchers focusing on how to diagnose this disorder by acoustic signal analysis methods. As a non-invasive approach, acoustic diagnosis brings a much more comfortable and convenient experience to subjects than the gold standard, polysomnography (PSG). However, there is a more demanding need from doctors to find the variations of the upper airway (UA) during a long duration for OSAHS patients. Formant features have a good performance on indicating the structure variations of UA, which can be regarded as a resonance in the snoring generation model. In this paper, we proposed a long duration analysis method of snore related signals (SRS) method based on formant features. The first three formant frequencies (F1, F2 and F3) are extracted to group the long duration SRS data into different states with the help of K-means method. Each state of SRS data represents a degree of collapse in UA. We found that formant features have distinguished values in different states and the transition possibility calculated by Hidden Markov Models (HMM) between each state is helpful for analysis of long duration SRS data. This method could be effective in analysis of variations in UA for OSAHS patients and establishment of long duration SRS database.
KW - Hidden Markov Models (HMM)
KW - Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS)
KW - formant features
KW - snore related signals (SRS)
KW - upper airway (UA)
UR - http://www.scopus.com/inward/record.url?scp=84893791261&partnerID=8YFLogxK
U2 - 10.1109/ITA.2013.27
DO - 10.1109/ITA.2013.27
M3 - Conference contribution
AN - SCOPUS:84893791261
SN - 9781479928767
T3 - Proceedings - 2013 International Conference on Information Technology and Applications, ITA 2013
SP - 91
EP - 95
BT - Proceedings - 2013 International Conference on Information Technology and Applications, ITA 2013
T2 - 2013 International Conference on Information Technology and Applications, ITA 2013
Y2 - 16 November 2013 through 17 November 2013
ER -