HMM-based snorer group recognition for Sleep Apnea diagnosis

Herath, Dulip L., Abeyratne, Udantha R. and Hukins, Craig (2013). HMM-based snorer group recognition for Sleep Apnea diagnosis. In: Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE. 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013, Osaka, Japan, (3961-3964). 3 - 7 July 2013. doi:10.1109/EMBC.2013.6610412


Author Herath, Dulip L.
Abeyratne, Udantha R.
Hukins, Craig
Title of paper HMM-based snorer group recognition for Sleep Apnea diagnosis
Conference name 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
Conference location Osaka, Japan
Conference dates 3 - 7 July 2013
Proceedings title Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE   Check publisher's open access policy
Journal name IEEE Engineering in Medicine and Biology Society. Conference Proceedings   Check publisher's open access policy
Place of Publication Piscataway, NJ United States
Publisher Institute of Electrical and Electronics Engineers
Publication Year 2013
Year available 2013
Sub-type Fully published paper
DOI 10.1109/EMBC.2013.6610412
ISBN 9781457702167
ISSN 1557-170X
Start page 3961
End page 3964
Total pages 4
Language eng
Abstract/Summary This paper presents an Hidden Markov Models (HMM)-based snorer group recognition approach for Obstructive Sleep Apenea diagnosis. It models the spatio-temporal characteristics of different snorer groups belonging to different genders and AHI severity levels. The current experiment includes selecting snore data from subjects, identifying snorer groups based on gender and AHI values (AHI < 15 and AHI > 15), detecting snore episodes, MFCC computation, training and testing HMMs. A set of multi-level classification rules is employed for incremental diagnosis of OSA. The proposed method, with a relatively small data set, produces results nearly comparable to any existing methods with single feature class. It classifies snore episodes with 62.0% (male), 67.0% (female) and recognizes snorer group with 78.5% accuracy. The approach makes its diagnosis decision at 85.7% (sensitivity), 71.4% (specificity) for males and 85.7% (sensitivity and specificity) for females.
Subjects 1707 Computer Vision and Pattern Recognition
1711 Signal Processing
2204 Religion and Religious Studies
2718 Health Informatics
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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