HMM-based snorer group recognition for Sleep Apnea diagnosis

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...

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Bibliographic Details
Main Authors: Herath, Dulip L., Abeyratne, Udantha R., Hukins, Craig
Format: Conference Proceeding
Language:English
Subjects:
Cepstral analysis
Hidden Markov models
Lead
Medical diagnostic imaging
Physiology
Online Access:Request full text
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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), 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.
ISSN:1094-687X
1558-4615
2694-0604
DOI:10.1109/EMBC.2013.6610412