An Investigation of Critical Frequency Sub-bands of Snoring Sounds for OSA Diagnosis.

Obstructive Sleep Apnea (OSA) is a result of upper airway narrowing during sleep. The upper airway characteristics are likely to manifest in the acoustic characteristics of snoring sounds as snoring is a result of upper airway structure vibrations. In previous studies, researchers have used different regions of the frequency spectrum to diagnose OSA and determine sites of obstruction as well. However, there is no agreement among researchers about the frequency ranges critical for OSA diagnosis. This paper provides the results of a study of snore sound based OSA diagnosis performance using a multiple acoustic features and multiple classifiers. The results of the study may provide useful insights for researchers to identify frequency sub-bands critical for OSA diagnosis.

Hidden Markov modelling of intra-snore episode behavior of acoustic characteristics of obstructive sleep apnea patients.

Obstructive sleep apnea (OSA) is a breathing disorder that can cause serious medical consequences. It is caused by full (apnea) or partial (hypopnea) obstructions of the upper airway during sleep. The gold standard for diagnosis of OSA is the polysomnography (PSG). The main measure for OSA diagnosis is the apnea-hypopnea index (AHI). However, the AHI is a time averaged summary measure of vast amounts of information gathered in an overnight PSG study. It cannot capture the dynamic characteristics associated with apnea/hypopnea events and their overnight distribution. The dynamic characteristics of apnea/hypopnea events are affected by the structural and functional characteristics of the upper airway. The upper airway characteristics also affect the upper airway collapsibility. These effects are manifested in snoring sounds generated from the vibrations of upper airway structures which are then modified by the upper airway geometric and physical characteristics. Hence, it is highly likely that the acoustical behavior of snoring is affected by the upper airway structural and functional characteristics. In the current work, we propose a novel method to model the intra-snore episode behavior of the acoustic characteristics of snoring sounds which can indirectly describe the instantaneous and temporal dynamics of the upper airway. We model the intra-snore episode acoustical behavior by using hidden Markov models (HMMs) with Mel frequency cepstral coefficients. Assuming significant differences in the anatomical and physiological upper airway configurations between low-AHI and high-AHI subjects, we defined different snorer groups with respect to AHI thresholds 15 and 30 and also developed HMM-based classifiers to classify snore episodes into those groups. We also define a measure called instantaneous apneaness score (IAS) in terms of the log-likelihoods produced by respective HMMs. IAS indicates the degree of class membership of each episode to one of the predefined groups as well as the instantaneous OSA severity. We then assigned each patient to an overall AHI band based on the majority vote of each episode of snoring. The proposed method has a diagnostic sensitivity and specificity between 87-91%.

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 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.