Obstructive sleep apnoea hypopnoea syndrome (OSAHS) is a highly prevalent disease in which upper airways are collapsed during sleep, leading to serious consequences. The reference standard of clinical diagnosis, called Polysomnography (PSG), requires a full-night hospital stay connected to over 15 measuring channels requiring physical contact with sensors. The vast quantity of physiological data acquired during the PSG has to be manually scored by a qualified technologist to assess the presence or absence of the decease. The PSG is inconvenient, time consuming, expensive and unsuited for community screening. The limited PSG facilities around the world have resulted in long waiting lists and a large fraction of patients remain undiagnosed at present. There has been a flurry of recent activities in developing a portable technology to resolve this need. All the devices have at least one sensor that requires physical contact with the subject. Unattended systems have not led to sufficiently high sensitivity/specificity levels to be used in a routine home monitoring or a community screening exercise. OSAHS is a sleep respiratory disorder principally caused by functional deficiencies occurring in the upper airways during sleep. These conditions and the reduced muscle tone during sleep, cause the muscles in the upper airways to collapse partially or completely thus resulting in episodes of hypopnoea and apnoea respectively. During the process leading to collapse of upper airways, upper airways act as an acoustic filter frequently producing snoring sounds. The process of snore sound production leads us to hypothesise that snore sounds should contain information on changes occurring in the upper airways during the OSAHS. Snoring almost always accompanies the OSAHS and is universally recognised as its earliest symptom. At present, however, the quantitative analysis of snore sounds is not a practice in clinical OSAHS detection. The vast potential of snoring in the diagnosis/screening of the OSAHS remains unused. Snoring-based technology opens up opportunities for building community-screening devices that do not depend on contact instrumentation. In this thesis, we present our work towards developing a snore–based non-contact instrumentation for the diagnosis/screening of the OSAHS. The primary task in the analysis of Snore Related Sounds (SRS) would be to segment the SRS data as accurately as possible into three main classes, snoring (voiced non-silence), breathing (unvoiced non-silence) and silence. A new algorithm was developed, based on pattern recognition for the SRS segmentation. Four features derived from the SRS were considered to classify samples of the SRS into three classes. We also investigated the performance of the algorithm with three commonly-used noise reduction (NR) techniques in speech processing, Amplitude Spectral Subtraction (ASS), Power Spectral Subtraction (PSS) and Short Time Spectral Amplitude (STSA) Estimation. It was found that the noise reduction, together with a proper choice of features, could improve the classification accuracy to 96.78%. A novel model for the SRS was proposed for the response of a mixed-phase system (total airways response, TAR) to a source excitation at the input. The TAR/source model is similar to the vocal tract/source model in speech synthesis and is capable of capturing the acoustical changes brought about by the collapsing upper airways in the OSAHS. An algorithm was developed, based on the higher-order-spectra (HOS) to jointly estimate the source and the TAR, preserving the true phase characteristics of the latter. Working on a clinical database of signals, we show that the TAR is indeed a mixed phased signal and second-order statistics cannot fully characterise it. Nocturnal speech sounds can corrupt snore recordings and pose a challenge to the snore-based OSAHS diagnosis. The TAR could be shown to detect speech segments embedded in snores and derive features to diagnose the OSAHS. Finally presented is a novel technique for diagnosing the OSAHS, based solely on multi-parametric snore sound analysis. The method comprises a logistic regression model fed with a range of snore parameters derived from its features — the pitch and Total Airways Response (TAR) estimated using a Higher Order Statistics (HOS) based algorithm. The model was developed and its performance validated on a clinical database consisting of overnight snoring sounds simultaneously recorded during a hospital PSG using a high fidelity sound recording setup. The K-fold cross validation technique was used for validating the model. The validation process achieved an 89.3% sensitivity with 92.3% specificity (the area under the Receiver Operating Characteristic (ROC) curve was 0.96) in classifying the data sets into the two groups, the OSAHS (AHI >10) and the non-OSAHS. These results are superior to the existing results and unequivocally illustrate the feasibility of developing a snore-based non-contact OSAHS screening device.
| Identifer | oai:union.ndltd.org:ADTP/286111 |
| Creators | Asela S Karunajeewa |
| Source Sets | Australiasian Digital Theses Program |
| Detected Language | English |
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