Dynamical analysis of respiratory signals for diagnosis of sleep disordered breathing disorders.

Suren Rathnayake (2009). Dynamical analysis of respiratory signals for diagnosis of sleep disordered breathing disorders. PhD Thesis, School of Information Technology and Electrical Engineering, The University of Queensland.

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads
s41038396_phd_abstract.pdf abstract application/pdf 34.81KB 3
s41038396_phd_totalthesis.pdf thesis application/pdf 4.70MB 13
Author Suren Rathnayake
Thesis Title Dynamical analysis of respiratory signals for diagnosis of sleep disordered breathing disorders.
School, Centre or Institute School of Information Technology and Electrical Engineering
Institution The University of Queensland
Publication date 2009-03
Thesis type PhD Thesis
Total pages 167
Total colour pages 14
Total black and white pages 153
Subjects 06 Biological Sciences
Abstract/Summary Sleep disordered breathing (SDB) is a highly prevalent but an under-diagnosed disease. Among adults in the ages between 30 to 60 years, 24% of males and 9% of females show conditions of SDB, while 82% of men and 93% of women with moderate to severe SDB remain undiagnosed. Polysomnography (PSG) is the reference diagnostic test for SDB. During PSG, a number of physiological signals are recorded during an overnight sleep and then manually scored for sleep/wake stages and SDB events to obtain the reference diagnosis. The manual scoring of SDB events is an extremely time consuming and cumbersome task with high inter- and intra-rater variations. PSG is a labour intensive, expensive and patient inconvenient test. Further, PSG facilities are limited leading to long waiting lists. There is an enormous clinical need for automation of PSG scoring and an alternative automated ambulatory method suitable for screening the population. During the work of this thesis, we focus (1) on implementing a framework that enables more reliable scoring of SDB events which also lowers manual scoring time, and (2) implementing a reliable automated screening procedure that can be used as a patient-friendly home based study. The recordings of physiological measurements obtained during patients’ sleep of- ten suffer from data losses, interferences and artefacts. In a typical sleep scoring session, artifact-corrupted signal segments are visually detected and removed from further consideration. We developed a novel framework for automated artifact detection and signal restoration, based on the redundancy among respiratory flow signals. The signals focused on are the airflow (thermistor sensors) and nasal pressure signals that are clinically significant in detecting respira- tory disturbances. We treat the respiratory system as a dynamical system, and use the celebrated Takens embedding theorem as the theoretical basis for sig- nal prediction. In this study, we categorise commonly occurring artefacts and distortions in the airflow and nasal pressure measurements into several groups and explore the efficacy of the proposed technique in detecting/recovering them. Results we obtained from a database of clinical PSG signals indicated that theproposed technique can detect artefacts/distortions with a sensitivity >88% and specificity >92%. This work has the potential to simplify the work done by sleep scoring technicians, and also to improve automated sleep scoring methods. During the next phase of the thesis we have investigated the diagnostic ability of single – and dual–channel respiratory flow measuring devices. Recent studies have shown that single channel respiratory flow measurements can be used for automated diagnosis/screening for sleep disordered breathing (SDB) diseases. Improvements for reliable home-based monitoring for SDB may be achieved with the use of predictors based on recurrence quantification analysis (RQA). RQA essentially measures the complex structures present in a time series and are relatively independent of the nonlinearities present in the respiratory measurements such as those due to breathing nonlinearities and sensor movements. The nasal pressure, thermistor-based airflow, abdominal movement and thoracic movement measurements obtained during Polysomnography, were used in this study to implement an algorithm for automated screening for SDB diseases. The algorithm predicts SDB-affected measurement segments using twelve features based on RQA, body mass index (BMI) and neck circumference using mixture discriminant analysis (MDA). The rate of SDB affected segments of data per hour of recording (RDIS) is used as a measure for the diagnosis of SDB diseases. The operating points to be chosen were the prior probability of SDB affected data segments (π1) and the RDIS threshold value, above which a patient is predicted to have a SDB disease. Cross-validation with five-folds, stratified based on the RDI values of the recordings, was used in estimating the operating points. Sensitivity and specificity rates for the final classifier were estimated using a two-layer assessment approach with the operating points chosen at the inner layer using five-fold cross-validation and the choice assessed at the outer layer using repeated learning-testing. The nasal pressure measurement showed higher accuracy compared to other respiratory measurements when used alone. The nasal pressure and thoracic movement measurements were identified as the best pair of measurements to be used in a dual channel device. The estimated sensitivity and specificity (standard error) in diagnosing SDB disease (RDI ≥ 15) are 90.3(3.1)% and 88.3(5.5)% when nasal pressure is used alone and together with the thoracic movement it was 89.5(3.7)% and 100.0(0.0)%. Present results suggest that RQA of a single respiratory measurement has potential to be used in an automated SDB screening device, while with dual-channel more reliable accuracy can be expected. Improvements may be possible by including other RQA based features and optimisation of the parameters.
Keyword nonlinear signal analysis
dynamic systems modelling
sleep disordered breathing
neural networks
recurrence quantification analysis
mixture discriminant analysis
Additional Notes 63, 88, 89, 90, 95, 96, 97, 129, 146, 147, 13, 14, 15, 16

Citation counts: Google Scholar Search Google Scholar
Created: Mon, 22 Feb 2010, 12:54:58 EST by Mr Suren Rathnayake on behalf of Library - Information Access Service