Comparative analysis of polysomnographic signals for classifying obstructive sleep apnoea

Thesis

Obstructive sleep apnoea (OSA) is a common disorder involving repeated cessations of breathing due to airway collapse, causing disruption of sleep cycles. The condition is under-diagnosed and the side effects are many and varied. Currently, the ‘gold standard’ diagnostic tool for OSA is a polysomnogram (PSG) which is carried out overnight in a hospital using multiple sensors. A PSG is expensive to set-up, run and analyse, and some subjects experience different sleep patterns due to the artificial conditions of the sleep laboratory.

The aim of this thesis was to find a parsimonious and easy-to-collect set of signals (from the superset of signals recorded in sleep clinics) and other related information (such as demographics), and a set of automated methods that reliably determine which subjects are suitable for standard treatments, i.e. classify subjects requiring treatment (moderate OSA, severe OSA) from those not requiring treatment (normal, snorer, mild OSA), using a smartphone. Data were collected from 1354 subjects in the home using the Grey Flash polysomnographic recording device (Stowood Scientific Instruments, Oxford, UK). Analysis of the audio signal was initially performed using standard speech processing methods, where individual events were annotated and classified. The results achieved (accuracy (Ac) = 69.6%) using this approach were lower than those required for clinical acceptance. In all subsequent work in the thesis, subjects were classified from entire recordings rather than events. Multiscale entropy (MSE) was used to identify non-linear correlations in the audio data and quantify the irregularity of the data over many time scales. The inter-snore interval (ISI) was developed, motivated by clinical intuition. MSE and ISI were then applied to both actigraphy and photoplethysomgraphy (PPG) data, and different combinations of features were analysed. The features which displayed the highest predictive accuracy were derived from the PPG signal (Ac = 89.2%). This work demonstrated that, although audio- and actigraphy-based OSA screening is possible, to achieve clinically acceptable performance PPG remains an important key factor in diagnosis.

Authors: Roebuck, A

• Publication date: 2015
• DOI: 10.5287/ora-wvewvbe24
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: Oxford University, UK
• Language: English
• Keywords: audio; app; OSA; actigraphy; PPG; machine learning
• Subjects: Biomedical Engineering; Engineering Science