Abstract
BackgroundObstructive Sleep Apnoea (OSA) is conventionally quantified by the Apnoea-Hypopnea Index (AHI), used to classify disease severity. Automation of AHI detection relies on identification of singular data points in long, multi-channel polysomnography (PSG) recordings. This can be easily compromised by signal noise. We present a novel mathematical method, the Symmetric Projection Attractor Reconstruction (SPAR), that may overcome this problem by transforming whole cyclic physiological recordings into corresponding ‘at-a-glance’ images (‘attractors’) which capture all available waveform morphology information, without relying on single point detection. Attractor quantification may provide a more rapid and robust mean of quantifying the number and duration of overnight apnoeic and hypopneic events.AimTo test whether SPAR can categorize overnight obstructive sleep apnoea recordings according to severity classifications informed by expert-annotated AHI.Methods74 PSG recordings were analysed (52 non-OSA subjects/22 severe-OSA patients, 43.0/27.3% female, 37.4/48.9 average y.o.). Abdominal-band motion data was chosen for pilot analysis due to its relatively low complexity of waveform morphology. Data were processed through bespoke SPAR software to create SPAR attractors. Quantification of high central attractor density corresponded to low/no-amplitude waveform regions, serving as a surrogate for the number and length of apnoeic and hypopneic events. This metric was used to classify between non-OSA subjects (AHI <5/hour) and severe-OSA patients (AHI>30/hour). Receiver Operator Characteristics Area Under the Curve (ROC AUC) was used to measure classification accuracy.ResultsSPAR attractors created from 3 continuous hours of overnight abdominal-band data visually differentiated non-OSA and severe OSA patients, showing a high-density central area in the severe-OSA group (figure 1) which was absent in the non-OSA group. Quantification of attractor central density classified these two groups with high accuracy (ROC AUC = 0.99).Abstract P102 Figure 1Overnight 5-minute abdominal movement data and corresponding 3-hour SPAR attractor from a representative non-OSA subject (top, low central attractor density) and severe-OSA patient (bottom, high central attractor density). Y axes on traces shown as ±105 arbitary units[Figure omitted. See PDF]ConclusionSPAR analysis of abdominal-band motion recordings accurately classified non-OSA vs. severe OSA patients. Further analysis will apply SPAR to categorise all types of OSA severities, and test SPAR’s ability to serve as a novel, visual, OSA triage tool. Future studies will compare SPAR to automated AHI scoring in various PSG signals to ascertain if the method could benefit the efficiency of current diagnostic processes.