Sleep apnoea is a clinical condition associated with a number of serious clinical and other problems. Patients who suffer from sleep apnoea have recurrent nocturnal apnoeas. The aim of this study was to assess the ability of an automated computer algorithm to detect sleep apnoea from the characteristic pattern of its recurrence, using RR interval data. Data from 35 training and 35 test subjects supplied by PhysioNet were analysed. To produce an algorithm which did not require highly accurate QRS detection, the QRS information supplied by PhysioNet were used without checking for artifactual data. Each subject's data were converted to a sequence of beat intervals, which was then analysed by Fourier transform. The study period varied from less than 7 hours to more than 10 hours. Patients with sleep apnoea tended to have a spectral peak lying between 0.01 and 0.05 cycles/beat, with the width of the peak indicating variability in the recurrence rate of the apnoea. In most subjects the frequency spectrum immediately below that containing the apnoea peak was relatively flat. The first visual analysis of the single computed spectrum from each subject led to a correct classification score of 28/30 (93%). The ratio of the content of the two spectral regions was obtained by dividing the area under the spectral curve between 0.01 and 0.05 cycles/beat by the area between 0.005 and 0.01 cycles/beat, and then a fixed threshold (3.15) was used to classify, the subjects automatically. The automated score for the training set was 27/30 (90%), 17/20 Apnoea (A), 10/10 Normal (C). The automated score for the test set was also 27/30 (90%).
|Name||Computers in Cardiology|
|Conference||Computers in Cardiology 2000|
|Period||24/09/00 → 27/09/00|