Session S53.1

Detection Of Sleep Apnoea From Frequency Analysis Of Heart-Rate Variability

M.J. Drinnan, J. Allen, P. Langley, A. Murray

Freeman Hospital
Newcastle upon Tyne, UK

Sleep apnoea is a serious clinical condition associated with high blood pressure, infarction, stroke and a high accident rate. 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.
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 under 7 hours to over 10 hours. Patients with sleep apnoea tended to have a spectral peak lying between 0.01 and 0.05/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 a single computed spectrum from each subject led to a correct classification score of 28/30 (93%) (score reference 20000503.025229, entrant 11). 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/beat by the area between 0.005 and 0.01/beat, using a fixed threshold (3.15) 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%) (score reference 20000503.095731, entrant 11).