Abstract
We are developing an algorithm for off-line detection and classification of sleep-disordered breathing based on time series analysis of nasal mask pressure and acoustic respiratory input impedance measured by forced oscillation technique at a frequency of 20 Hz throughout the night. A first version of the algorithm was applied to a data set consisting of full-night measurements on 5 subjects. The data set had a total duration of 34 hours and contained 577 respiratory events (hypopneas, obstructive and central apneas) recognized by the staff physicians of an accredited sleep laboratory. The algorithm detected 455 (79 %) of these events and 138 events that had not been marked by the physicians. 75 % of the congruently detected events were also concordantly classified. After further optimization and evaluation, this approach might be useful when implemented into a device designed to screening or treatment control of sleep-related breathing disorders at home.
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Steltner, H. et al. (2000). Detection and Classification of Sleep-Disordered Breathing Using Acoustic Respiratory Input Impedance and Nasal Pressure. In: Brause, R.W., Hanisch, E. (eds) Medical Data Analysis. ISMDA 2000. Lecture Notes in Computer Science, vol 1933. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39949-6_10
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DOI: https://doi.org/10.1007/3-540-39949-6_10
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