Description
We present and evaluate a dynamic model to predict blood oxygen saturation from respiration signals for use in automated sleep apnea detection. The model uses the averaged integral of a signal representing the respiration magnitude entering the lungs. This signal affects changes in blood oxygen saturation level through standard respiration processes. The results demonstrate that the approach provides reliable and robust estimation of blood oxygen saturation using only the respiration signal. Furthermore, the model may be incorporated in automated sleep apnea detection algorithms that can lead to inexpensive and reliable sleep apnea detection that do not require an overnight stay in the hospital for polysomnogram recordings.
Publication Date
4-12-2013
Publisher
Case Western Reserve University Research ShowCase 2013
City
Cleveland, OH
Keywords
Signal Processing, Sleep Apnea
Recommended Citation
Shewinvanakitkul, Prapan; Buchner, Marc; and Loparo, Kenneth A., "Using Respiration Data to Predict Oxygen Saturation for Sleep Apnea Detection" (2013). Research ShowCASE. 13.
https://commons.case.edu/research-showcase/13