Digital Case

Using Respiration Data to Predict Oxygen Saturation for Sleep Apnea Detection (Text)

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Creator
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.
Subject
Date
  • 2013-03-20
Source
  • Case Western Reserve University
Language
  • English
Type
  • poster
Format
  • application/pdf
PID
  • ksl:sheusi00
Identifier ksl:sheusi00
HANDLE http://hdl.handle.net/2186/ksl:sheusi00
Access Rights Open Access
RIS Citation

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Case Western Reserve University Research ShowCASE Posters 2013