Abstract
Respiratory CO(2) measurement (capnography) is an important diagnosis tool that lacks inexpensive and wearable sensors. This paper develops techniques to enable use of inexpensive but slow CO(2) sensors for breath-by-breath tracking of CO(2) concentration. This is achieved by mathematically modeling the dynamic response and using model-inversion techniques to predict input CO(2) concentration from the slow-varying output. Experiments are designed to identify model-dynamics and extract relevant model-parameters for a solidstate room monitoring CO(2) sensor. A second-order model that accounts for flow through the sensor's filter and casing is found to be accurate in describing the sensor's slow response. The resulting estimate is compared with a standard-of-care respiratory CO(2) analyzer and shown to effectively track variation in breath-by-breath CO(2) concentration. This methodology is potentially useful for measuring fast-varying inputs to any slow sensor.
Original language | English (US) |
---|---|
Pages (from-to) | 3039-3042 |
Number of pages | 4 |
Journal | Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference |
State | Published - 2009 |
Externally published | Yes |
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Signal Processing
- Biomedical Engineering
- Health Informatics