Dynamic Model Inversion Techniques for Breath-by-Breath Measurement of Carbon Dioxide from Low Bandwidth Sensors

Respiratory CO2measurement (capnography) is an important diagnosis tool that lacks inexpensive and wearable sensors. This paper develops techniques to enable use of inexpensive but slow CO2sensors for breath-by-breath tracking of CO2concentration. This is achieved by mathematically modeling the dynamic response and using model-inversion techniques to predict input CO2concentration from the slowly varying output. Experiments are designed to identify model-dynamics and extract relevant model-parameters for a solid-state room monitoring CO2sensor. 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 corresponding model-inversion algorithm is however found to be susceptible to noise sources. Techniques to remove spurious noise, while retaining quality of estimate are developed. The resulting estimate is compared with a standard-of-care respiratory CO2analyzer and shown to effectively track variation in breath-by-breath CO2concentration. This methodology is potentially useful for measuring fast-varying inputs to any slow sensor.