Location
University of Utah
Start Date
5-13-2002 9:10 AM
Description
A system for measurement of oxygen consumption (V02) and determination of respiratory quotient (RQ: RQ = VO2/VCO2) is currently being developed by a joint project between Novametrix Inc. (Wallingford CT) and the University of Utah Department of BioEngineering. The system may prove to be highly useful on 'extended duration space flight to monitor the metabolic rate of astronauts. The system employs a novel oxygen partial pressure sensor based on oxygen luminescence quenching technology for real-time measurement of respiratory oxygen concentration. This paper addresses the sensors's signal vs. noise properties. The signal to noise (SIN) ratio of the sensor has been found to degrade progressively with increasing oxygen partial pressure (pO2) with the degradation appearing to become problematic at oxygen partial pressures above approximately 60%. In order to improve the (high pO2) SIN ratio of the sensor, a number of signal processing techniques were investigated. These techniques were selected based on a qualitative assessment of the sensor's unique signal processing requirements and the effectiveness of the techniques was quantitatively characterized for comparison purposes. The techniques included linear as well as non-linear filtering strategies. The linear filtering strategies investigated consisted of two classes of notch filters while the more disparate non-linear filters consisted of classes of polynomial (Voltera series) filters, median and median-related filters, order statistic filters, morphological filters and weighted majority with minimum range filters. Each of the filters investigated were optimized using actual sensor data to improve sensor SIN ratio performance while maintaining adequate sensor dynamics. A number of candidate filters with varying degrees of computational complexity and noise suppression effectiveness are proposed for the sensor. Future studies will evaluate the performance of these filters within the framework of candidate oxygen consumption algorithms.
Quo Oxygen Sensor: Linear and Non-Linear Filtering Approaches to Noise Reduction
University of Utah
A system for measurement of oxygen consumption (V02) and determination of respiratory quotient (RQ: RQ = VO2/VCO2) is currently being developed by a joint project between Novametrix Inc. (Wallingford CT) and the University of Utah Department of BioEngineering. The system may prove to be highly useful on 'extended duration space flight to monitor the metabolic rate of astronauts. The system employs a novel oxygen partial pressure sensor based on oxygen luminescence quenching technology for real-time measurement of respiratory oxygen concentration. This paper addresses the sensors's signal vs. noise properties. The signal to noise (SIN) ratio of the sensor has been found to degrade progressively with increasing oxygen partial pressure (pO2) with the degradation appearing to become problematic at oxygen partial pressures above approximately 60%. In order to improve the (high pO2) SIN ratio of the sensor, a number of signal processing techniques were investigated. These techniques were selected based on a qualitative assessment of the sensor's unique signal processing requirements and the effectiveness of the techniques was quantitatively characterized for comparison purposes. The techniques included linear as well as non-linear filtering strategies. The linear filtering strategies investigated consisted of two classes of notch filters while the more disparate non-linear filters consisted of classes of polynomial (Voltera series) filters, median and median-related filters, order statistic filters, morphological filters and weighted majority with minimum range filters. Each of the filters investigated were optimized using actual sensor data to improve sensor SIN ratio performance while maintaining adequate sensor dynamics. A number of candidate filters with varying degrees of computational complexity and noise suppression effectiveness are proposed for the sensor. Future studies will evaluate the performance of these filters within the framework of candidate oxygen consumption algorithms.