A new method for measurement of gas exchange during anaesthesia using an extractable marker gas

Abstract

A new method for the measurement of pulmonary gas exchange during inhalational anaesthesia is described which measures fresh gas and exhaust gas flows using carbon dioxide as an extractable marker gas. The theoretical precision of the method was compared by Monte Carlo modelling with other approaches which use marker gas dilution. A system was constructed for automated measurement of uptake of oxygen, nitrous oxide, volatile anaesthetic agent and elimination of carbon dioxide by an anaesthetized patient. The accuracy and precision of the method was tested in vitro on a lung gas exchange simulator, by comparison with simultaneous measurements made using nitrogen as marker gas and the Haldane transformation. Good agreement was obtained for measurement of simulated uptake or elimination of all gases studied over a physiologically realistic range of values. Mean bias for oxygen and nitrous oxide uptake was 0.003 l min(-1), for isoflurane 0.0001 l min(-1) and for carbon dioxide 0.001 l min(-1). Limits of agreement lay within 10% of the mean uptake rate for nitrous oxide, within 5% for oxygen and isoflurane and within 1% for carbon dioxide. The extractable marker gas method allows accurate and continuous measurement of gas exchange in an anaesthetic breathing system with any inspired gas mixture.

Dealing with the Effects of Sensor Displacement in Wearable Activity Recognition

“…Subsequently a feature extraction process is carried out on each data window to provide a handler representation of the signals for the pattern recognition stage. A wide range of heuristics [36], time-frequency domain [37,38] and other mathematical and statistic functions [39] are commonly used. In some cases, a feature selector is used to reduce redundancy among features, as well as to minimize dimensionality.…”

Window Size Impact in Human Activity Recognition

“…Subsequently, a feature extraction process is carried out to provide a handler representation of the signals for the pattern recognition stage. A wide range of heuristics [67], time/frequency domain [44,45] and other sophisticated mathematical and statistic functions [68] are commonly used. The feature vector is provided as the input of the classifier or reasoner [69], ultimately yielding the recognized activity or class to one of those considered for the target problem.…”

Measurement of osteogenic exercise – how to interpret accelerometric data?

“…The measured total acceleration involves both acceleration due to movement and acceleration due to gravity. Accelerometers have become a valuable method of assessing human motion in clinical research and everyday life (Mathie et al, 2004; Plasqui and Westerterp, 2007). They are small, unobtrusive, and light-weight, and they are also able to record inactivity.…”

“…However, the dose–response, i.e., the optimal amount, intensity, frequency, and duration of bone exercise, is far less known, because of technological challenges for long-term evaluation of the osteogenic features of exercise in population-based studies. Accelerometer-based measurement of body movement is an accepted method for monitoring physical activity (Mathie et al, 2004; Chen and Bassett, 2005). The method can also be used to assess the mechanical loading of bones, and thus for optimizing exercises to prevent osteoporosis.…”