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Impedance pneumography (IP) is mainly used as a noninvasive method to measure respiratory rate, tidal volume or minute ventilation. It could also register flow-related signals, after differentiation, from spirometrybased forced vital capacity maneuvers or ambulatory-based signals reflecting flow values during natural activity. The aim of this paper is to assess the possibility of improving the accuracy of flow parameters calculated by IP, by using nonlinear neural network correction (as opposed to simple linear calibration), and to evaluate the impact of various calibration procedures and neural network configurations. Ten students carried out fixed static breathing sequences, for both calibration and testing. A reference pneumotachometer and the Pneumonitor 2 were used. The validation of calculating peak and mean flow value during each inspiration and expiration was considered. A mean accuracy of 80% was achieved for a separate neural network with two hidden layers with 10 neurons in each layer, trained individually for each subject and body position, using the data from the longest, fixed calibration procedure. Simple linear modeling achieved only 72.5%.

Flow Parameters Derived from Impedance Pneumography after Nonlinear Calibration based on Neural Networks