Selection of Stimulus and Measurement Schemes
نویسندگان
چکیده
EIT uses patterns of current stimulation and voltage measurement (stim & meas patterns) to create images, and it is clear that the choice of stim & meas patterns is critical to the quality of the reconstructed images. Optimal L1-, L2and L∞-norm schemes have been considered for circular, two-dimensional domains [1, 2]. Constructing optimal patterns that maximize the distinguishability of a conductivity contrast with a constrained total stimulation power (L2-norm) results in trigonometric patterns which use many stimulus electrodes simultaneously [3]. A restriction to pair-wise stimulus and measurement electrodes, common to many EIT hardware implementations, results in schemes such as the adjacent-drive and oppositedrive stim & meas patterns. Our conceptual approach is shown in fig. 1. Here, we seek image contrast changes in a “true" ROI, T , while not being confused by changes in nearby “false" ROIs, F1, F2, F3. If the EIT system makes measurements, m1,m2, then, including noise, the detected changes from each ROI are shown. Using Linear Discriminant Analysis (LDA), an optimal decision boundary can be defined, and a probability of error, p( ), of false detection is calculated. The quality of the pattern is defined by the maximum error probability. Stim & meas patterns can then be compared, where the best pattern minimizes the maximum probability of error p( ). An Initial stimulus and measurement pair can be selected (fig. 3) for a particular geometry (fig. 2) based on minimizing the maximum distinguishability z [4], but further choices are needed to balance sensitivity and specificity. FRAMEWORK
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