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Objective-To classify patients by age based upon information extracted from their electrocardiograms (ECGs). To develop and compare the performance of Bayesian classifiers.Methods and Material-We present a methodology for classifying patients according to statistical features extracted from their ECG signals using a genetically evolved Bayesian network classifier. Continuous signal feature variables are converted to a discrete symbolic form by thresholding, to lower the dimensionality of the signal. This simplifies calculation of conditional probability tables for the classifier, and makes the tables smaller. Two methods of network discovery from data were developed and compared: the first using a greedy hill-climb search and the second employed evolutionary computing using a genetic algorithm (GA).Results and Conclusions-The evolved Bayesian network performed better (86.25% AUC) than both the one developed using the greedy algorithm (65% AUC) and the naïve Bayesian classifier (84.75% AUC). The methodology for evolving the Bayesian classifier can be used to evolve Bayesian networks in general thereby identifying the dependencies among the variables of interest. Those dependencies are assumed to be non-existent by naïve Bayesian classifiers. Such a classifier can then be used for medical applications for diagnosis and prediction purposes.

The long term goal of our research at the Intelligent Robotic Laboratory at Vanderbilt University is to develop advanced intelligent robotic aid systems for human services. As a first step toward our goal, the current thrusts of our R&D are centered on the development of an intelligent robotic aid called the ISAC (Intelligent Soft Arm Control). In this paper, we describe the overall system architecture and current activities in intelligent control, adaptive/interactive control and task learning.

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