Background-Early recognition of heart disease is an important goal in pediatrics. Efforts in developing an inexpensive screening device that can assist in the differentiation between innocent and pathological heart murmurs have met with limited success. Artificial neural networks (ANNs) are valuable tools used in complex pattern recognition and classification tasks. The aim of the present study was to train an ANN to distinguish between innocent and pathological murmurs effectively. Methods and Results-Using an electronic stethoscope, heart sounds were recorded from 69 patients (37 pathological and 32 innocent murmurs). Sound samples were processed using digital signal analysis and fed into a custom ANN. With optimal settings, sensitivities and specificities of 100% were obtained on the data collected with the ANN classification system developed. For future unknowns, our results suggest the generalization would improve with better representation of all classes in the training data. Conclusion-We demonstrated that ANNs show significant potential in their use as an accurate diagnostic tool for the classification of heart sound data into innocent and pathological classes. This technology offers great promise for the development of a device for high-volume screening of children for heart disease.

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The modeling of the energy storage system (ESS) of a Hybrid Electric Vehicle (HEV) poses a considerable challenge. The problem is not amenable to physical modeling without simplifying assumptions that compromise the accuracy of such models. An alternative is to build conventional empirical models. Such models however, are time-consuming to build and are dataintensive. In this paper, we demonstrate the application of an artificial neural network (ANN) to modeling the ESS. The model maps the system's state-of-charge (SOC) and the vehicle's power requirement to the bus voltage and current. We show that neural network models can accurately capture the complex, non-linear correlations accurately. Further, we propose and deploy our new technique, Smart Select, for designing neural network training data. The underlying principle of Smart Select is to design training data such that it is uniformly distributed over the entire range of an appropriate ANN output variable, which is typically the variable that is most difficult to model. In this case, we selected training data that was uniformly distributed over the current range. We show that smart-select is economical in comparison with conventional techniques for selection of training data. Using this technique and our in-house neural network software (the CUANN), we developed an artificial neural network model (inputs=2, hidden neurons=3, outputs=2) utilizing only 4047 of the available 29,244 points. When validated on the remaining points, its predictive accuracy, measured by R-squared error, was 0.97. Finally, we describe the integration of the ESS neural network model into the MATLAB-SIMULINK environment of NREL's Advanced Vehicle Simulator (ADVISOR). iii Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to