No abstract

Cough generated infectious aerosols are of interest while developing strategies for the mitigation of disease risks ranging from the common cold to SARS. In this work, the velocity field of human cough was measured using particle image velocimetry (PIV). The project subjects (total 29) coughed into an enclosure seeded with stage fog. Cough flow velocity profiles, average widths of the cough jet, and maximum cough velocities were measured. Maximum cough velocities ranged from 1.5 m/s to 28.8 m/s. The average width of all coughs ranged between 35 to 45 mm. Wide variability in the data suggests that future cough simulations consider a range of conditions.

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.