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Monitoring the well-being of a fetus through Fetal Phonocardiography (FPCG) has been occurring for more than a century. Throughout history, there have been continuous advances in sensor development, data acquisition systems, and signal processing techniques. Despite these advancements, FPCG based point of care technologies are facing serious challenges in translating from basic research to clinical trials and commercialization. This is partly due to the noisy characteristic associated with FPCG, to the lesser clinical knowledge about fetal and maternal physiological profiles, to the unavailability of gold standard databases, and to the limited application of reliable signal processing techniques. In order to understand why FPCG continues to be underutilized, it is necessary to know about the existing standards of fetal monitoring, data collection trends, and the signal processing aspects. To serve this purpose, this paper will first provide an overview of the existing standards of fetal monitoring and then provide a comprehensive survey on Fetal Phonocardiography with focus on trends in data collection, signal processing techniques and synthesis models that have been developed to date. Finally, a set of guidelines will be proposed for future research and use in signal analysis, processing and modeling based on the outlined challenges. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: An analysis for the optimum selection of image features in feature domain to represent lung nodules was performed, with implementation into a classification module of a computer-aided diagnosis system. Materials and Methods: Forty-two regions of interest obtained from 38 cases with effective diameters of 3 to 8.5 mm were used. On the basis of image characteristics and dimensionality, 11 features were computed. Nonparametric correlation coefficients, multiple regression analysis, and principal-component analysis were used to map the relation between the represented features from four radiologists and the computed features. An artificial neural network was used for the classification of benign and malignant nodules to test the hypothesis obtained from the mapping analysis. Results: Correlation coefficients ranging from 0.2693 to 0.5178 were obtained between the radiologists'' annotations and the computed features. Of the 11 features used, three were found to be redundant when both nodule and non-nodule cases were used, and five were found redundant when nodule or non-nodule cases were used. Combination of analysis from correlation coefficients, regression analysis, principal-component analysis, and the artificial neural network resulted in the selection of optimum features to achieve F-test values of 0.821 and 0.643 for malignant and benign nodules, respectively. Conclusion: This study demonstrates that for the optimum selection of features, each feature should be analyzed individually and collectively to evaluate the impact on the computer-aided diagnosis system on the basis of its class representation. This methodology will ultimately aid in improving the generalization capability of a classification module for early lung cancer diagnosis. [Copyright &y& Elsevier]