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Heart murmur recognition and classification play an important role in the auscultative diagnosis. The method based on hidden markov model (HMM) was presented to recognize the heart murmur. The murmur was isolated on basis of the principle of wavelet analysis considering the time-frequency characteristics of the heart murmur. This method uses Mel frequency cepstral coefficient (MFCC) to extract representative features and develops hidden Markov model (HMM) for signal classification. The result shows that this method is able to recognize the murmur efficiently and superior to BP neural network (94.2% vs 82.8%). And the findings suggest that the method may have the potential to be used to assist doctors for a more objective diagnosis.
Objective: To investigate the correlation between radiation dose and radiation risk when patients are scanned by 64-slice spiral CT. Materials and Methods: SPSS 17.0 is used statistically for analyzing the patient’s scanning parameters, radiation dose of monitoring and examining the patients who are scanning of their abdomen, chest and pelvic in our affiliated hospital. Results: SPSS statistical analysis shows that the factor related to radiation dose is scanning layer; the basic characteristics such as height and heart rate don’t affect the patient’s scan dose directly. Conclusion: Increasing the delay time after injection can reduce the scan numbers and monitoring layers of the machine, thus reduce the patient’s radiation dose and tube’s exposure time.