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 b...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.展开更多
Background: Dynamic subaortic stenosis occurs in differing situations, commonly with hypertrophic cardiomyopathy. Regardless of the underlying cause, the resulting murmurs usually possess a characteristic sound spectr...Background: Dynamic subaortic stenosis occurs in differing situations, commonly with hypertrophic cardiomyopathy. Regardless of the underlying cause, the resulting murmurs usually possess a characteristic sound spectral pattern, manifesting a sharp and high frequency peak occurring late in systole, often bearing a striking resemblance to the subaortic Doppler flow pattern. Methods: Murmurs found in thirty one subjects with dynamic subaortic stenosis were analyzed after having been recorded with a novel portable device capable of spectral and waveform sound displays. Results: All subjects manifested characteristic frequency patterns, consisting of high and sharp peaks occurring in late systole. With significant subaortic stenosis (resting subaortic flow velocity > 2 m/sec) this pattern was evident at rest. In the presence of little or no resting subaortic obstruction (< 2 m/sec) this pattern was produced regularly by the Valsalva maneuver. Conclusions: Dynamic subaortic stenosis produces a specific sound spectral pattern that may provide a basis for clinical evaluation, especially in early detection of this disorder and in screening situations.展开更多
文摘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.
文摘Background: Dynamic subaortic stenosis occurs in differing situations, commonly with hypertrophic cardiomyopathy. Regardless of the underlying cause, the resulting murmurs usually possess a characteristic sound spectral pattern, manifesting a sharp and high frequency peak occurring late in systole, often bearing a striking resemblance to the subaortic Doppler flow pattern. Methods: Murmurs found in thirty one subjects with dynamic subaortic stenosis were analyzed after having been recorded with a novel portable device capable of spectral and waveform sound displays. Results: All subjects manifested characteristic frequency patterns, consisting of high and sharp peaks occurring in late systole. With significant subaortic stenosis (resting subaortic flow velocity > 2 m/sec) this pattern was evident at rest. In the presence of little or no resting subaortic obstruction (< 2 m/sec) this pattern was produced regularly by the Valsalva maneuver. Conclusions: Dynamic subaortic stenosis produces a specific sound spectral pattern that may provide a basis for clinical evaluation, especially in early detection of this disorder and in screening situations.
