The analysis of 10 normal and 51 mitral valve pathology making a total of 61 heart sound signals that were obtained with Littmann 4100 Digital Stethoscope were conducted in this study.Following the recorded sound sign...The analysis of 10 normal and 51 mitral valve pathology making a total of 61 heart sound signals that were obtained with Littmann 4100 Digital Stethoscope were conducted in this study.Following the recorded sound signals were denoised by using wavelet filters,the signals were applied bicoherence analysis that is an high order spectral analysis method.It has been demonstrated that varieties of mitral valve pathology could be determined by three-dimensional surfaces of bicoherence and maximum bicoherence values.展开更多
Heart sound signals are easy to introduce noise during the acquisition process, and traditional denoising algorithms always remove the characteristic information of the heart sound while removing the noise. The denois...Heart sound signals are easy to introduce noise during the acquisition process, and traditional denoising algorithms always remove the characteristic information of the heart sound while removing the noise. The denoising effects in turn affect the subsequent diagnosis results. So an improved algorithm based on variational mode decomposition (VMD) and wavelet threshold method is proposed. First, the number of decomposition modes <i>K</i> of the VMD is selected by analyzing the average instantaneous frequency curve of the different decomposition values, and the noisy heart sound is decomposed into <i>K</i> modes by the VMD algorithm. Then, the modes that need to be retained are decided by the energy curve of each mode. Finally, wavelet threshold denoising method is performed on the retained modes. Experiment simulation results show that under different signal-to-noise ratio conditions, the proposed method can improve heart sounds’ ratio of signal to noise and reduce the root mean square error. Compared with traditional algorithms, it has good noise suppression capabilities under different noise levels.展开更多
In the previous studies of heart sounds, the calculation model of small waveform is often used, and new waveform graph is formed through the decomposition and restructuring of small waveform so as to remove the noise ...In the previous studies of heart sounds, the calculation model of small waveform is often used, and new waveform graph is formed through the decomposition and restructuring of small waveform so as to remove the noise from the new waveform. There are a lot of shortcomings in the use of such a method. The features of new waveform are difficult to be controlled, and thus the noise generated by the wave and the interference of wave will be disturbed by the filter to certain degree. In this paper, the integrated faltering algorithm is introduced, and a wave can be used in the studied use of small waveform, and also the high-order algorithm in mathematics is used, so that the frequency is controlled in a certain range, the frequency of heart sounds to be interfered is effectively reduced, and also the harmonic harm generated by the waveform is considered. After the signal sources are protected with some technologies, the effect of filtering and denoising is eventually achieved.展开更多
In this paper, we demonstrate the prototype of a new stethoscope using laser technology to make the heart-beat signal “visible”. This heartbeat detection technique could overcome the limitation of the acoustic steth...In this paper, we demonstrate the prototype of a new stethoscope using laser technology to make the heart-beat signal “visible”. This heartbeat detection technique could overcome the limitation of the acoustic stethoscope brought by the poor ability of human ear to hear low frequency heart sounds. This is important, as valuable information from sub-audio sounds is present at frequencies below the range of human hearing. Moreover, the diagnostic accuracy of the acoustic stethoscope is also very sensitive to noise from immediate environment. In the prototype of laser-based stethoscope, the heartbeat signal is correlated to the optical spot of a laser beam reflected from a thin mirror attached to the patient’s chest skin. The motion of the mirror with the chest skin is generated by the heart sounds. A linear optical sensor is applied to detect and record the motion of the optical spot, from which the heartbeat signal in time-domain is extracted. The heartbeat signal is then transformed to frequency domain through digital signal processing. Both time-domain and frequency-domain signals are analyzed in order to classify different type of heart murmurs. In the prototype of the laser-based stethoscope, we use a heart-sound box to simulate the chest of a human being. The heart-sounds collected from real patients are applied to activate the vibration of the heart-sound box. We also analyze different heart murmur patterns based on the time-domain and frequency-domain heartbeat signals acquired from the optical system.展开更多
Congenital heart disease(CHD),the most prevalent congenital ailment,has seen advancements in the“dual indi-cator”screening program.This facilitates the early-stage diagnosis and treatment of children with CHD,subse-...Congenital heart disease(CHD),the most prevalent congenital ailment,has seen advancements in the“dual indi-cator”screening program.This facilitates the early-stage diagnosis and treatment of children with CHD,subse-quently enhancing their survival rates.While cardiac auscultation offers an objective reflection of cardiac abnormalities and function,its evaluation is significantly influenced by personal experience and external factors,rendering it susceptible to misdiagnosis and omission.In recent years,continuous progress in artificial intelli-gence(AI)has enabled the digital acquisition,storage,and analysis of heart sound signals,paving the way for intelligent CHD auscultation-assisted diagnostic technology.Although there has been a surge in studies based on machine learning(ML)within CHD auscultation and diagnostic technology,most remain in the algorithmic research phase,relying on the implementation of specific datasets that still await verification in the clinical envir-onment.This paper provides an overview of the current stage of AI-assisted cardiac sounds(CS)auscultation technology,outlining the applications and limitations of AI auscultation technology in the CHD domain.The aim is to foster further development and refinement of AI auscultation technology for enhanced applications in CHD.展开更多
An advanced signal processing technique, higher-order spectra, is proposed to in vestigate the nonlinear coupling phenomena of the heart sounds. To extract more higher-order information of the heart sounds, a non-Gaus...An advanced signal processing technique, higher-order spectra, is proposed to in vestigate the nonlinear coupling phenomena of the heart sounds. To extract more higher-order information of the heart sounds, a non-Gaussian AR model is selected for parametric bispectral estimation in analyzing several kinds of heart sounds. The non-Gaussian AR model of the sound signals is llsed to detect quadratic nonlinear interactions and to classify two patterns of heart sounds in terms of the parametric bispectral estimate. The bispectral cross-correlation is employed to the order determination of the model. Several real heart sound data are imple mented to show that the quadratic nonlinearity exist in both normal and clinical heart sounds.It was found that bispectral techniques are effective and useful tools in analyzing heart sounds and other acoustical signals展开更多
文摘The analysis of 10 normal and 51 mitral valve pathology making a total of 61 heart sound signals that were obtained with Littmann 4100 Digital Stethoscope were conducted in this study.Following the recorded sound signals were denoised by using wavelet filters,the signals were applied bicoherence analysis that is an high order spectral analysis method.It has been demonstrated that varieties of mitral valve pathology could be determined by three-dimensional surfaces of bicoherence and maximum bicoherence values.
文摘Heart sound signals are easy to introduce noise during the acquisition process, and traditional denoising algorithms always remove the characteristic information of the heart sound while removing the noise. The denoising effects in turn affect the subsequent diagnosis results. So an improved algorithm based on variational mode decomposition (VMD) and wavelet threshold method is proposed. First, the number of decomposition modes <i>K</i> of the VMD is selected by analyzing the average instantaneous frequency curve of the different decomposition values, and the noisy heart sound is decomposed into <i>K</i> modes by the VMD algorithm. Then, the modes that need to be retained are decided by the energy curve of each mode. Finally, wavelet threshold denoising method is performed on the retained modes. Experiment simulation results show that under different signal-to-noise ratio conditions, the proposed method can improve heart sounds’ ratio of signal to noise and reduce the root mean square error. Compared with traditional algorithms, it has good noise suppression capabilities under different noise levels.
文摘In the previous studies of heart sounds, the calculation model of small waveform is often used, and new waveform graph is formed through the decomposition and restructuring of small waveform so as to remove the noise from the new waveform. There are a lot of shortcomings in the use of such a method. The features of new waveform are difficult to be controlled, and thus the noise generated by the wave and the interference of wave will be disturbed by the filter to certain degree. In this paper, the integrated faltering algorithm is introduced, and a wave can be used in the studied use of small waveform, and also the high-order algorithm in mathematics is used, so that the frequency is controlled in a certain range, the frequency of heart sounds to be interfered is effectively reduced, and also the harmonic harm generated by the waveform is considered. After the signal sources are protected with some technologies, the effect of filtering and denoising is eventually achieved.
文摘In this paper, we demonstrate the prototype of a new stethoscope using laser technology to make the heart-beat signal “visible”. This heartbeat detection technique could overcome the limitation of the acoustic stethoscope brought by the poor ability of human ear to hear low frequency heart sounds. This is important, as valuable information from sub-audio sounds is present at frequencies below the range of human hearing. Moreover, the diagnostic accuracy of the acoustic stethoscope is also very sensitive to noise from immediate environment. In the prototype of laser-based stethoscope, the heartbeat signal is correlated to the optical spot of a laser beam reflected from a thin mirror attached to the patient’s chest skin. The motion of the mirror with the chest skin is generated by the heart sounds. A linear optical sensor is applied to detect and record the motion of the optical spot, from which the heartbeat signal in time-domain is extracted. The heartbeat signal is then transformed to frequency domain through digital signal processing. Both time-domain and frequency-domain signals are analyzed in order to classify different type of heart murmurs. In the prototype of the laser-based stethoscope, we use a heart-sound box to simulate the chest of a human being. The heart-sounds collected from real patients are applied to activate the vibration of the heart-sound box. We also analyze different heart murmur patterns based on the time-domain and frequency-domain heartbeat signals acquired from the optical system.
基金supported by Jiangsu Provincial Health Commission(Grant No.K2023036).
文摘Congenital heart disease(CHD),the most prevalent congenital ailment,has seen advancements in the“dual indi-cator”screening program.This facilitates the early-stage diagnosis and treatment of children with CHD,subse-quently enhancing their survival rates.While cardiac auscultation offers an objective reflection of cardiac abnormalities and function,its evaluation is significantly influenced by personal experience and external factors,rendering it susceptible to misdiagnosis and omission.In recent years,continuous progress in artificial intelli-gence(AI)has enabled the digital acquisition,storage,and analysis of heart sound signals,paving the way for intelligent CHD auscultation-assisted diagnostic technology.Although there has been a surge in studies based on machine learning(ML)within CHD auscultation and diagnostic technology,most remain in the algorithmic research phase,relying on the implementation of specific datasets that still await verification in the clinical envir-onment.This paper provides an overview of the current stage of AI-assisted cardiac sounds(CS)auscultation technology,outlining the applications and limitations of AI auscultation technology in the CHD domain.The aim is to foster further development and refinement of AI auscultation technology for enhanced applications in CHD.
文摘An advanced signal processing technique, higher-order spectra, is proposed to in vestigate the nonlinear coupling phenomena of the heart sounds. To extract more higher-order information of the heart sounds, a non-Gaussian AR model is selected for parametric bispectral estimation in analyzing several kinds of heart sounds. The non-Gaussian AR model of the sound signals is llsed to detect quadratic nonlinear interactions and to classify two patterns of heart sounds in terms of the parametric bispectral estimate. The bispectral cross-correlation is employed to the order determination of the model. Several real heart sound data are imple mented to show that the quadratic nonlinearity exist in both normal and clinical heart sounds.It was found that bispectral techniques are effective and useful tools in analyzing heart sounds and other acoustical signals
