Machine Learning-Based Intelligent Auscultation Techniques in Congenital Heart Disease:Application and Development

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.

The analysis of heart sounds by using bispectral technique

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

Detection of Mitral Valve Diseases by Bicoherence Analysis of Heart Sound Signals

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.

Research on Heart Sound Signal Denoising Algorithm Based on Variational Mode Decomposition and Wavelet Threshold

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 K 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 K 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.

Diagnosis of Coronary Artery Disease by Acoustic Analysis of Turbulent Murmur Caused by Coronary Artery Stenosis:A Single Center Study from China

Aim:Intracoronary murmur results from turbulent flow due to coronary artery narrowing.This study evaluated the diagnostic performance of a method for acoustic analysis of turbulent murmur caused by coronary artery stenosis in coronary artery disease(CAD)in Chinese populations.Method:Patients admitted to the cardiovascular department of the Sixth Medical Center of the Chinese People’s Liberation Army General Hospital between September 2021 and June 2022 for elective coronary angiography were prospectively enrolled.A digital electronic stethoscope was used to record heart sounds before angiography.Quantitative coronary angiography(QCA)served as the“gold standard”for CAD diagnosis to evaluate the diagnostic performance of the acoustic analysis method for CAD.Results:A total of 452 patients had complete QCA and heart sound data.The final interpretation results of the acoustic analysis method indicated 310 disease cases and 142 normal results.Increasing the cut-off values of coronary artery diameter stenosis from 30%to 50%,70%,and 90%increased the sensitivity and NPV of the acoustic analysis method;the sensitivity was 75.6%,81.9%,83.3%,and 85.7%,respectively;the NPV was 33.1%,57.0%,69.7%,and 88.0%,respectively;the specificity and PPV decreased(specificity of 75.8%,70.4%,51.0%,and 37.5%,respectively;PPV of 95.2%,89.0%,69.4%,and 32.9%,respectively);and the AUC values were 0.757,0.762,0.672,and 0.616,respectively.The sensitivity of the acoustic analysis method for one-vessel disease was 86.6%when the cut-off value was 50%.The sensitivity for identifying left anterior descending coronary artery lesions was best,at 90.7%.The sensitivity for identifying isolated coronary artery branch lesions was 66.7%,whereas the sensitivity for identifying three-vessel disease in multi-vessel coronary artery lesions was better,at 82.9%.Conclusion:Acoustic analysis of turbulent murmur caused by coronary artery stenosis for diagnosis of CAD may have favorable performance in the Chinese population.This method has good performance in CAD diagnosis with a cut-off coronary artery diameter for stenosis of 50%.

Validation of a Marker of Atrial Contraction in the SonR Signal

Introduction: The main component of the endocardial acceleration signal (SonR) is today used for cardiac resynchronization therapy (CRT) optimization. This prospective, single center pilot study focuses on another signal component, SonR4 that may provide further information on the atrial activity. Methods and Results: SonR signal and ECG tracings were recorded simultaneously during a CRT-D optimization procedure in 15 patients (12 men, 68 ± 9.5 years, ischemic heart disease 53%) indicated for CRT. Correlation between SonR4 signal, recorded using SonR and atrial contraction, identified by Echo Doppler was evaluated by Pearson and Student’s t tests under different Atrio-Ventricular (AV) delay programming. From 15 consecutive screened patients, 9 had concomitant analyzable SonR4 and ECG recordings and were included in the study population. The presence of the SonR4 component was systematically correlated to the presence of the A wave. A significant correlation was observed between SonR4 and A wave timings (r = 0.75, p = 0.02) according to different AV delays, with a high reproducibility in SonR4 assessment. Conclusion: A strong correlation between SonR4 and atrial contraction timings was observed, further suggesting that SonR4 is a marker of the atrial contraction. Additional assessments in larger populations are required to confirm these results and build further applications.