Interest in developing durable mitral valve repair methods is growing, underscoring the need to better understand the native mitral valve mechanics. In this study, the authors investigate the dynamic deformation of the mitral valve strut chordae-to-anterior leaflet transition zone using a novel stretch mapping method and report the complex mechanics of this region for the first time. Eight structurally normal porcine mitral valves were studied in a pulsatile left heart simulator under physiological hemodynamic conditions −120 mm peak transvalvular pressure, 5 l/min cardiac output at 70 bpm. The chordal insertion region was marked with a structured array of 31 miniature markers, and their motions throughout the cardiac cycle were tracked using two high speed cameras. 3D marker coordinates were calculated using direct linear transformation, and a second order continuous surface was fit to the marker cloud at each time frame. Average areal stretch, principal stretch magnitudes and directions, and stretch rates were computed, and temporal changes in each parameter were mapped over the insertion region. Stretch distribution was heterogeneous over the entire strut chordae insertion region, with the highest magnitudes along the edges of the chordal insertion region and the least along the axis of the strut chordae. At early systole, radial stretch was predominant, but by mid systole, significant stretch was observed in both radial and circumferential directions. The compressive stretches measured during systole indicate a strong coupling between the two principal directions, explaining the small magnitude of the systolic areal stretch. This study for the first time provides the dynamic kinematics of the strut chordae insertion region in the functioning mitral valve. A heterogeneous stretch pattern was measured, with the mechanics of this region governed by the complex underlying collagen architecture. The insertion region seemed to be under stretch during both systole and diastole, indicating a transfer of forces from the leaflets to the chordae and vice versa throughout the cardiac cycle, and demonstrating its role in optimal valve function.

Heart auscultation is a convenient tool for early diagnosis of heart diseases and is being developed to be an intelligent tool used in online medicine. Currently, there are few studies on intelligent diagnosis of pediatric murmurs due to congenital heart disease (CHD). The purpose of the study was to develop a method of intelligent diagnosis of pediatric CHD murmurs. Phonocardiogram (PCG) signals of 86 children were recorded with 24 children having normal heart sounds and 62 children having CHD murmurs. A segmentation method based on the discrete wavelet transform combined with Hadamard product was implemented to locate the first and the second heart sounds from the PCG signal. Ten features specific to CHD murmurs were extracted as the input of classifier after segmentation. Eighty-six artificial neural network classifiers were composed into a classification system to identify CHD murmurs. The accuracy, sensitivity, and specificity of diagnosis for heart murmurs were 93%, 93.5%, and 91.7%, respectively. In conclusion, a method of intelligent diagnosis of pediatric CHD murmurs is developed successfully and can be used for online screening of CHD in children.

Purpose: Children's heart sounds were denoised to improve the performance of the intelligent diagnosis. Methods: A combined noise reduction method based on variational modal decomposition (VMD) and wavelet soft threshold algorithm (WST) was proposed, and used to denoise 103 phonocardiogram samples. Features were extracted after denoising and employed for an intelligent diagnosis model to verify the effect of the denoising method. Results: The noise in children's phonocardiograms, especially crying noise, was suppressed. The signal-to-noise ratio obtained by the method for normal heart sounds was 14.69 dB at 5 dB Gaussian noise, which was higher than that obtained by WST only and the other VMD denoising method. Intelligent classification showed that the accuracy, sensitivity and specificity of the classification system for congenital heart diseases were 92.23, 92.42, and 91.89%, respectively and better than those with WST only. Conclusion: The proposed noise reduction method effectively eliminates noise in children's phonocardiograms and improves the performance of intelligent screening for the children with congenital heart diseases.