Intelligent Diagnosis of Heart Murmurs in Children with Congenital Heart Disease


Wang J, You T, Yi K, Gong Y, Xie Q, Qu F, Wang B, He Z.J Healthc Eng. 2020 May 9;2020:9640821. doi: 10.1155/2020/9640821. eCollection 2020.PMID: 32454963 Free PMC article.



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.


Figure 1 Schematic of normal PCG signal and nomenclature.


Figure 2 Pathological distribution of 86 subjects with normal heart sound (normal), ventricular septal defect (VSD), atrial septal defect (ASD), patent foramen ovale (PFO), patent ductus arteriosus (PDA), double outlet right ventricle (DORV), and endocardial cushion defect (ECD).


Figure 3 Block diagram of PCG decomposition and recombination.


Figure 4 The structure of artificial neural network.

Figure 5 The recombined signal and its normalized average Shannon energy.


Figure 6 The result of PCG segmentation.


Figure 7 The time-frequency distribution of CAV and CSV in cycle #1, 2, 3. (a) The CAV and CSV signal. (b) The power spectral density of CAV and CSV.


Figure 8 The result of PCG classification.