Automated pediatric cardiac auscultation
Most of the relevant and severe congenital cardiac malfunctions can be recognized in the neonatal period of a child’s life. The delayed recognition of a congenital heart defect may have a serious impact on the long-term outcome of the affected child. Experienced cardiologists can usually evaluate heart murmurs with a high sensitivity and specificity, although non-specialists, with less clinical experience, may have more difficulty. Although primary care physicians frequently encounter children with heart murmurs most of these murmurs are innocent. The aim of this project is to design an automated algorithm that can assist the primary care physician in screening and diagnosing pediatric patients with possible cardiac malfunctions. Although attempts have been made to automate screening by auscultation, no device is currently available to fulfill this function. Multiple indicators of pathology are nonetheless available from heart sounds and were elicited using several signal processing techniques. The three feature extraction algorithms (FEA’s) developed respectively made use of a Direct Ratio technique, a Wavelet analysis technique and a Knowledge based neural network technique. Several implementations of each technique are evaluated to identify the best performer. To test the performance of the various algorithms, the clinical auscultation sounds and ECG-data of 163 patients, aged between 2 months and 16 years, were digitized. Results presented show that the De-noised Jack-Knife neural network can classify 163 recordings with a sensitivity and specificity of 92 % and 92.9 % respectively. This study concludes that, in certain conditions, the developed automated auscultation algorithms show significant potential in their use as an alternative evaluation technique for the classification of heart sounds in normal (innocent) and pathological classes.