Abstract
This paper presents a machine learning method to detect and discriminate between Aortic Stenosis (AS) and Bicuspid Aortic Valve (BAV) based on heart sound analysis. Differentiation between the two heart conditions is clinically important, but complicated if relying merely on the conventional auscultation. A novel form of the Time Growing Neural Network (TGNN) is introduced for the classification purpose. The method is applied to a dataset comprised of 87 children referrals to a university hospital, from which 50 individuals are healthy (with and without innocent murmur), and the rest are abnormal with either AS (15 individuals) or BAV (22 individuals). The baseline for comparison is a Time-Delayed Neural Network (TDNN) with the same size of the feature vector and the temporal frame. We used our original validation methods, named A-Test, which provides valuable information about structural risk and also learning capacity of any supervised classification method. A-Test is an elaborated version of K-Fold validation method, in a rather profound way. Performance of the TGNN is superior comparing to the presented TDNN, with an accuracy of 85.8% against 81.5%. This method can be integrated with our intelligent phonocardiography to serve as an enhanced assessment tool in hands of nurses or practitioners at primary healthcare centers.
