This study presents an artificial intelligent-based method for processing phonocardiographic (PCG) signals of patients with ejection murmur to assess the underlying pathology initiating the murmur.

The method is based on our unique method for finding disease-related frequency bands in conjunction with a sophisticated statistical classifier. Children with aortic stenosis (AS), and pulmonary stenosis (PS) were the two patient groups subjected to the study, taking the healthy ones (no murmur) as the control group. 

PCG signals were acquired from 45 referrals to the Children's University Hospital, comprised of 15 individuals from each group; all were diagnosed by expert pediatric cardiologists according to the echocardiographic measurements together with the complementary tests.

 The accuracy of the method is evaluated to be 90% and 93.3% using the 5-fold and leave-one-out validation methods, respectively. The accuracy is slightly degraded to 86.7% and 93.3% when a Gaussian noise with signal to noise ratio of 20 dB is added to the PCG signals, exhibiting an acceptable immunity against the noise.

The method offered promising results to be used as a decision support system in primary healthcare centers or clinics.