1. A computer-implemented method of classifying, a recorded, digitized acoustic heart sound signal of a patient, the heart sound signal having been pre-processed so as to be parsed into respective heart beat cycles with S1 and S2 heart sounds identified, the method being performed by a computing device and including the steps of: by a computing device, segmenting and extracting features from heart beat cycles making up the heart sound signal and compiling the extracted features into a feature vector, the extracted features including at least energy features extracted by utilizing a power spectrum calculation conducted on the heart beat cycles of the heart sound signal, and frequency domain features extracted by utilizing a frequency domain transformation of the heart beat cycles of the heart sound signal; feeding, by a computing device, the feature vector into a network of a plurality of artificial neural networks, each artificial neural network being associated with a specific pathology and being trained with at least features extracted from similarly pre-processed heart sound signals associated with the specific pathology; determining, by a computing device, whether the heart sound signal exemplifies features corresponding to the artificial neural network associated with any one or more of the pathologies; and classifying, by a computing device, the heart sound signal as normal or containing a murmur associated with one or more of the pathologies according to the determination.