22 Classification of Phonocardiograms with Convolutional Neural Networks Omer Deperlioglu Afyon Kocatepe University, Afyonkarahisar, Turkey Erenler Mahallesi, Gazlıgöl Yolu Rektörlük E Blok, 03200, Afyonkarahisar Merkez/Afyonkarahisar, Turkey Tel,: +90 272 228 13 50 deperlioglu@aku.edu.tr Abstract The diagnosis of heart diseases from heart sounds is a matter of many years. This is the effect of having too many people with heart diseases in the world. Studies on heart sounds are usually based on classification for helping doctors. In other words, these studies are a substructure of clinical decision support systems. In this study, three different heart sound data in the PASCAL Btraining data set such as normal, murmur, and extrasystole are classified. Phonocardiograms which were obtained from heart sounds in the data set were used for classification. Both Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) were used for classification to compare obtained results. In these studies, the obtained results show that the CNN classification gives the better result with 97.9% classification accuracy according to the results of ANN. Thus, CNN emerges as the ideal classification tool for the classification of heart sounds with variable characteristics. Keywords: Heart sounds classification, Artificial neural network, Phonocardiograms classifications, Convolutional neural network 1. Introduction One of the first causes of human deaths in recent years in our world is heart diseases or cardiovascular diseases. Phonocardiograms (PCG) and electrocardiograms (ECG) are usually used for the detection of heart diseases. The most common method used to detect heart diseases is to listen to the sounds produced by the contraction of the heart during blood pump. These sounds are called PCG. Doctors often use a stethoscope to listen or record heart sounds. But it does not always suffice to be able to diagnose heart diseases by simply listening or seeing a record of them. For this reason, studies on heart sounds or PCGs have been increased to make it easier for doctors to make a diagnosis (Deperlioglu, 2018; Bahekar et. al., 2017; Ali et. al., 2017; Shervegar et. al., 2017). Studies on heart sounds are usually based on classification. These classification studies are being done to create infrastructure for clinic decision support systems, which is the largest reference source for physicians. Naturally, a great majority of these studies are done to increase the classification success. There are the most common methods of improving classification success such as the segmentation of S1 and S2 sounds, determination of the peak values of S1 and S2 sounds, using different filtering methods, or using different classification techniques. For segmentation of S1 and S2 sounds of heart sounds, energy methods or comparison with ECG signals methods are usually used. In Deperlioglu's work, he proposed a practical method of segmentation by re-sampled energy method. He explained that this method is easier than other segmentation methods and that segmentation can be done efficiently (Deperlioglu, 2018). Choi and Jiang have made a comparative study about Shannon energy, and Hilbert transform and the cardiac sound characteristic waveform (Choi & Jiang., 2008). The algorithm proposed by Saini is an automatic detection of two dominant heart sounds based on a 3-order normalized mean Shannon energy envelope. This proposed automatic detection and analysis algorithm can effectively detect heart sounds S1 and S2 by reducing the effect of noises in heart sounds. Due to the fact that the signal and the envelope calculation was pre-processed, the noises in the heart sounds could be easily suppressed (Saini, 2016). In another study, Shannon-Energy-Envelope based Phonocardiogram Peak Spacing Analysis method was used to examine the characteristics of PCG and cardiac rhythms.