# Classification of Heart Sounds Using Convolutional Neural Network
## 1 Introduction  
Statistics show that cardiovascular disease (CVD) is one of the main reasons for mortality in the world [1, 2]. Heart sounds are a kind of mechanical vibrations caused by the movement of blood in the cardiovascular system [3] and they are considered to be an important indicator in the diagnosis of several CVDs. Compared with electrocardiograph (ECG) signals, a phonocardiogram (PCG), which is a graphical recording of heart sounds, can clearly express changes in the state of the heart. So PCG can be used to diagnose deformation in heart organs and damage to heart valves [4]. Auscultation is the major means by which clinicians listen to heart sounds. The results of an auscultation are then used by doctors to detect cardiac diseases. This process is subjective, time-consuming and requiresextensive training and experience in auscultation [5]. Therefore, it is necessary to develop a method for the automatic classification of heart sounds. The common approaches to heart sound or PCG classification include three steps--(1) feature extraction; (2) feature selection; and (3) classification by a classifier. The performance of PCG classification is influenced by all three of these processes. [PERSON] et al. [6] were the first researchers to attempt the automatic classification of a pathology in a PCG recording by the threshold-based method. In our previous work [7, 8], we, in Reference [7], extracted 324 features and used a back propagation neural network (BP, NN) for PCG classification. Furthermore, in Reference [8], we extracted 515 features from nine feature domains on the same dataset as used in the present studying and trained a support vector machine (SVM) with a radial basis kernel function for signals classification. [PERSON] et al. [9] applied an SVM and a deep neural network (DNN) to extract Mel Frequency Cepstral Coefficients (MFCCs) and Discrete Wavelets Transform (DWT) features from their own database and evaluated the contribution of each feature. Other researchers have worked on extracting features from heart sound signals; however, to the best of our knowledge, the features extracted in Reference [8] include almost all of the features that are referred to in other related research. Deep learning [10], especially for convolutional neural network (CNN), has been used to achieve great success in such fields, image detection [11, 12], speech recognition [13] and medical image and signal analysis [14]. Recently, many researchers have used deep learning methods to automatically classify heart sounds. A CNN was used as a feature extractor in Reference [15] and the features extracted by the CNN were fed into an SVM for heart sound classification. Time-frequency features of raw heart sound signals were fed into a CNN model to classify normal and abnormal heart sounds in References [16, 17]. In Reference [18], the authors used Mel-frequency spectral coefficients (MFSCs) features as inputs into a CNN model. [PERSON] et al. in Reference [19] proposed an ensemble of feature-based and CNN -based classifiers for heart sound classification tasks. [PERSON] et al. compared the classification performance between a CNN and two other classifiers (SVM and K-nearest Neighbor, (KNN)) based on the features extracted from the CNN. These authors also compared the results obtained from the same classifiers using different features extracted from a linear binary pattern (LBP) and the CNN [20]. All of these studies have made a great contribution to the field of automatic heart sound classification. However, some challenges remain in the field of heart sound classification:  
1. The extraction of new feature types from heart sounds. 2. New methods for heart sound classification. 3. imbalanced numbers of normal and abnormal heart sounds. Deep learning is a big data algorithm. However, we used a small number of dataset in this study, which would likely have led to overfitting if we employed a deep neural network [21].