# Heart Sound Classification Using Wavelet Analysis Approaches and Ensemble of Deep Learning Models
###### Abstract  
Analyzing the condition and function of the heart is very important because cardiovascular diseases (CVDs) are responsible for high mortality rates worldwide and can lead to strokes and heart attacks; thus, early diagnosis and treatment are important. Phonocardiogram (PCG) signals can be used to analyze heart rate characteristics to detect heart health and detect heart-related diseases. In this paper, we propose a method for designing using wavelet analysis techniques and an ensemble of deep learning models from phonocardiogram (PCG) for heart sound classification. For this purpose, we use wavelet scattering transform (WST) and continuous wavelet transform (CWT) as the wavelet analysis approaches for 1D-convolutional neural network (CNN) and 2D-CNN modeling, respectively. These features are insensitive to translations of the input on an invariance scale and are continuous with respect to deformations. Furthermore, the ensemble model is combined with 1D-CNN and 2D-CNN. The proposed method consists of four stages: a preprocessing stage for dividing signals at regular intervals, a feature extraction stage through wavelet scattering transform (WST) and continuous wavelet transform (CWT), a design stage of individual 1D-CNN and 2D-CNN, and a classification stage of heart sound by the ensemble model. The datasets used for the experiment were the PhysioNet/CirC 2016 challenge dataset and the PASCAL classifying heart sounds challenge dataset. The performance evaluation is performed by precision, recall, F1-score, sensitivity, and specificity. The experimental results revealed that the proposed method showed good performance on two datasets in comparison to the previous methods. The ensemble method of the proposed deep learning model surpasses the performance of recent studies and is suitable for predicting and diagnosing heart-related diseases by classifying heart sounds through phonocardiogram (PCG) signals. heart sound classification; phonocardiogram signal; deep learning; convolutional neural network; ensemble +
Footnote †: journal:  
the PCG measurement method is noninvasive because it records heart sounds through a sensor and stethoscope, and can be measured in a simple and low-cost manner compared to other biological signal measurements. Because the heart plays essential roles and functions in survival, such as temperature control, nutrient delivery, blood pressure maintenance, and oxygen supply, information on the condition and function of the heart can be obtained through this organ, making it possible to diagnose cardiovascular diseases [4]. It is important to analyze PCG signals for early diagnosis and treatment of cardiovascular diseases. The PCG signal consisted of S1 (1 st heart sound), S2 (2 nd heart sound), S3 (3 rd heart sound), and S4 (4 th heart sound). S1 is the first heart sound during a heartbeat and is a signal produced when the mitral and tricuspid valves close when ventricular contraction begins. S2 is the second heart sound during the heartbeat and represents the signal that occurs when the ventricle ends systole and begins to relax, whereas S3 and S4 are low-frequency sounds that represent signals that appear in patients with cardiovascular disease [5; 6]. In this way, we aimed to achieve a more accurate diagnosis using deep learning models to analyze and diagnose cardiac function from normal and abnormal cardiac signals. PCG signals, which are biological signals, can be used to detect and classify heart diseases and abnormalities using machine and deep learning methods [7]. PCG sounds can be classified using machine learning classifiers such as the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) [8; 9]. An SVM is an algorithm that classifies classes by determining the optimal decision boundary for class classification. If the input data are nonlinear, they can be classified by converting them into a high-dimensional feature space using a kernel trick [10; 11].