# Heart Sound Classification Using Wavelet Analysis Approaches and Ensemble of Deep Learning Models
###### Abstract
KNN is an algorithm that classifies the k-closest classes of new data from training data using a distance metric [12]. Meanwhile, deep learning is a deep neural network comprising multiple hidden layers, and various network models can be used depending on the complexity and availability of the data. Deep learning is an artificial intelligence technology that is being actively researched in various fields, such as medicine, agriculture, robots, and self-driving cars. As it can automatically extract features and patterns from physiological signals and images, such as PCG, PCG, and EEG, it is suitable for processing data for disease classification, diagnosis, and lesion segmentation in the medical field. The Mel-Frequency Cepstral Coefficient (MFCC), which is widely used for PCG signal analysis, can effectively extract voice and audio signals by retaining important information and reducing dimensionality; however, since MFCC is weak in robustness to noise, it can affect various noises included in PCG acquisition [13]. Variable PCG signals contain various features depending on heart disease and appear in irregular patterns [14]. Due to this, there is a limitation that it is difficult to extract important information and characteristics. In addition, when using a single feature extraction method on PCG signals, considering the possibility of losing information owing to time changes, information, and features in various frequency domains, two types of analyses are possible: using wavelet analysis technology to extract features and classifying them using 1D-(Convolutional Neural Network) CNN and 2D-CNN. The wavelet scattering transformation method and continuous wavelet transformation method, which can extract information and characteristics about transformation invariance and put limitations in PCG signals, can be used to extract characteristics from information such as the period, intensity, and frequency of the heartbeat. In addition, through the 1D-CNN model, it is possible to learn the temporal and spatial features of the signal, and hierarchical feature learning that allows pattern analysis by layer, making it possible to analyze heartbeat patterns and analyze the health status of the heart. Not only can the time and frequency domain characteristics be analyzed through the 2D-CNN model using the image converted from the 1D PCG signal to the 2D time-frequency domain, but also a multi-resolution analysis is possible; thus, the abnormal parts of the signal can be analyzed. Since each model represents different characteristics, combining the predicted values of the two models allows you to analyze your heart health with accurate and reliable results. We propose a method for classifying heart sounds by the ensemble of the two classified models. The dataset used was the PhysioNet/CinC 2016 Challenge Dataset and the PASCAL Classifying Heart Sounds Challenge Dataset. Because the PCG signal appears differently for each person depending on the measurement method and the shape, size, and location of the heart and heart valves, the signal was divided at regular intervals to extract features. By extracting signal features from the segmented PCG signal through a Wavelet Scattering Transform (WST), a 1D-CNN suitable for the two datasets was designed. In addition, one-dimensional PCG signals are converted into two-dimensional images through CWT (Continuous Wavelet Transform), and among CNN's transfer learning models, GoogleNet, ResNet50, and ResNet101 are used. The performance evaluation method is performed by the precision, recall, F1-score, sensitivity, and specificity. It was confirmed that the ensemble method of 1D-CNN and 2D-CNN using two wavelet analysis techniques improved the heart sound classification performance compared to the single feature extraction method. The remainder of this paper is organized as follows.