# Heart Sound Classification Using Wavelet Analysis Approaches and Ensemble of Deep Learning Models
## 2 Related Work  
Research using deep learning is actively underway in various fields, but especially in the medical field, which has been attracting attention in recent years, lesion segmentation through biosignals such as PCG, ECG, EMG, and medical images such as MRI, CT scan, and ultrasound, and research on disease prediction and diagnosis is in progress. [PERSON] [15] extracted features from a PCG signal through the Mel Frequency Cepstral Coefficient (MFCC) and Discrete Wavelets Transform (DWT) and fused the two features. The performance results were analyzed using SVM, KNN, and a Deep Neural Network (DNN) for model learning and classification. [PERSON] [8] divided the entire PCG signal into short time periods and merged the features extracted through high-order statistics, energy, frequency domain, and Mel Coefficients. Classification performance was evaluated using specific algorithms of the Decision Tree, Naive Bayes (NB), Fine Gaussian, KNN, and Ensemble Method models. [PERSON] [9] extracted features from a time-frequency matrix based on the Fourier-based Synchrosqueezing Transform (FSST) to classify normal and abnormal PCG. The performance was evaluated using an SVM classifier to classify normal and pathological signals. [PERSON]'s [16] proposed model consists of five stages. The first obtained a spectrogram through Mel-spectrogram from the audio signal, and the second used interpolation to generate new data. Third, the feature maps of the data were extracted through the Darknet53 architecture, fourth, the extracted feature maps were optimized using Relief as a feature selection method, and fifth, the obtained feature maps were classified using KNN, SVM, NB, Logistic Regression (LR), Random Forest (RF), Gradient Boosting Classifier (GBC), XGBoost, Light Gradient Boosting Machine (LGBM), and CatBoost model. [PERSON] [17] extracted features using MFCC from PCG signals to automate the recognition of normal and abnormal heart rates and used transfer learning Inception-ResNet-v2 as a CNN model. [PERSON] [18] proposed a PCG classification method based on WST and quality assessment. For feature extraction through WST, classification was conducted using an SVM classifier, and quality evaluation and normal and abnormal PCG were classified using the Root Mean Square of Successive Difference (RMSSD) and Ratio of Zero Crossing (RZC). [PERSON] [19] proposed three steps for heart sound classification: signal preprocessing, feature extraction, and classification. During preprocessing, noise was removed using a Band Pass Filter, and the length of all signals was set to the same 7 s. MFCC was used in the feature extraction process, and a CNN-based lightweight model with an Inverted Residual structure was used for heart sound classification. [PERSON] [20] used the PhysioNet/CinC 2016 Challenge dataset and the publicly available open heartsounds dataset. The publicly available open heartsounds dataset includes five classes: normal, aortic stenosis, mitral stenosis, mitral regurgitation, and mitral valve prolapse. To develop a cardiac diagnostic system, they proposed a hybrid model that combined the components of a CNN and LSTM. [PERSON] [4] used the MFCC algorithm to extract features from PCG signals by fusing the PhysioNet/CinC 2016 Challenge dataset, the PASCAL Classifying Heart Sounds Challenge dataset, and the Yassen dataset. The extracted features are classified as Normal, Noise, and Abnormal using a deep residual network. [PERSON] [21] proposed a Time-Frequency Domain DNN method to detect FHS Activity (FHSA) in PCG signals. The proposed method consists of a preprocessing step, Modified Gaussian Window-based Stockwell Transform (MGWST) step for time-frequency matrix evaluation, using the Shannon-Teager-Kaiser Energy (STKE) envelope, smoothing, and thresholding techniques to assess heart sound boundaries, TFD Shannon entropy (TFDSE) feature extraction step calculated through signal segmented components, and FHS component recognition step through a Stacked Autoencoder (SAE)-DNN model.