## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & \
\begin{tabular}{c} **Number of** \\ **Heart Cycles** \\ \end{tabular} & **Segmentation** &
\begin{tabular}{c} **Feature Extraction** \\ **(Number of Features)** \\ \end{tabular} & **Classifier** & **Results** \\ \hline
[25] & N:200; A:800 & Manually & MFCC + DWT (43 F) & SVM & A: 97\% \\ \hline
[26] & N: 2488; A: 665 & Not applicable & \
\begin{tabular}{c} Statistical domain, \\ Frequency domain and \\ MFCC (27 F) \\ \end{tabular} & XgBoost &
\begin{tabular}{c} S: 94.5\%; E: \\ 91.3\%; A: 92.9\% \\ \end{tabular} \\ \hline
[27] & N: 155; A: 133 & Manually & \
\begin{tabular}{c} LPC and MFCC \\ coefficients (80 F) \\ \end{tabular} & SVM; KNN &
\begin{tabular}{c} A: 94,6\%; E: \\ 98,6\%; S: 89.4\% \\ \end{tabular} \\ \hline
[28] & N: 2488; A: 665 & Manually & \begin{tabular}{c} 1D – Convolutional \\ neural network (CNN) \\ \end{tabular} & DNN & \
\begin{tabular}{c} S: 91.5\%; E: \\ 71.6\%; A: 87.5\% \\ \end{tabular} \\ \cline{3-6}  & & MFCC and 2D – CNN & DNN &
\begin{tabular}{c} S: 92.5\%; E: \\ 76.6\%; A: 89.3\% \\ \end{tabular} \\ \hline
[29] & N: 320; A: 141 & Not applicable &
\begin{tabular}{c} Long short-term \\ memory (LSTM) \\ \end{tabular} & DNN & A: 80.8\% \\ \hline
[30] & N: 399; A: 399 & [13] & \
\begin{tabular}{c} Time, time-frequency \\ and perceptual \\ domain. (90 F) \\ \end{tabular} & ANN &
\begin{tabular}{c} S: 90,1\%; E: \\ 93,1\% \\ \end{tabular} \\ \hline
[31] & N: 669; A: 722 & [13] & MFSC and CNN & DNN & A: 93,7\% \\ \hline
[32] & & [13] & \
\begin{tabular}{c} Time and frequency \\ domain, wavelet and \\ statistics (29 F) \\ \end{tabular} & RF + LB + CSS &
\begin{tabular}{c} S: 79.6\%; \\ E:80.6\% \\ \end{tabular} \\ \hline
[33] & & \
\begin{tabular}{c} Hilbert \\ transformation \\ \end{tabular} &
\begin{tabular}{c} Statistical properties, \\ Heart rate (53 F) \\ \end{tabular} & Logic rules & S: 91.3\%; E:77\% \\ \hline
[34] & N: 2488; A: 665 & [13] & \
\begin{tabular}{c} Time features and \\ MFCC (13 F) \\ \end{tabular} & SVM &
\begin{tabular}{c} S: 91.8\%; E: \\ 82\%; A: 97\% \\ \end{tabular} \\ \hline
[36] & & [13] & \
\begin{tabular}{c} Sparse coding and time \\ domain features (25 F) \\ \end{tabular} & SVM &
\begin{tabular}{c} S: 84.3\%; E: \\ 77.2\%; A: 80.7\% \\ \end{tabular} \\ \hline
[37] & & [13] & \begin{tabular}{c} Time domain, \\ frequency domain and \\ entropy (40 F) \\ \end{tabular} & \
\begin{tabular}{c} BP Neural \\ networks (40 F) \\ \end{tabular} &
\begin{tabular}{c} S: 68.3\%; E: \\ 94\%; A: 88.5\% \\ \end{tabular} \\ \hline
[38] & N: 115; A: 287 & \multirow{2}{*}{Manually} & Curve fitting, MFCC & \multirow{2}{*}{KNN} & A: 92\% \\ \cline{2-2} \cline{6-6}  & N: 7; A: 22 & & & & A: 81\% \\ \hline
[39] & N: 40; A: 80 & Manually & DWT (50 F) &
\begin{tabular}{c} KNN, Fuzzy \\ C-Means \\ \end{tabular} & A: 86.67\% \\ \hline
[40] & N: 336; A: 171 & \
\begin{tabular}{c} DWT and \\ Shannon \\ energy \\ \end{tabular} &
\begin{tabular}{c} Spectrogram and \\ tensor decomposition (100 F) \\ \end{tabular} & SVM & A: 83\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the state-of-the-art approaches. N: normal, A: abnormal, T: total, NN: does not specify the data, F: features, A: accuracy, E: specificity, S: sensibility, DWT: discrete wavelet transform, LPC: linear prediction coefficients, SVM: support vector machine, KNN: K-nearest neighbor, RF: random forest, LB: LogitBoost, CSS: cost-sensitive classification, DNN: dense neural network, ANN: artificial neural network. (SVM, KNN and random forest), obtaining an accuracy of 94.6%. In [30], a total of 90 features were extracted in the time domain, time-frequency and perceptual dimensions. These were used as the input to a two-layer feed forward neural network, achieving 90.1% sensitivity and 93.1% specificity in the validation process, using samples that present high sound quality. In [31], deep convolutional neural networks and MFCCs were used for the recognition of normal and abnormal heart sounds, achieving an overall accuracy of 84.15%.