## 4 Results
### Results of Automatic Classification of Heart Sounds  
Accuracy, specificity and sensitivity are the metrics used to evaluate the performance of each classifier. These values were calculated from Equations (5)-(7), respectively, where the values TP (true positive), TN (true negative), FP (false positive) and FN (false negative) are taken from a confusion matrix [57]. TP: Number of normal heart sounds that were classified as normal. TN: number of abnormal heart sounds that were classified as abnormal. FP: Number of abnormal heart sounds that were classified as normal. FN: Number of normal heart sounds that were classified as abnormal. \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}*100 \tag{5}\]  
\[Specificity=\frac{TN}{FP+TN}*100 \tag{6}\]  
\[Sensitivity=\frac{TP}{FN+TP}*100 \tag{7}\]  
This work makes the comparison of models [25; 26; 27] with the proposed method, since they used characteristics in the domains of time, time-frequency and perceptual domains and techniques used for audio recognition--specifically, the voice. Therefore, different techniques were implemented to extract the characteristics that the authors used in their works. Tables 7-9 describe in detail the characteristics used in [25; 26; 27], respectively. Regarding the classification stage, in [27], the SVM, KNN and random forest algorithms were used; in [25], deep neural networks and SVM were used, with latter showing the best performance; and in [26], the best performance was obtained using the XgBoost algorithm. Figure 7: Examples of segmentation using the proposed method. Heart sound signals for recordings (**A**) a031, (**B**) a0112, (**C**) a0284 and (**D**) a0352 are taken from the PhysioNet/CinC Challenge 2016, together with the successful segmentations using the proposed algorithm. In the case of the model in [28], 1D and 2D convolutional neural networks (CNN) were used. In the 1D-CNN model, the authors performed the normalization of 1000 samples for each cardiac signal in order to use it as the input to the model. For the 2D-CNN model, they extracted 12 MFCC features in each 30 ms frame, obtaining a 96 \(\times\) 12 feature matrix. Table 10 shows the configurations of the 1D-CNN and 2D-CNN model. Finally, in the model in [29], the authors used 12.5 s of each recording of heart sounds, making a total of 50,000 samples. Then, they applied a decimation with a factor of eight twice until they obtained a total of 782 samples in each recording. This model used recurrent neural networks, specifically long short-term memory (LSTM). Table 11 shows the model configuration. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Domain** & **Features** \\ \hline Perceptual & 19 Mel-frequency cepstral coefficient (MFCC) \\ \hline Time-frequency & 24 DWT features \\ \hline Perceptual + Time-frequency & 19 MFCC and 24 DWT features \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Features used in [25]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Features** \\ \hline Linear prediction coefficients (LPC) & 6 LPC per segment (S1, S2, systole and diastole) \\ \hline MFCC & 14 MFCC per segment (S1, S2, systole and diastole) \\ \hline LPC + MFCC & 6 LPC + 14 MFCC per segment (S1, S2, systole and diastole) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Features used in [27].