## 4 Results
### Results of Automatic Classification of Heart Sounds
\begin{table}
\begin{tabular}{c c c} \hline \hline
**1D-CNN Model** & \multicolumn{2}{c}{**2D-CNN Model**} \\ \hline
**Layer** & **Output Shape** & **Layer** & **Output Shape** \\ \hline Input & \(1000\times 1\) & Input & \(96\times 12\) \\ Conv (kernel = 6; strides = 1) & \(1000\times 8\) & Conv (kernel = 4; strides = 1) & \(96\times 12\times 16\) \\ Batch-Norm & \(1000\times 8\) & Batch-Norm & \(96\times 12\times 16\) \\ Activation Function (ReLu) & \(1000\times 8\) & Activation Function (ReLu) & \(96\times 12\times 16\) \\ MaxPool (kernel = 2; strides = 2) & \(500\times 8\) & MaxPool (kernel = 2; stride = 2) & \(48\times 6\times 16\) \\ Conv (kernel = 6; strides = 1) & \(500\times 8\) & Conv (kernel = 4; strides = 1) & \(48\times 6\times 16\) \\ Dropout (0.4) & \(500\times 8\) & Dropout (0.5) & \(48\times 6\times 16\) \\ Activation Function (ReLu) & \(500\times 8\) & Activation Function (ReLu) & \(48\times 6\times 16\) \\ MaxPool (kernel = 2; strides = 2) & \(250\times 8\) & MaxPool (kernel = 2; strides = 2) & \(24\times 3\times 16\) \\ Conv (kernel = 6; strides = 1) & \(250\times 8\) & Conv (kernel = 4; strides = 1) & \(24\times 3\times 16\) \\ Dropout (0.4) & \(250\times 8\) & Dropout (0.5) & \(24\times 3\times 16\) \\ Activation Function (ReLu) & \(250\times 8\) & Activation Function. (ReLu) & \(24\times 3\times 16\) \\ MaxPool (kernel = 2; strides = 2) & \(125\times 8\) & MaxPool (kernel = 2; strides = 2) & \(12\times 1\times 16\) \\ Flatten & \(1000\) & Flatten & \(192\) \\ Dense & \(512\) & Dense & \(256\) \\ Dropout (0.4) & \(512\) & Dropout (0.4) & \(256\) \\ SoftMax & \(2\) & SoftMax & \(2\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Summary of 1D-CNN and 2D-CNN model configurations in [28]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Domain** & **Features** \\ \hline Statistical & Mean value, median value, standard deviation, mean absolute deviation, quartile 25, quartile 75, iqr, skewness, kurtosis, coefficient of variation \\ \hline Frequency & Entropy, dominant frequency value, dominant frequency magnitude, dominant frequency ratio \\ \hline Perceptual & 13 MFCC \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Features used in [26]. A total of 805 heart sounds (415 normal and 390 abnormal) from six databases were selected:  
- Samples taken from PhysioNet/Computing in Cardiology Challenge 2016 [20]. - Pascal challenge database [21]. - Database of the University of Michigan [58]. - Database of the University of Washington [59]. - Thinklabs database (digital stethoscope) [60]. - 3M database (digital stethoscope) [61]. In the models in [25; 26; 27], segmentation was performed manually for the identification of the cardiac cycle, S1 and S2 sounds and systolic and diastolic intervals, unlike the proposed method that uses the automatic segmentation method described in Section 2. In the case of the models proposed in [28], normalization was applied to 1000 samples in each cardiac cycle and the MFCC features were calculated. Furthermore, for the model in [29], the decimation process was carried out in each recording, until a total of 782 samples were obtained in each signal. Table 12 shows the comparison of the accuracy results between the methods in [25; 26; 27] and the proposed method using the different ML models. Tables 13-15 show the results of specificity, sensitivity and the area under the receiver operating characteristic curve (AUC), respectively. Table 16 presents the comparison of the results obtained with the proposed method using the KNN classifier and the models proposed in [28; 29]. Our method obtained an accuracy of 99.25%, a specificity of 100%, a sensitivity of 98.57% and an AUC of 91.81% using the KNN classifier, which was the best result obtained. Furthermore, with all other classifiers tested, our method ranks at or close to the top, suggesting that the proposed segmentation and feature extraction algorithms are indeed useful, irrespective of the classification model applied on their output.