## 4 Discussion  
Since PCG signals contain information about the condition of the heart valves, the analysis and development of technology that allows the correct classification of these signals can be used as an auxiliary technique for the precise and timely diagnosis of HVDs. The information of the PCG signals has a temporality, relative intensity and spectral encoding, which invites us to use feature extraction techniques to enhance their contributions, facilitating their classification. To propose a novel method to detect HVDs, we decided to build a DL neural network that would receive as input a set of features extracted by CWT, MFCCs and the energy associated with each level of decomposition by DWT. Through the experimentation, the performance of each constituent part of the network was measured independently and in pairs for a multiclass and binary classification problem. The configurations with the best performance were those that generalized from the features extracted by MFCCs and CWT, scilicet, the complete or partial DNN model that consisted of two parallel CNNs. Nevertheless, the F1 scores and binary accuracy of the complete model reached values slightly higher than 95% and 99%, respectively, indicating that the energy associated with each level of decomposition by DWT contains information that CWT or MFCCs does not, making the complete model the most competitive architecture. In addition to the F1 score, one of the principal metrics to evaluate the competitiveness of the proposed model is the specificity, since this is the only one that takes into account the false positives of each class. In particular, it is of great interest to observe the specificity calculated in the \"Normal\" class, since a perfect score (100%) in this metric would ensure that no patient with any HVD would be classified as a healthy person, which is the worst-case scenario during diagnosis, and could mean a risk for the patient's health. We consider that, for clinical applications, this is the most important metric that must be accounted for and where this work stands out, having specificities in the \"Normal\" class of up to 99%. Figure 11: Experimental results: The bar graphs show the results achieved by each architecture, for each class, measured using precision, recall, F1 score and specificity as metrics. It should be noted that this work is not the first to address a similar problem; however, a fair comparison can only be made with those publications that have used the same database as us. Table 3 shows, in a comparative way, seven of these works (including the present one), which were given the task of applying artificial intelligence techniques to perform binary or multiclass classification. If we take the performance of the model proposed in the present work, achieved using 80% of the dataset as the training set and measured through the F1 score, and compare it with the F1 score of the work proposed by [9], or any global accuracy proposed in the other works, it would seem that our proposal is less competitive. However, there are certain neglected considerations in the mentioned works, which the present work addresses more thoroughly. First, it is possible to observe that the results reported in [9; 12; 14] are those obtained during training, since the sum of the data contained in the confusion matrices is the total of the data in the dataset. This has important implications when interpreting their results, since using 100% of the dataset during training allows us only to know the degree of compaction of the data and the memorization capacity of the classification algorithms, but it is not possible to measure the capability of the algorithm to generalize, as it has not been tested with unseen data. In order to compare our work with the state-of-the-art algorithms mentioned in Table 3, the performance of our model using 100% of the dataset as the training set was experimentally calculated, surpassing all, in both multiclass and binary classification.