## 3 Methodology
### Dataset Acquisition  
Physionet, also known as \"Research Resources for Complex Physiologic Signals\", is a repository of extensive clinical and physiological data samples. The dataset, published under the Physionet CINC challenge 2016, contains PCG recordings ranging from 5 to 100 s, recorded under clinical or non-clinical conditions [43]. The dataset is a collection of eight independent heart sound recording datasets referenced in previous research and collected through various clinical and non-clinical settings. For simplicity, all the individual datasets are labeled 'a' through 'i' [11]. The PCG data sample recordings available in the challenge were collected from 1072 patients, along with information on their heart conditions. More than one recording sample is available from a single patient, as these were recorded from different chest locations as mentioned above; therefore, the overall available recordings are 4430. Additionally, the data sample is labeled with one of two values: abnormal or normal. Therefore, this qualifies the dataset for training the supervised machine learning classification model. Figure 3: Research methodology for classification of PCG signals. Moreover, the dataset has been divided into training and test datasets, which are mutually exclusive based on the patient records. The training dataset contains the recordings from 764 patients, with 3153 recordings. The test dataset contains the mutually exclusive records from datasets 'b' through 'e' and a complete dataset from the 'g' and 'i' datasets, consisting of 308 patients with 1277 recordings. Table 3 highlights the training and test dataset record statistics [29]:  
Based on the research analysis from the results reported in [29], datasets 'b' and 'c' contain the highest number of unsure recordings in both the training and test datasets. Here, the \"unsure\" recording label refers to audio with poor signal quality, but the dataset still contains a label for it, i.e., abnormal or normal. Also, training sets 'a', 'c', and 'd' contain the most abnormal cases, while training sets 'b', 'd', 'e', and 'f' contain the most normal cases. As reported in [11], the recordings labeled as abnormal may include the following pathologies: innocent or benign murmurs, aortic disease, coronary artery disease, mitral valve prolapse, and miscellaneous pathological conditions. Moreover, under pathologic recordings, the following conditions are mentioned: cardiomyopathies, congenital heart defects, arrhythmia, or valvular diseases. However, these are not specifically mentioned for each patient record. Our study specifically excluded datasets 'b' and 'c' due to a high prevalence of recordings marked as \"unsure,\" which indicates poor signal quality. According to the challenge data documentation, recordings labeled as 'unsure' are typically characterized by significant noise and artifacts, which can compromise the reliability of machine learning models when used for classification tasks. This work chose to exclude these subsets to ensure that the training data provided a cleaner and more consistent signal, which is crucial for the robustness of our classification model. Therefore, to accommodate the optimal proportion of the abnormal and normal cases for the neural network training and to avoid overfitting, this research will use the composition of the original datasets, as mentioned in Table 4. However, the categorizations of unsure signals are highlighted in Table 5 for analysis purposes to compose a balanced dataset, while for training the model, the audio signals identified as \"unsure\" are included in the training, along with their designated label of abnormal or normal. Similarly, the test dataset also included the unsure records for testing the model after training. This selection of datasets for the training of the model arguably yields a few advantages:  
1.