## 2 Related Literature  
Since 1994, significant research has been published that discusses approaches to simple classifications and identifying specific structural and functional abnormalities of the heart. The research literature published before 2016 was based on data collected from a small group of Outpatient Department (OPD) patients from specific hospitals. However, in 2016, a PhysioNet dataset [11] was published to the research community as a challenge to produce viable AI systems. This dataset is a repository of the best available PCG data of that time. Since then, this dataset has become a benchmark and is used in various research studies discussed further in the proposed methodology. The reported research is based on heart sound recordings obtained through various types of stethoscopes. The heart sounds are collected from any of four locations: the aortic area (right second intercostal space), pulmonic area (left second intercostal space), tricuspid area (left fourth intercostal space), and mitral area (left fifth intercostal space). While the heart sounds were being recorded, the environmental noise was also being recorded. To reduce such additional noise, various preprocessing techniques were used. Additionally, with few exceptions, the authors generated synthetic dataset samples by intentionally adding noise to the heart sound recording to measure the sensitivity of their presented solution. Moreover, during the investigation of the research literature, it was observed that various neural networks are been trained and tested with or without modification for the classification of FHS. Furthermore, recent research has emphasized the significance of anomaly detection in heart rate data. [PERSON] et al. [12], [PERSON] et al. [13], and [PERSON] et al. [14] highlighted the application of machine learning techniques for anomaly detection in heart rate using ECG. These methods can be extended to enhance the detection of irregular patterns in PCG signals. Table 2 summarizes the various types of neural networks that were reviewed in the scholarly work under investigation. \begin{table}
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**Author** & **Dataset** & **Model** & **Features** & **Results** \\ \hline [PERSON] et al. [15] & PhysioNet/CinC Challenge 2016 [11] & SVM, k-NN, CNN & Tempogram, Spectrogram, MFCC, ZCR & Acc: 88.84; Sens: NA; Spec: NA \\ \hline [PERSON] et al. [16] & PhysioNet Heart Sound Database 2016 [11] & Capsule Neural Network (CapsNet) & MFCC & Acc: 91.67; Sens: NA; Spec: NA \\ \hline [PERSON] et al. [17] & Voices of 142 individuals collected at Heart Institute of Sao Paulo University and Metropolitan Hospital of Paraiba & Artificial Neural Networks (ANNs) & Statistical parameters, amplitude, fundamental frequency, MFCC & Acc: 91.86; Sens: 88.1; Spec: 92.1 \\ \hline [PERSON] et al. [18] & PhysioNet/CinC Challenge 2016 [11] & CNN & MFCC, Log-MFCC & Acc: 91.74 \(\pm\) 3.72; Sens: 73.86; Spec: 70.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Models reported in the literature regarding PCG classification (acc: accuracy; sens: sensitivity; spec: specificity; NA: not available). Neural networks were trained using backpropagation [27; 33; 34; 35; 36; 37], with computational complexity and resource and time constraints influencing the choice of network architecture. Moreover, transfer learning [38] enables neural networks to extend knowledge from one domain to a related domain. However, when there is a substantial difference between the pre-trained model's dataset and the target dataset, it can lead to the transfer of irrelevant or uninformative features. Additionally, challenges such as insufficient fine-tuning data,  
\begin{table}
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architectural mismatches, and negative transfer may result in suboptimal performance or outcomes worse than a model trained from scratch.