###### Abstract  
Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, with a particularly high burden in India. Non-invasive methods like Phonocardiogram (PCG) analysis capture the acoustic activity of the heart. This holds significant potential for the early detection and diagnosis of heart conditions. However, the complexity and variability of PCG signals pose considerable challenges for accurate classification. Traditional methods of PCG signal analysis, including time-domain, frequency-domain, and time-frequency domain techniques, often fall short in capturing the intricate details necessary for reliable diagnosis. This study introduces an innovative approach that leverages harmonic-percussive source separation (HPSS) to extract distinct harmonic and percussive spectral features from PCG signals. These features are then utilized to train a deep feed-forward artificial neural network (ANN), classifying heart conditions as normal or abnormal. The methodology involves advanced digital signal processing techniques applied to PCG recordings from the PhysioNet 2016 dataset. The feature set comprises 164 attributes, including the Chroma STFT, Chroma CENS, Mel-frequency cepstral coefficients (MFCCs), and statistical features. These are refined using the ROC-AUC feature selection method to ensure optimal performance. The deep feed-forward ANN model was rigorously trained and validated on a balanced dataset. Techniques such as noise reduction and outlier detection were used to improve model training. The proposed model achieved a validation accuracy of 93.40% with sensitivity and specificity rates of 82.40% and 80.60%, respectively. These results underscore the effectiveness of harmonic-based features and the robustness of the ANN in heart sound classification. This research highlights the potential for deploying such models in non-invasive cardiac diagnostics, particularly in resource-constrained settings. It also lays the groundwork for future advancements in cardiac signal analysis.  
and geographical diversities like India, it is a big challenge to make such health professionals available on the scale necessary to support CVD-related healthcare for the affected population.  
Also, due to the recent COVID-19 pandemic, the number of CVD patients is expected to increase in the coming years [3]. This has motivated the research discussed in this manuscript, where the work related to the intelligent analysis of PCG recordings is presented. This work can further assist healthcare staff in diagnosis and telemedicine services, even in remote locations where only primary healthcare is available. In the past, few computer-assisted decision support systems (DSSs)/expert systems have assisted physicians in minimizing intra- and inter-observer variability. More heart sound data become available has helped practical algorithm developments and supported the cost-effective implementation of artificial intelligence (AI).  
Therefore, the use of AI presents an opportunity to address the shortfall problem mentioned in [2] and has been experimentally used by various groups of health organizations to reduce treatment recommendation times with minimal health professional intervention for diseases like diabetes [4], CVD [5], various types of cancers [6], etc. This use of AI minimizes errors and controls disease progression.