Abstract : This thesis provides a comparative study related to various signal processing and Artificial Intelligence (AI) techniques applied to Phonocardiography signals (PCG) to automatically differentiate normal heart sounds from five types of common murmurs. This should help to provide a simple-to-use and cost-effective PCG-based system for the early detection of cardiovascular anomalies. The average detection rate for heart anomalies in our study was 92.14%, while the classification rate was 71.02%. This is achieved by using Deep Neural Network (DNN) classification with Hyperbolic Tangent (tanh) activation function, a 5-layer, and 100 neurons per layer configuration. This thesis shows that the Discrete Wavelet Transform (DWT) was found to be the best denoising algorithm and the Heart Sound Envelogram (HSE) was the best segmentation method for the PCG signal. Mel Frequency Cepstral Coefficients (MFCC) Features conjugated with DNN classification outperformed the Time and Frequency-Domain counterparts. In the framework of smart and preventative health care, the proposed system serves as a convenient home-care tool by providing potentially-ill individuals with vital early warnings and guiding them to cardiologists for more precise diagnoses