BOUATOUATA, Chakib2025-11-302025-11-302025-06-06http//localhost:8080/jspui/handle/123456789/13628work presents a comprehensive approach to classifying cardiac sound (PCG) signals into normal and abnormal categories using hybrid deep learning and ma- chine learning techniques. Our goal is to develop a solution based on artificial intelligence techniques. The study leverages the PhysioNet/CinC Challenge 2016 dataset, which consists of recordings from multiple environments and patient cases, introducing significant variability in signal quality, duration, and class balance. A robust preprocessing pipeline was implemented to normalize and segment the sig- nals by patient. Time-domain and frequency-domain features were extracted to improve representation. n our work, we proposed a hybrid architecture that inte- grates multiple models, long short-term memory (LSTM) networks to model tem- poral context, and an attention mechanism to weight the most informative heart- beats. The most relevant segments were selected using attention weights, forming the final input to a random forest classifier. Experimental results demonstrate that the proposed system achieves promising classification performance, proving its effectiveness in handling PCG variability and imbalanced real-world data. This work highlights the potential of hybrid deep learning methods in automated car- diac listening systemsenHeart sound , Phonocardiogram (PCG)„ Feature ,machine learning, Deep learning, CNN, LSTM, Attention mechanism, Random Forest, Sig- nal segmentation, PhysioNet/CinC 2016,SVM,padding,boostingThesis