Résumé:
work 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 systems
