Diagnosing Heart Diseases With One-Dimensional Evolutionary Neural Networks Based On Heart Sounds

This study aims to investigate the effectiveness of deep learning methods in classifying heart sounds (phonocardiograms - PCG). Early diagnosis of heart diseases is critical for improving patients' quality of life and reducing mortality rates. The project uses a comprehensive heart sounds database provided by PhysioNet/CinC Challenge 2016.The data is collected from various sources, enhancing the model's generalizability. During the feature extraction process, MFCC, Chroma, and Mel-Spectrogram features were extracted from the sound signals. A high-pass filter was applied for noise reduction.In the modeling phase, a 1D Convolutional Neural Network (CNN) was used. The model was evaluated using accuracy, classification report, and ROC-AUC scores. The results show that the proposed CNN model provides high accuracy in classifying heart sounds as normal or abnormal. This study represents a significant step towards developing an automatic system for early diagnosis of heart diseases.