부산대학교 도서관

The classification of heart sounds is of utmost importance in promptly identifying cardiovascular disorders, particularly in small primary healthcare clinics. Although significant advancements have been achieved in the classification of heart sounds recently, most of these developments rely on traditional segmented attributes and classifiers with limited depth. These traditional approaches to representing and classifying acoustic signals must be revised to capture the nuances of heart adequately sounds. They often face challenges in delivering accurate results due to the cardiac environment's complex and variable acoustic conditions. This study suggests an enhanced Mel-Frequency Cepstrum Coefficient (MFCC) feature-based technique for classifying heart sounds and a Long-Short Term Memory neural network (LSTM). The neural network receives MFCC-based features to perform feature learning, followed by the classification task. The experiment's findings show that the suggested technique performs effectively over a range of tolerance windows.