Work

Robust Heart Sound Analysis With MFCC and Light Weight Convolutional Neural Network

Year: 2025

Type: article

Abstract: Objective: Heart sound analysis is essential for cardiovascular disorder classification. Traditional auscultation and rule-based methods require manual feature engineering and clinical expertise. This work proposes a CNN-based model for automated multiclass heart sound classification. Results: Using MFCC features extracted from segmented real-world recordings, the model classifies heart sounds into murmur, extrasystole, extrahls, artifact, and normal. It achieves 98.7% training accuracy and 91% validation accuracy, with strong precision and recall for normal and murmur classes, and a weighted F1-score of 0.91. Conclusions: The results show that the proposed MFCC-CNN framework is robust, generalizable, and suitable for automated auscultation and early cardiac screening

Source: IEEE Open Journal of Engineering in Medicine and Biology

DOI: 10.1109/ojemb.2025.3615395

Authors: Aliya Hasan, Mohammad Karim

Institution: University of Massachusetts Dartmouth

Language: English

Topic: Phonocardiography and Auscultation Techniques

Subfield: Pulmonary and Respiratory Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,075