Towards Smartphone-Based Heart Sound Classification

Globally prevalent, cardiovascular diseases (CVD) result in high annual mortality rates. Heart auscultation, though accessible and non-invasive, is $a$ difficult skill to acquire for accurate CVD diagnosis, and requires years of extensive training. To tackle this challenge, our study explores automatic heart sound classification to assist in the early detection of CVD. Our study collected cardiac sounds from 20 healthy individuals and 30 individuals with pathological heart conditions, compiling the dataset within a clinical environment using a smartphone. Employing transfer learning, we adapted the VGGish model for binary classification (healthy vs. pathological), achieving an accuracy of 95.0%. Feature extraction involved Mel spectrograms processed by a 26-layer VGGish model. The Grad-CAM method further enhanced interpretability by highlighting frequency regions deemed influential in the decision-making process.