Abstract
As the major cause of deaths worldwide, cardiovascular diseases are responsible for about 17.9 million deaths per year 1. Research on new technologies and methodologies allowed the acquisition of reliable data in several high income countries, however, in various developing countries, due to poverty and common scarcity of resources, this has not been reached yet. In this work, cardiovascular data acquired using cardiac auscultation is going to be used to detect cardiac murmurs through an innovative deep learning approach. The proposed screening algorithm was built using pre-trained models comprising Residual Neural Networks, namely Resnet50, and Visual Geometry Groups, such as VGG16 and VGG19. Furthermore, and up to our knowledge, our proposal is the first one that characterizes heart murmurs based on their frequency components, i.e. the murmur pitch. Such analysis may be used to augment the system's capability on detecting heart diseases. A novel decision-making function was also proposed regarding the murmur's pitch. From our experiments, low-pitch murmurs were more difficult to detect, with final f1-score values nearing the 0.40 value mark for all three models, while high-pitch murmurs presented an higher f1-score value of about 0.80. This might be due to the fact that the low-pitch share their respective frequency range with the normal and fundamental heart sounds, therefore making it harder for the model to correctly detect their presence whereas high-pitch murmurs' frequencies distance from the latter.