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Title: Do we really need a segmentation step in heart sound classification algorithms?
Authors: Oliveira, Jorge
Nogueira, Diogo
Renna, Francesco
Ferreira, Carlos
Jorge, Alípio M.
Coimbra, Miguel
Keywords: Cardiac auscultation
Cardiovascular diseases
Issue Date: 1-Nov-2021
Publisher: IEEE
Citation: Oliveira, J., Nogueira, D., Renna, F., Ferreira, C., Jorge, A. M., & Coimbra; M. (2021). Do we really need a segmentation step in heart sound classification algorithms?. In 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 31th October-4th November 2021 (pp. 286-289). https// Repositório Institucional UPT.
Abstract: Cardiac auscultation is the key screening procedure to detect and identify cardiovascular diseases (CVDs). One of many steps to automatically detect CVDs using auscultation, concerns the detection and delimitation of the heart sound boundaries, a process known as segmentation. Whether to include or not a segmentation step in the signal classification pipeline is nowadays a topic of discussion. Up to our knowledge, the outcome of a segmentation algorithm has been used almost exclusively to align the different signal segments according to the heartbeat. In this paper, the need for a heartbeat alignment step is tested and evaluated over different machine learning algorithms, including deep learning solutions. From the different classifiers tested, Gate Recurrent Unit (GRU) Network and Convolutional Neural Network (CNN) algorithms are shown to be the most robust. Namely, these algorithms can detect the presence of heart murmurs even without a heartbeat alignment step. Furthermore, Support Vector Machine (SVM) and Random Forest (RF) algorithms require an explicit segmentation step to effectively detect heart sounds and murmurs, the overall performance is expected drop approximately 5% on both cases.
ISSN: 978-1-7281-1179-7/21/$31.00
Appears in Collections:REMIT - Publicações em Livros de Atas Internacionais / Papers in International Proceedings

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