Do we really need a segmentation step in heart sound classification algorithms?

• Type: Article in International Conference Proceedings Book
• Date: 2021

Authors

Oliveira, J (Author) Other; Nogueira, D (Author) Other; Renna, F (Author) FCUP; Ferreira, C (Author) Other; Jorge, AM (Author) FCUP; Coimbra, M (Author) FCUP

Conference proceedings International

Title: 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2021, Mexico, November 1-5, 2021

Pages: 286-289

Indexing

ISI Web of Knowledge - 3 Citations

Scopus - 5 Citations

Other information

Authenticus ID: P-00V-WT1

DOI: 10.1109/embc46164.2021.9630559

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4