On the Impact of Synchronous Electrocardiogram Signals for Heart Sounds Segmentation

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

Silva, A (Author) Other; Teixeira, R (Author) Other; Fontes-Carvalho R (Author) FMUP; Coimbra, M (Author) FCUP; Renna, F (Author) FCUP

Conference proceedings International

Title: 2023 45TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY, EMBC

Pages: 1-5

45th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

Sydney, AUSTRALIA, JUL 24-27, 2023

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Other information

Authenticus ID: P-00Z-X50

DOI: 10.1109/embc40787.2023.10341149

Abstract (EN): In this paper we study the heart sound segmentation problem using Deep Neural Networks. The impact of available electrocardiogram (ECG) signals in addition to phonocardiogram (PCG) signals is evaluated. To incorporate ECG, two different models considered, which are built upon a 1D U-net - an early fusion one that fuses ECG in an early processing stage, and a late fusion one that averages the probabilities obtained by two networks applied independently on PCG and ECG data. Results show that, in contrast with traditional uses of ECG for PCG gating, early fusion of PCG and ECG information can provide more robust heart sound segmentation. As a proof of concept, we use the publicly available PhysioNet dataset. Validation results provide, on average, a sensitivity of 97.2%, 94.5%, and 95.6% and a Positive Predictive Value of 97.5%, 96.2%, and 96.1% for Early-fusion, Late-fusion, and unimodal (PCG only) models, respectively, showing the advantages of combining both signals at early stages to segment heart sounds.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 5