Deep learning-based beat-to-beat delineation of heart sounds and fiducial points in seismocardiography.

ABSTRACT:

Introduction

The application of deep learning methods in automatic delineation of fiducial points in seismocardiography (SCG) on a beat-to-beat basis provides the possibility of obtaining a novel and comprehensive approach to assess and monitor myocardial mechanics and hemodynamic status. Therefore, the aim of this study was to develop an adaptive and data-driven algorithm for automatic delineation of 11 fiducial points in SCG.

Methods

SCG signals from subjects both with and without known cardiac disease (CD) were included. A semi-automatic annotation pipeline was prepared for effective annotation of fiducial points for each individual cardiac cycle, in which 42,452 individual beats from 198 subjects were annotated. A deep learning model with U-Net architecture was developed

SUBMITTER: Korsgaard E 

PROVIDER: S-EPMC12712800 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature