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

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 to detect 11 fiducial points and predict multiple time intervals in the SCG signal. The evaluation metrics were positive predictive value and sensitivity. Results The median positive predictive value and sensitivity of the algorithm ranged between 0.809 and 1.000 and 0.843 and 0.918 for different fiducial points, respectively. Conclusion A novel algorithm for automatic detection of 11 fiducial points in SCG was developed and tested in subjects both with and without CD.