Automatic Cardiac Feature Point Annotation of Cardio-mechanical Signals from Valvular Heart Disease Patients Using Multi-modal Sensor Fusion

The study proposes an innovative ECG-free algorithm for the automatic annotation of cardiac feature points in seismo-cardiogram (SCG) signals from valvular heart disease (VHD) patients. The algorithm is based on multi-modality signals, obviating the need for manual SCG annotation. It consists of self-supervised learning and pseudo-label generation from ECG signals. Initially, the algorithm employs self-supervised learning to analyze the pseudo-periodic nature of SCG signals. Subsequently, pseudo-labels are established using synchronized ECG signals for model training. Finally, labeling tests are conducted with SCG signals alone. The training and testing datasets consist of samples from 100 VHD patients. Experiments demonstrate that the proposed algorithm achieves a precision of 98.94% and a recall of 97.44% on the test set, suggesting the feasibility of the proposed method. The presented framework would be essential for further fiducial point detection and analysis of cardio-mechanical signals, which might provide out-of-clinic evaluations of VHD in the future.