Automatic and Robust Delineation of the Fiducial Points of the Seismocardiogram Signal for Non-invasive Estimation of Cardiac Time Intervals.

Objective: The purpose of this research was to design a delineation algorithm that could detect specific fiducial points of the seismocardiogram (SCG) signal with or without using the electrocardiogram (ECG) R-wave as the reference point. The detected fiducial points were used to estimate cardiac time intervals. Due to complexity and sensitivity of the SCG signal, the algorithm was designed to robustly discard the low-quality cardiac cycles, which are the ones that contain unrecognizable fiducial points.

Method: The algorithm was trained on a dataset containing 48,318 manually annotated cardiac cycles. It was then applied to three test datasets: 65 young healthy individuals (dataset 1), 15 individuals above 44 years old (dataset 2), and 25 patients with previous heart conditions (dataset 3).

Results: The algorithm accomplished high prediction accuracy with the rootmean- square-error of less than 5 ms for all the test datasets. The algorithm overall mean detection rate per individual recordings (DRI) were 74, 68, and 42 percent for the three test datasets when concurrent ECG and SCG were used. For the standalone SCG case, the mean DRI was 32, 14 and 21 percent.

Conclusion: When the proposed algorithm applied to concurrent ECG and SCG signals, the desired fiducial points of the SCG signal were successfully estimated with a high detection rate. For the standalone case, however, the algorithm achieved high prediction accuracy and detection rate for only the young individual dataset.

Significance: The presented algorithm could be used for accurate and non-invasive estimation of cardiac time intervals.