Adaptive morphological modeling and optimal estimation for dynamic LVET measurement from seismocardiographic signals

Abstract

The dynamic left ventricular ejection time (LVET) represents a crucial cardiovascular health indicator. Seismocardiography (SCG) offers substantial advantages over conventional techniques, however, it is susceptible to motion artifacts and exhibits complex morphologies, posing challenges for accurate dynamic LVET measurement. This study proposes a synergistic approach that integrates adaptive morphological modeling with an optimal estimation technique to accurately measure dynamic LVET from SCG signals. Firstly, an active feature recognition method was proposed to automatically identify 10 morphological features, followed by modeling them using 10 Gaussian functions. Subsequently, the model results were fused with measured SCG through optimal estimation, involving adaptive updating of the measurement noise matrix. When evaluated against state-of-the-art algorithms on the open-source CEBS dataset (82997 heartbeats) and the experimental dataset (34181 heartbeats), the proposed algorithm exhibits outstanding performance, with a goodness of fit (R2) of 0.9272 and root-mean-square error (RMSE) of 13.808 ms, convincingly demonstrating its effectiveness and advancement.