State-Space versus Linear Regression Models between ECG Leads

The first attempts to modeling relationships between electrocardiographic leads, were based on measuring lead vectors by using models of human torso (deterministic approach), and by estimating liner regression models between leads of interest (statistical models). Among the most recent attempts, one of the most prominent is the state-space models approach, because of better noise immunity compared to mean squared error estimated statistical models. This study uses state-space models to synthesize precordial leads and Frank leads, from leads I, II, and V1. The synthesis was evaluated with the linear correlation coefficient (CC) on 200 measurements from the Physionet's PTB diagnostic ECG database. The results show better performance of regression models (mean CC between 0.88 and 0.96) than the state-space models (mean CC between 0.78-0.86). The leads were not pre-aligned for the R-peaks, which can be the main cause for the lower performances of state-space models, as a previous study has also shown. Residual baseline wander (after filtering) was the dominant reason for not obtaining better synthesis results with both methods.