Accurate synchronization between electrocardiogram (ECG) and photoplethysmogram (PPG) recordings is crucial for reliable cardiovascular monitoring. This study compares a conventional linear correlation (LC) method with two nonlinear approaches: determinism (DET), derived from cross-recurrence plots, and normalized mutual information (NMI), calculated between a cross-distance matrix (CDM) and a reference distance matrix (DM). Using data from 13 subjects, we evaluated the alignment between heart rate variability (HRV) from ECG and pulse rate variability (PRV) from PPG. The NMI-based method, using the CDM from HRV-PRV and the DM from a reference HRV segment, consistently produced more stable and physiologically plausible alignments, outperforming LC in 31% of the cases. These findings underscore the advantages of nonlinear metrics for accurate alignment of multimodal cardiovascular signals.