Waveletkkkk analysis has emerged as a cornerstone in processing cardiac signals for biometric authentication systems. The prevailing trend in research has been to experiment with multiple wavelets, ultimately selecting and reporting a single best wavelet. However, this approach often falls short of accommodating the unique signal variations observed in individuals with heart conditions and those exhibiting irregularities in their cardiac signals. Our research introduces the Pulse-to-Pair method, a cross-modality authentication method that utilizes ECG and SCG signals. Through rigorous testing of eighty-nine wavelets on a dataset featuring heart signals from individuals with valvular heart diseases (VHDs), our findings reveal a striking limitation: a generic, one-size-fits-all wavelet fails to successfully authenticate 60% of subjects. This insight underscores the necessity for a tailored approach to wavelet selection before authentication, and we outline a method to identify the most appropriate wavelet for each subject quickly. To tackle the inherent noise in cross-modality scenarios, we propose a hash-based Inter-Pulse Interval (IPI) alignment algorithm. This significantly reduces authentication failures and boosts key generation rates by effectively addressing noise in multimodal authentication systems. It reduces failed authentication attempts by 90% for 128-bit secrets and increases key generation efficiency fivefold.