This paper presents a Systematic Literature Review (SLR) on hybrid noise-reduction methods applied to electrocardiography (ECG) and photoplethysmography (PPG) signals, combining modal decomposition techniques with adaptive filtering. The review followed a structured PICOC-based protocol and analyzed 798 records retrieved from seven databases, resulting in 20 primary studies published between 2020 and 2025. The findings indicate a clear shift from classical approaches, such as Empirical Mode Decomposition (EMD), toward more robust variants — CEEMDAN, ICEEMDAN, VMD, and emerging techniques such as TVF-EMD, IMD and mDMD — which mitigate mode mixing and offer improved stability for nonlinear and non-stationary signals. Most studies focus on clinical and wearable scenarios, particularly targeting the suppression of motion artifacts, PLI, BW, EMG, and impulsive noise. Publicly available datasets (MIT-BIH, NSTDB, PTB, fECG Challenge) are widely used, often complemented by proprietary acquisitions. Common performance metrics include SNR, MSE/RMSE, PRD, and correlation coefficients, with hybrid approaches frequently reporting significant improvements in SNR. Despite these advances, challenges remain, including an overreliance on synthetic noise, limited validation in continuous real-world conditions, and the lack of standardized metrics. Overall, hybrid architectures emerge as a promising and rapidly evolving strategy, especially for wearable applications and high-interference physiological monitoring.