Data recovery methods in composite similarity-based data fusion: Application to physiological vital signs

Abstract

The functionality of a statistical framework for unified multivariate vital signs data fusion is seen; its flexibility for switching between the two data spaces. This paper develops data recovery methods for data fusion built on composite similarity measures using three distinct variable specific weighting methods - mean, median, and Orthogonalized Gnanadesikan-Kettenring (OGK). Using spatial covariance parameters derived from empirical patient data, the method successfully exhibits the ability to accurately recover eight different vital signs from its fused counterpart. Performance evaluation using Root Mean Squared Recovery Error (RMSRE), Root Mean Absolute Recovery Error (RMARE), and Root Standardized Mean Absolute Fusion Error (RSMARE) demonstrated that all three weighting approaches achieved comparable and highly accurate results, with parameters converging within ±0.01 of 1.0. The recovered signals closely matched the original data patterns, preserving both long-term trends and short-term fluctuations. Notably, the method proved computationally efficient and robust across different weighting approaches, suggesting broad applicability in clinical settings. This approach offers a promising solution for dimensionality reduction in complex physiological datasets while maintaining clinical relevance, with potential applications in patient monitoring systems and physiological modelling.