Evolutionary Optimization of Extended Kalman Filter Hyperparameters for Adaptive Data-Driven ABP Prediction Framework Inspired by Cardiovascular Digital-Twin Principles

Continuous arterial blood pressure (ABP) prediction is relevant to cardiovascular monitoring and development of adaptive patient-specific digital twins. This study presents an adaptive data-driven ABP prediction framework inspired by cardiovascular digital-twin principles for causal one-step-ahead prediction using a single physiological signal modality. The proposed methodology combines a seven-sample causal ABP representation, a compact 7–5–1 multilayer perceptron, recursive Extended Kalman Filter (EKF) adaptation of all neural-network weights and biases, and offline evolutionary optimization of EKF covariance hyperparameters. Model development and evaluation used invasive ABP recordings from five CHARIS subjects available in PhysioNet, with five non-overlapping 100 s windows per subject. A rotating 3/1/1 subject-wise protocol assigned three subjects to supervised MLP training, one independent subject to covariance-hyperparameter selection, and one completely unseen subject to final testing in each fold. A fixed MLP achieved a mean test RMSE of 3.4123 mmHg. Recursive EKF adaptation using identical manually selected covariance hyperparameters across folds reduced mean RMSE to 3.0033 mmHg, corresponding to an average improvement of 11.98%. Offline optimization of P0, Q0, and R0 further reduced mean RMSE to 2.1546 mmHg with Differential Evolution, 2.1548 mmHg with Particle Swarm Optimization, and 2.1546 mmHg with Genetic Algorithm, representing an overall improvement of approximately 35.9% relative to the fixed-MLP baseline. Despite differences in selected covariance values, all three evolutionary methods produced nearly identical final predictive performance. These results show that recursive neural-parameter adaptation and systematic covariance tuning provide complementary improvements in causal ABP prediction while preserving strict subject-level separation among training, hyperparameter selection, and final evaluation.