Noninvasive Intracranial Pressure Estimation Using Subspace System Identification and Bespoke Machine Learning Algorithms: A Learning-to-Rank Approach

2026-01-28

AI Summary

This study addresses the clinical challenge of intracranial pressure (ICP) monitoring in intensive care, which currently relies on invasive measurements, by proposing a novel non-invasive and accurate estimation method. The approach integrates subspace system identification with tailored machine learning to construct a cerebral hemodynamic model using non-invasive signals—namely arterial blood pressure, cerebral blood flow velocity, and ECG R-R intervals. Innovatively, it introduces a joint framework combining ranking constraints with convex optimization to formulate an error-mapping function for estimating mean ICP. This work represents the first integration of system identification with rank-constrained machine learning, substantially enhancing non-invasive ICP estimation performance: 31.88% of test samples achieved errors ≤2 mmHg, and 34.07% exhibited errors between 2–6 mmHg, demonstrating strong clinical feasibility.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Accurate noninvasive estimation of intracranial pressure (ICP) remains a major challenge in critical care. We developed a bespoke machine learning algorithm that integrates system identification and ranking-constrained optimization to estimate mean ICP from noninvasive signals. A machine learning framework was proposed to obtain accurate mean ICP values using arbitrary noninvasive signals. The subspace system identification algorithm is employed to identify cerebral hemodynamics models for ICP simulation using arterial blood pressure (ABP), cerebral blood velocity (CBv), and R-wave to R-wave interval (R-R interval) signals in a comprehensive database. A mapping function to describe the relationship between the features of noninvasive signals and the estimation errors is learned using innovative ranking constraints through convex optimization. Patients across multiple clinical settings were randomly split into testing and training datasets for performance evaluation of the mapping function. The results indicate that about 31.88% of testing entries achieved estimation errors within 2 mmHg and 34.07% of testing entries between 2 mmHg and 6 mmHg from the nonlinear mapping with constraints. Our results demonstrate the feasibility of the proposed noninvasive ICP estimation approach. Further validation and technical refinement are required before clinical deployment, but this work lays the foundation for safe and broadly accessible ICP monitoring in patients with acute brain injury and related conditions.

Problem

• intracranial pressure
• noninvasive estimation
• machine learning
• system identification
• cerebral hemodynamics

Innovation

• subspace system identification
• noninvasive intracranial pressure estimation
• learning-to-rank
• ranking-constrained optimization
• cerebral hemodynamics modeling