Comparison of Time- and Frequency-Domain Methods for Assessing Brain Compliance in Brain-Injured Patients Monitored With Intraparenchymal Intracranial Pressure Sensors

Intracranial pressure (ICP) monitoring is commonly used in neuro-intensive care, but its utility may be limited by a suboptimal use. The brain pressure-volume relationship, a potential predictor of neurological health, is now approached using time-domain methods, which can be challenging to implement. Frequency-domain methods may offer an alternative, but their relationship with time-domain metrics remains unclear. This study compares time- and frequency-domain methods for assessing brain compliance and evaluates their real-time usability. A monocentric, prospective observational study was conducted in the neurological ICU of the Hospices Civils de Lyon, France, to evaluate markers of brain compliance. Adult patients with brain lesions requiring multimodal monitoring were included. Continuous high-density physiological data, including ICP, arterial blood pressure (ABP), end-tidal CO₂, and electrocardiogram (ECG), were collected for analysis. Some spontaneous ICP rise events were automatically detected based on heuristic criteria and used as brain compliance challenges to compare the co-evolution of metrics across multiple time windows. Time-domain (pulse shape-related metrics) and frequency-domain analyses (examining heart and respiratory components in ICP) were computed to assess the intracranial pressure-volume state. Statistical analyses were performed using linear mixed-effects modeling, adjusting for vasoactive and sedative medications and Spearman correlations. The study included 66 patients, with a mean age of 49.16 [38.38, 57.58] years. A total of 518 spontaneous ICP rise events were detected in 56 patients. Our findings revealed that: 1) frequency-domain metrics strongly correlated with time-domain metrics during these challenges (r > 0.8, p < 0.001), 2) frequency-domain metrics were significantly drastically less computationally demanding, and 3) the impact of the heart on ICP showed a significant correlation with the P2/P1 ratio (r = 0.391, p < 0.001) and other potential markers of brain compliance. In contrast, the impact of respiration on ICP was only marginally correlated with these markers. Frequency-domain analyses exploring the impact of cardiac activity on ICP map provide similarly informative value to more complex machine learning-based tools, but with the advantage of being much less computationally demanding. This makes this approach particularly suitable and intuitive for real-time clinical monitoring in hospital settings, where computational resources are often limited. Initials: VG, NE, GP, LB, FG, RC, CB, FC, AM, AR, RM, FD, TR, SG, BB Conception and Design: VG, NE, BB Data Collection : VG, NE, LB, GP, TR, FG, RC, CB, FC, FD, BB Data Analysis and Interpretation: VG, BB Methodology Development: VG, BB, SG Manuscript Drafting and Writing: VG, BB Supervision and Project Oversight: BB Funding Acquisition: BB, VG Visualization: VG, BB Critical Review and Editing: NE, GP, LB, FG, RC, CB, FC, AM, AR, RM, FD, TR, SG, BB Approval of the Final Version: All Software Development: VG, SG Ethical Approval and Regulatory Compliance: BB