ICP-WAVES: Intracranial Pressure Waveform Analysis and Visualization for Enhanced Signal Processing.

OBJECTIVE: Intracranial pressure (ICP) waveform morphology reflects brain compliance and cerebrospinal fluid dynamics. Existing monitoring methods fail to fully capture complex temporal patterns nor enable real-time interactive analysis to improve clinical decision-making. METHODS: We trained a transformer-based foundation model to capture temporal dynamics and generate embeddings from physiological data. The model was fine-tuned on physiological waveform data from patients with intracerebral hemorrhage (ICH) admitted to Columbia University Irving Medical Center (CUIMC) and validated on two non-overlapping datasets: a) patients with cerebral external ventricular drainage (EVD) at CUIMC and b) a synthetic ICP dataset. The embeddings generated from the foundation model were used to train a support vector machine (SVM) classifier to classify different morphologies. Model performance was evaluated using area under the receiver operating curve (AUC) and confusion matrices, by splitting the dataset into training and testing. We developed a graphical user interface to enable ad-hoc analysis, fine-tune models, and visualize ICP trends. RESULTS: A total of 190 patients between March 2009 and August 2013 with ICH were included to fine-tune the foundation model with a median length of stay (LOS) of 5 [4-9.25] days, and a Glasgow Coma Scale (GCS) score of 11 [7-15]. 6s2 of these patients had ICP waveform data. A total of 23 other patients (train: 11, test 12) from January 2021 to August 2023 with EVD were used to train the SVM model to classify different ICP morphologies; with median LOS 17 [12-23] days, and GCS 7 [5 13]. Two trained experts (YL, GG) labeled 8406 ICP (train:3613, test: 4793) pulses. The model achieved AUCs of 0.90 for 3-peak compliant, 0.93 for single-peak non-compliant, and 0.78 for multi peak non-compliant waveforms. On simulated ICP data, the AUCs were 1.00 for all the waveform classes. However, the confusion matrix analysis revealed that 1-peak compliant waveforms were classified with 77.5% accuracy while all other categories had 100% accuracy. CONCLUSION: A deep learning-based foundation model optimized for analyzing invasive ICP waveforms can extract clinically relevant information about cerebral compliance. The performance was best for distinguishing compliant from non-compliant waveforms.