Continuous Cardiac Arrest Prediction in ICU using PPG Foundation Model

• URL: http://arxiv.org/abs/2502.08612v1
• Date: Wed, 12 Feb 2025 18:01:04 GMT
• Title: Continuous Cardiac Arrest Prediction in ICU using PPG Foundation Model
• Authors: Saurabh Kataria, Ran Xiao, Timothy Ruchti, Matthew Clark, Jiaying Lu, Randall J. Lee, Jocelyn Grunwell, Xiao Hu,
• Abstract summary: Non-invasive patient monitoring for tracking and predicting acute health events is an emerging area of research.<n>We present IHCA prediction results in ICU patients using only unimodal (signal waveform) deep representations.<n>We also provide comprehensive analysis of our model through architectural tuning and PaCMAP visualization of patient health trajectory in latent space.
• Score: 6.469423282286416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Non-invasive patient monitoring for tracking and predicting adverse acute health events is an emerging area of research. We pursue in-hospital cardiac arrest (IHCA) prediction using only single-channel finger photoplethysmography (PPG) signals. Our proposed two-stage model Feature Extractor-Aggregator Network (FEAN) leverages powerful representations from pre-trained PPG foundation models (PPG-GPT of size up to 1 Billion) stacked with sequential classification models. We propose two FEAN variants ("1H", "FH") which use the latest one-hour and (max) 24-hour history to make decisions respectively. Our study is the first to present IHCA prediction results in ICU patients using only unimodal (continuous PPG signal) waveform deep representations. With our best model, we obtain an average of 0.79 AUROC over 24~h prediction window before CA event onset with our model peaking performance at 0.82 one hour before CA. We also provide a comprehensive analysis of our model through architectural tuning and PaCMAP visualization of patient health trajectory in latent space.

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