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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.08612 |
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| _version_ | 1866909489908678656 |
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| author | Kataria, Saurabh Xiao, Ran Ruchti, Timothy Clark, Matthew Lu, Jiaying Lee, Randall J. Grunwell, Jocelyn Hu, Xiao |
| author_facet | Kataria, Saurabh Xiao, Ran Ruchti, Timothy Clark, Matthew Lu, Jiaying Lee, Randall J. Grunwell, Jocelyn Hu, Xiao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_08612 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Continuous Cardiac Arrest Prediction in ICU using PPG Foundation Model Kataria, Saurabh Xiao, Ran Ruchti, Timothy Clark, Matthew Lu, Jiaying Lee, Randall J. Grunwell, Jocelyn Hu, Xiao Machine Learning 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. |
| title | Continuous Cardiac Arrest Prediction in ICU using PPG Foundation Model |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.08612 |