Forecasting intracranial hypertension using multi-scale waveform metrics. Hüser, M., Kündig, A., Karlen, W., De Luca, V., & Jaggi, M. Physiological Measurement, 41(1):014001, 2020. ![Forecasting intracranial hypertension using multi-scale waveform metrics [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex Objective: Acute intracranial hypertension is an important risk factor of secondary brain damage after traumatic brain injury. Hypertensive episodes are often diagnosed reactively and time is lost before counteractive measures are taken. A pro-active approach that predicts critical events several hours ahead of time could be beneficial for the patient. Methods: We developed a prediction framework that forecasts onsets of acute intracranial hypertension in the next 8 hours. It jointly uses cerebral auto-regulation indices, spectral energies and morphological pulse metrics to describe the neurological state of the patient. One-minute base windows were compressed by computing signal metrics, and then stored in a multi-scale history, from which physiological features were derived. Results: Our model predicted events up to 8 hours in advance with alarm recall rates of 90% at a precision of 36% in the MIMIC-II waveform database, improving upon two baselines from the literature. We found that features derived from high-frequency waveforms substantially improved the prediction performance over simple statistical summaries of low-frequency time series, and each of the three feature classes contributed to the performance gain. The inclusion of long-term history up to 8 hours was especially important. Conclusion: Our approach showed promising performance and enabled us to gain insights about the critical components of the prediction model. Significance: Our results highlight the importance of information contained in high-frequency waveforms in the neurological intensive care unit. They could motivate future studies on pre-hypertensive patterns and the design of new alarm algorithms for critical events in the injured brain.
@article{
title = {Forecasting intracranial hypertension using multi-scale waveform metrics},
type = {article},
year = {2020},
pages = {014001},
volume = {41},
websites = {http://arxiv.org/abs/1902.09499,https://iopscience.iop.org/article/10.1088/1361-6579/ab6360},
id = {45fa9fa2-d113-3407-8516-5c9664705125},
created = {2019-09-12T13:35:08.513Z},
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last_modified = {2022-09-04T18:12:23.207Z},
read = {false},
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authored = {true},
confirmed = {true},
hidden = {false},
citation_key = {Huser2019},
notes = {IF-2018: 2.25},
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abstract = {Objective: Acute intracranial hypertension is an important risk factor of secondary brain damage after traumatic brain injury. Hypertensive episodes are often diagnosed reactively and time is lost before counteractive measures are taken. A pro-active approach that predicts critical events several hours ahead of time could be beneficial for the patient. Methods: We developed a prediction framework that forecasts onsets of acute intracranial hypertension in the next 8 hours. It jointly uses cerebral auto-regulation indices, spectral energies and morphological pulse metrics to describe the neurological state of the patient. One-minute base windows were compressed by computing signal metrics, and then stored in a multi-scale history, from which physiological features were derived. Results: Our model predicted events up to 8 hours in advance with alarm recall rates of 90% at a precision of 36% in the MIMIC-II waveform database, improving upon two baselines from the literature. We found that features derived from high-frequency waveforms substantially improved the prediction performance over simple statistical summaries of low-frequency time series, and each of the three feature classes contributed to the performance gain. The inclusion of long-term history up to 8 hours was especially important. Conclusion: Our approach showed promising performance and enabled us to gain insights about the critical components of the prediction model. Significance: Our results highlight the importance of information contained in high-frequency waveforms in the neurological intensive care unit. They could motivate future studies on pre-hypertensive patterns and the design of new alarm algorithms for critical events in the injured brain.},
bibtype = {article},
author = {Hüser, Matthias and Kündig, Adrian and Karlen, Walter and De Luca, Valeria and Jaggi, Martin},
doi = {10.1088/1361-6579/ab6360},
journal = {Physiological Measurement},
number = {1}
}
Downloads: 0
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