Forecasting intracranial hypertension using multi-scale waveform metrics
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Objective: 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 ahead of time could be beneficial for the patient. Methods: We developed a prediction framework that forecasts onsets of intracranial hypertension in the next 8 hours. Its main innovation is the joint use of cerebral auto-regulation indices, spectral energies and morphological pulse metrics to describe the neurological state. 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 intracranial hypertension up to 8 hours in advance with alarm recall rates of 90 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, in which each of the three feature categories 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 prediction models for intracranial hypertension. 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.
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