Legé, D.; Gergelé, L.; Prud’homme, M.; Lapayre, J.-C.; Launey, Y.; Henriet, J. A Deep Learning-Based Automated Framework for Subpeak Designation on Intracranial Pressure Signals. Sensors2023, 23, 7834.
Legé, D.; Gergelé, L.; Prud’homme, M.; Lapayre, J.-C.; Launey, Y.; Henriet, J. A Deep Learning-Based Automated Framework for Subpeak Designation on Intracranial Pressure Signals. Sensors 2023, 23, 7834.
Legé, D.; Gergelé, L.; Prud’homme, M.; Lapayre, J.-C.; Launey, Y.; Henriet, J. A Deep Learning-Based Automated Framework for Subpeak Designation on Intracranial Pressure Signals. Sensors2023, 23, 7834.
Legé, D.; Gergelé, L.; Prud’homme, M.; Lapayre, J.-C.; Launey, Y.; Henriet, J. A Deep Learning-Based Automated Framework for Subpeak Designation on Intracranial Pressure Signals. Sensors 2023, 23, 7834.
Abstract
The intracranial pressure (ICP) signal, as monitored on patients in intensive care units, contains pulses of cardiac origin where P1 and P2 subpeaks can often be observed. When calculable, the ratio of their relative amplitudes is an indicator of the patient’s cerebral compliance. This characterization is particularly informative on the overall state of the cerebrospinal system. We developed a recurrent neural network-based framework for P2/P1 ratio computation that only takes a raw ICP signal as an input. Two tasks are performed, namely pulse classification and subpeak designation. Pulse classification was achieved with an area under the curve of 0.90 on a 4,344-pulse testing dataset, while the peak designation algorithm identified pulses with a P2/P1 ratio > 1 with a 97.3% accuracy. Although it still needs to be evaluated on a larger number of labeled recordings, our automated P2/P1 ratio calculation framework appears to be a promising tool for improving bedside monitoring devices.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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