k-Shape clustering for extracting macro-patterns in intracranial pressure signals
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Background: Intracranial pressure (ICP) monitoring is a core component of neurosurgical diagnostics. With the introduction of telemetric monitoring devices in the last years, ICP monitoring has become feasible in a broader clinical setting including monitoring during full mobilization and at home, where a greater diversity of ICP waveforms are present. The need for identification of these variations, the so-called macro-patterns lasting seconds to minutes—emerges as a potential tool for better understanding the physiological underpinnings of patient symptoms. Methods: We introduce a new methodology that serves as a foundation for future automatic macro-pattern identification in the ICP signal to comprehensively understand the appearance and distribution of these macro-patterns in the ICP signal and their clinical significance. Specifically, we describe an algorithm based on k-Shape clustering to build a standard library of such macro-patterns. Results: In total, seven macro-patterns were extracted from the ICP signals. This macro-pattern library may be used as a basis for the classification of new ICP variation distributions based on clinical disease entities. Conclusions: We provide the starting point for future researchers to use a computational approach to characterize ICP recordings from a wide cohort of disorders.
Originalsprog | Engelsk |
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Artikelnummer | 12 |
Tidsskrift | Fluids and Barriers of the CNS |
Vol/bind | 19 |
Udgave nummer | 1 |
Antal sider | 13 |
ISSN | 2045-8118 |
DOI | |
Status | Udgivet - 2022 |
Bibliografisk note
Funding Information:
This project has received funding from the Novo Nordisk Tandem (NNF17OC0024718).
Publisher Copyright:
© 2022, The Author(s).
ID: 313613493