Beschreibung:
<jats:title>Abstract</jats:title><jats:sec>
<jats:title>Background</jats:title>
<jats:p>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.</jats:p>
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<jats:title>Methods</jats:title>
<jats:p>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.</jats:p>
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<jats:title>Results</jats:title>
<jats:p>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.</jats:p>
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<jats:title>Conclusions</jats:title>
<jats:p>We provide the starting point for future researchers to use a computational approach to characterize ICP recordings from a wide cohort of disorders.</jats:p>
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