PhD defense by Isabel Martinez Tejada

Isabel Martinez Tejada

On October 22nd, Isabel Martinez Tejada successfully defended her PhD thesis with the title “Characterization of intracranial pressure signal.”

The PhD investigated long-term intracranial pressure following craniectomy and subsequent cranioplasty.

The research was conducted under the supervision of

  • Professor Marianne Juhler
  • Professor Jens E Wilhjelm
  • Morten Andresen, MD, PhD

Opponents on the thesis were

  • Associate Professor Sadasivan Puthusserypady,
    Technical University of Denmark
  • Professor Anders Eklund,
    Umeå University, Sweden
  • Associate Professor Anders Rosendal Korshøj,
    Aarhus University, Denmark

Thesis summary

With the development of telemetric monitoring devices in the last decade, ICP monitoring has become feasible in a broader clinical setting, with patients undergoing ICP monitoring with mobile equipment either in-hospital or in the home setting, where a larger variety of ICP waveforms exist. Currently, the identification of these waveforms, the so-called macro-patterns lasting seconds to minutes, is primarily based on visual inspection. The need for objective and more automated identification of these variations emerges as a potential tool for better understanding the physiological underpinnings of the patient’s clinical state.

During my PhD thesis, we investigated a new methodology that could serve as a foundation for future objective and reproducible macro-pattern identification in the ICP signal with the hope to better understand the morphological characteristics and distribution of these macro-patterns in the ICP signal and their clin ical significance.

First, we showed that the current terminology and descriptions of B-waves no longer adequately address the ICP waveforms found in the clinical practice today. Their origin is also unclear. Our results found that ICP B-waves, also observed in healthy subjects during sleep, are associated both with respiratory disturbances and vascular contribution of flow velocity in a limited frequency range.

Second, a new data quality pipeline was designed to integrate all data validation checks to ensure high data quality. The method was applied to ICP signals before macro-pattern identification, to mitigate the possibility of artefacts masking the true signal.

Finally, a method based on k-Shape clustering was developed to identify the most encountered macro-patterns in ICP signals. We found a total of seven macro-patterns—with varying occurrence and distribution—that described our ICP signals.

In conclusion, we proposed a new, objective, and more automated method to identify macro-patterns in ICP signals. Thanks to this method, disease entities are likely to be identifiable based on the internal distribution and weighting of specific ICP macro-patterns. This information aims at optimizing both disease and treatment identification.