Multivariate analysis of intracranial pressure (ICP) signal using principal component analysis

The diagnosis and treatment of hydrocephalus and other neurological disorders often involve the acquisition and analysis of large amount of intracranial pressure (ICP) signal. Although the analysis and subsequent interpretation of this data is an essential part of the clinical management of the disorders, it is typically done manually by a trained clinician, and the difficulty in interpreting some of the features of this complex time series can sometimes lead to issues of subjectivity and reliability. This paper presents a method for the quantitative analysis of this data using a multivariate approach based on principal component analysis, with the aim of optimising symptom diagnosis, patient characterisation and treatment simulation and personalisation. In this method, 10 features are extracted from the ICP signal and principal components that represent these features are defined and analysed. Results from ICP traces of 40 patients show that the chosen features have relevant information about the ICP signal and can be represented with a few components of the PCA (approximately 91% of the total variance of the data is represented by the first four components of the PCA) and that these components can be helpful in characterising subgroups in the patient population that would otherwise not have been apparent. The introduction of supplementaty (non-ICP) variables has offered insight into additional groupings and relationships which may prove to be a fruitful avenue for exploration.