Intracranial pressure analysis software: A mapping study and proposal

Introduction Intracranial pressure (ICP) monitoring and analysis are techniques that are, each year, applied to millions of patients with pathologies with million of patients annually. The detection of the so called A and B-waves, and the analysis of subtle changes in C-waves, which are present in ICP waveform, may indicate decreased intracranial compliance, and may improve the clinical outcome. Despite the advances in the field of computerized data analysis, the visual screening of ICP continues to be the means principally employed to detect these waves. To the best of our knowledge, no review study has addressed automated ICP analysis in sufficient detail and a need to research the state of the art of ICP analysis has, therefore, been identified. Methodology This paper presents a systematic mapping study to provide answers to 7 research questions: publication time, venue and source trends, medical tasks undertaken, research methods used, computational systems developed, validation methodology, tools and systems employed for evaluation and research problems identified. An ICP software prototype is presented and evaluated as a consequence of the results. Results A total of 23 papers, published between 1990 and 2020, were selected from 6 online databases. After analyzing these papers, the following information was obtained: diagnosis and monitoring medical tasks were addressed to the same extent, and the main research method used was evaluation research. Several computational systems were identified in the papers, the main one being image classification, while the main analysis objective was single pulse analysis. Correlation with expert analysis was the most frequent validation method, and few of the papers stated the use of a published dataset. Few authors referred to the tools used to build or evaluate the proposed solutions. The most frequent research problem was the need for new analysis methods. These results have inspired us to propose a software prototype with which provide an automated solution that integrates ICP analysis and monitoring techniques. Conclusions The papers in this study were selected and classified with regard to ICP automated analysis methods. Several research gaps were identified, which the authors of this study have employed as a based on which to recommend future work. Furthermore, this study has identified the need for an empirical comparison between methods, which will require the use and development of certain standard metrics. An in-depth analysis conducted by means of systematic literature review is also required. The software prototype evaluation provided positive results, showing that the prototype may be a reliable system for A-wave detection.