Ferramenta para avaliação neurológica em pacientes com hipertensão intracraniana

• Autor(a) principal: Silva, Gustavo Moreira da
• Data de Publicação: 2014
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFU
• Texto Completo: https://repositorio.ufu.br/handle/123456789/14584; https://doi.org/10.14393/ufu.di.2014.483
• topic: Perfusão cerebral; Pressão intracraniana; Processamento de sinais biomédicos; Volume intracraniano; Vasoreatividade intracraniana; Processamento de sinais - Técnicas digitais - Simulação (computadores); Cerebral perfusion; Intracranial pressure; Biomedical signal processing; Volume intracranial; Intracranial vasoreactivity; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA

The Intracranial hypertension (ICH) is a neurological condition that primarily affects patients with Traumatic Brain Injury (TBI), cerebrovascular accident (CVA) and hydrocephalus. Currently, the best treatment of this condition requires continuous brain monitoring of clinical parameters associated with cerebral perfusion. In this context, two variables are highly relevant in clinical evaluations: The Intracranial Pressure (ICP) and the Mean Arterial Pressure (MAP). The aim of this work is to develop a computer program that provides information on the waveform of the PIC and the cerebral perfusion. The system performs a digital signal processing of the PIC which is not found in multiparameter monitors that are commonly used in intracranial monitoring. The system interface displays the following parameters: ICP, MAP, Cerebral Perfusion Pressure (CPP), Cerebral vasoreactivity index (PRx) and evaluates the stationarity of the signal from PIC. Moreover, the system still detects waves Lundberg (A and B) and estimates the characteristics of stationarity and vasoreactivity. To evaluate the usability of the system a case study is conducted, in addition to extracting features of stationarity curve intracranial pressure. The evaluation of characteristics allowed us to observe some trends that may compose the HIC prediction tools.