Author, Institution: Basant Kumar Bajpai, Kaunas University of Technology
Science area, field of science: Technological Sciences, Measurement Engineering, T010
Scientific Supervisor: Prof. Dr. Habil. Arminas Ragauskas (Kaunas University of Technology, Technological Sciences, Measurement Engineering, T010)
Dissertation Defence Board of Measurement Engineering Science Field:
Prof. dr. Renaldas Raišutis (Kaunas University of Technology, Technological Sciences, Measurement Engineering, T010) – chairman
Dr. Asif Manzoor Khan (Aarhus University, Denmark, Medicine and Health Sciences, Medicine, M001)
Prof. dr. Vaidotas Marozas (Kaunas University of Technology, Technological Sciences, Measurement Engineering, T010)
Prof. Edmundas Širvinskas (Lithuanian University of Health Sciences, Medicine and Health Sciences, Medicine, M001)
Assoc. Prof. Dr. Reimondas Šliteris (Kaunas University of Technology, Technological Sciences, Measurement Engineering, T010)
The dissertation defence takes place online.
The doctoral dissertation is available on the internet and at the library of Kaunas University of Technology (K. Donelaičio g. 20, Kaunas).
Cerebral autoregulation (CA) is a process, which aims to maintain adequate and stable cerebral blood flow. Autoregulation has been explained as a balancing act between vasoconstriction and vasodilation as the cerebrovascular bed’s resistance accepts slow variations in CPP. CA’s impairment influences the patient outcomes most of all. Where the quality of slow arterial (ABP) and intracranial pressure (ICP) waves having major impact on CA status assessment reliability. Therefore, it is essential to provide better quality neurophysiological signals for CA assesment. The aim of the doctoral thesis was to develop a method for the extraction of a higher quality slow ABP(t) waves as a reference signal from the arterial line and to identify a slow wave of higher and acceptable quality for reliable, sensitive, and specific cerebral autoregulation monitoring by filtering the unwanted signals in both invasive and non-invasive autoregulation monitoring technologies by their reactivity index assessment in a cost-effective way. The sensitivity and specificity was estimated in 60 invasive patient data, where the relation between the sensitivity of the pressure reactivity index (PRx), traumatic brain injured (TBI) patient’s clinical outcome, and the quality of ABP and ICP slow waves shows that the FIR Parks–McClellan type filtering method (featuring a sensitivity of 70% and a specificity of 81%) was more sensitive towards autoregulation than the moving average filter (58% sensitivity and 72% specificity). Also, an alternative novel non – invasive volumetric reactivity index (VRx2) reflected from attenuation dynamics was developed and compared to already existing VRx1 based on ultrasonic time-of-flight (TOF) monitoring method. It has been shown that correlation coefficient of 0.769 can be achieved with a statistical significance p<0.0001 by the FIR (Parks–McClellan) filtering method. This reflects a significant correlation. Thus VRx2 can be used as a non – invasive CA index in the same way as VRx1. The attenuation based (VRx2) non – invasive CA monitoring technology is attractive because it is up to 3 times more cost effective than the TOF based (VRx1) CA monitoring while offering almost the same reliability as TOF CA monitoring. The most important finding of this doctoral thesis is the explanation of the improving slow ABP/ICP signal quality (sensitive and specific towards CA) for better re;iability of cerebrovascular autoregulation assessment by automatic elimination of artifacts in the arterial line and by the FIR (Parks–McClellan) filtering developed under this thesis. It has been demonstrated in the process of both invasive and non – invasive monitoring of subjects (patient/healthy volunteer) data analysis.