Analysis & automatic classification of nuclear magnetic resonance signals

Abstract

The human brain consists of a myriad of chemical compounds critical to its functioning. A group of these compounds, collectively known as metabolites, have been a research interest for years because the pathogenesis of neurodegenerative diseases, a tumours classification, the effectiveness of a drug, etc., can be investigated via variations in brain metabolite concentration levels. Nuclear Magnetic Resonance Spectroscopy (NMRS) enables investigators to conduct non-invasive in vivo studies of metabolites in the human brain and the rest of the body. However a number of problems have hindered the usage of NMRS as a clinical diagnostic tool. One is the non-uniqueness of the most widely used analysis methods, i.e. as the parameters and/or prior knowledge data of an analysis method are changed, the results also change. A second problem is the lack of a method that can automatically classify the signal components estimated via signal decomposition based signal analysis methods. Additionally, some of the most widely used analysis methods, by virtue of their algorithms, intrinsically assume the nature of NMRS signals, e.g. stationary, linear, Lorentzian, etc. Hence, this thesis explores a new analysis approach, based on a theoretical and practical understanding of NMRS, that (a) avoids making assumptions about the nature of experimentally acquired NMRS signals, (b) relies on a unique decomposition analysis method, and (c) automatically classifies the estimated peaks of an analysis. Unique decomposition analysis was conducted via the rarely used unique and non-linear signal decomposition method − the Fast Pad´e Transform (FPT). The FPT is compared with the main decomposition based NMRS analysis methods via a detailed mathematical analysis, and a comparative analysis. Automatic classification was conducted via a novel classification method, which is introduced herein, and which is based on quantum mechanical predictions of metabolite NMRS behaviour.