  In vivo H nuclear magnetic resonance (NMR) spectroscopy is an important tool
for performing non-invasive quantitative assessments of brain tumour glucose
metabolism. Brain tumours are considered fast-growth tumours because of their
high rate of proliferation. In addition, tumour cells exhibit profound genetic,
biochemical and histological differences with respect to the original
non-transformed cell types. Therefore, there is strong interest from the
clinical investigator's point of view in understanding the role of brain
metabolites under normal and pathological conditions and especially in the
development of early tumour detection techniques. Unfortunately, current
diagnosis techniques ignore the dynamic aspects of these signals. It is largely
believed that temporal variations of NMR Spectra are simply due to noise or do
not carry enough information to be exploited by any reliable diagnosis
procedure. Thus, current diagnosis procedures are mainly based on empirical
observations extracted from single averaged spectra. In this paper, firstly a
machine learning framework for the analysis of NMR spectroscopy signals which
can exploit both static and dynamic aspects of these signals is introduced.
Secondly, the dynamics of the signals are further analyzed using elements from
chaos theory in order to understand their underlying structure. Furthermore, we
show that they exhibit rich chaotic dynamics suggesting the encoding of
metabolic pathway information.
