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Identifier

000449725

Title

Graph signal processing and recurrence quantification techniques for the analysis of biomedical signal ensembles

Alternative Title

Επεξεργασία σήματος μέσω γράφων και ποσοτικές αναδρομικές τεχνικές για την ανάλυση συνόλων βιοσημάτων

Author

Πεντάρη, Αναστασία Β.

Thesis advisor

Τσακαλίδης, Παναγιώτης

Reviewer

Μαριάς, Κώστας
Παπανικολάου, Νικόλαος
Σίμος, Παναγιώτης
Τζαγκαράκης, Γεώργιος
Παπαδάκη, Ευφροσύνη
Ζερβάκης, Μιχάλης
Αργυρός, Αντώνιος

Abstract

As the field of brain monitoring is evolving rapidly, there is an increasing demand for innovative approaches to handle relevant signals. Recently, a powerful tool has employed the research interest, namely the graph signal processing, as it provides the opportunity to treat signal ensembles, in contrary to the conventional per-signal techniques. Electroencephalogram (EEG) signals belong to a biosignals’ family, which can naturally admit graph representations. However, a main disadvantage of these signals is that they often be corrupted by impulsive noise. The nature of this noise can be best characterized by heavy-tailed statistics, thus driving the conventional denoising methods to failure. To address this problem, an efficient regularized graph filtering method was proposed, based on the fractional lower-order moments which better adapt to heavy-tailed statistics. Human brain connectivity was also one of the main interests of this dissertation. The most well-established approach of describing the interrelations between pairs of brain regions is via the Pearson’s correlation. Nevertheless, brain functionality is mostly dynamic, a fact which attracted our research interest and led to an alternative procedure of approaching such interrelations. Specifically, cross recurrence quantification analysis is an efficient mathematical tool which can quantify the dynamic behavior of two time series via the analysis of their recurrence plots, thus leading to a group of features. The application of this method on a satisfying number of resting-state functional magnetic resonance imaging examinations, in the form of time series, proved to be more sensitive in recognizing significant interrelations among brain regions, than the conventional ones. Furthermore, we extended this application to the introduction of these features to brain networks. The construction of graphs, via a group of features which better describe the dynamic behavior of human brains, and their analysis via conventional graph-based methods, such as the small-world procedure, proved to be more effective than the existing tools, in the analysis of a set combined of healthy controls and neuropsychiatric lupus diseased subjects. Finally, we aimed to extend our analyses from signals to images, which were acquired through the Diffusion-Weighted magnetic resonance imaging technique. More specifically, a main issue of this technique is its long examination time, thus increasing the patients’ discomfort. An effective signal processing technique is the sparse representations. Sparse representations aim to undersample a quantity and via the use of a fully-trained dictionary reconstruct the missing values. In our case, this quantity was the so-called b-value, the most important quantity of this technique. Sparse representations proved to be promising to the improvement of the solution of this main biomedical issue.

Language

English

Subject

Brain network

Cross recurrence quantification analysis

DW-MRI

Dynamic brain functionality

Functional connectivity

Small-world analysis

Sparse representations

Topological connectivity

b-value