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

This thesis develops novel mathematical signal processing methods for the analysis of biomedical signals. We address three distinct biomedical applications. First, we extend graph signal processing theory using fractional lower-order statistics in order to robustly denoise electroencephalogram (EEG) signals corrupted by additive heavy-tailed impulsive noise modeled with alpha-stable distributions. The proposed graph-based filter adapts to the statistics of the impulsive noise, due to head motions or the environment, and it suppresses its effects on EEG signals.The second part concerns the nonlinear dynamic analysis of resting-state functional magnetic resonance imaging (rs-fMRI) time series. As the human brain is highly dynamic mathematical methods should approach the human brain functionality in a dynamical manner. Recurrence is a fundamental property of dynamical systems, which can be exploited to characterize the system's behavior in phase space. We employ cross recurrence quantification analysis (CRQA) to exploit the recurrence plots and via their quantitative analysis extract specific features, which characterize the dynamic behavior of rs-fMRI recordings. We showed that this method is able to recognize regional brain interconnections allowing us to separate healthy controls from patients with neuropsychiatric systemic lupus erythematosus (NPSLE). We extended our approach to create brain networks through the linear combination of the individual CRQA-based features. The analysis of these networks through conventional graph theory of small-world led to a high classification accuracy.Finally, we addressed the problem of accelerating Diffusion-Weighted MRI (DW-MRI). DW-MRI is a non-invasive technique that provides information about the microstructure of human organs via long duration examinations. Decreasing DW-MRI examination time reduces patient discomfort. In DW-MRI, b-value is a factor that reflects the strength and timing of the gradients used to generate diffusion-weighted images. The higher the b-value, the stronger the diffusion effects. Through the application of a sparse representation method, we sampled a limited number of b-values and we reconstructed the rest through trained dictionaries. Experimental results on pancreas imaging showed the potential of our approach.Η διατριβή με τίτλο “Επεξεργασία σήματος μέσω γράφων και ποσοτικές αναδρομικές τεχνικές για την ανάλυση συνόλων βιοσημάτων” αποτελείται από τρία διαφορετικά τμήματα: Το πρώτο κομμάτι αφορά την εφαρμογή της θεωρίας επεξεργασίας σήματος μέσω γράφων σε σήματα ηλεκτροεγκεφαλογραφήματος (ΗΕΓ) με στόχο την αποθορυβοποίηση των ΗΕΓ από κρουστικό θόρυβο. Είναι γνωστό ότι τα ΗΕΓ επηρεάζονται από κρουστικό θόρυβο που μπορεί να οδηγήσει σε λανθασμένες κλινικές διαγνώσεις. Ο κρουστικός θόρυβος μπορεί να μοντελοποιηθεί μέσω της οικογένειας των αλφα-ευσταθών κατανομών. Σχεδιάσαμε και υλοποιήσαμε ένα γραφο-φίλτρο βασισμένο σε στατιστικές ροπές χαμηλής τάξης και προσαρμοσμένο σε κρουστικό θόρυβο, το οποίο μπορεί να μειώσει αποτελεσματικά θόρυβο προερχόμενο από τις κινήσεις του κεφαλιού ή το περιβάλλον.Το δεύτερο κομμάτι αφορά την μη-γραμμική δυναμική ανάλυση χρονοσειρών καταγεγραμμένων από την λειτουργική μαγνητική τομογραφία σε κατάσταση ηρεμίας. Ο ανθρώπινος εγκέφαλος έχει μία πολύ δυναμική φύση, επομένως η ανθρώπινη εγκεφαλική λειτουργία πρέπει να αναλύεται με τρόπο δυναμικό και προσαρμοστικό. Εφαρμόσαμε την αναδρομική ποσοτική ανάλυση ζευγών, μία μαθηματική μέθοδο που εκμεταλλεύεται τους πίνακες αναδρομής και διά μέσου της ποσοτικής ανάλυσής τους εξάγει συγκεκριμένα χαρακτηριστικά που περιγράφουν την δυναμική συμπεριφορά ενός συστήματος. Δείξαμε ότι η μέθοδος αυτή είναι ευαίσθητη στο να αναγνωρίζει περισσότερες συνδέσεις εγκεφαλικών περιοχών που διαφοροποιούν τους ασθενείς με νευροψυχιατρικό συστημικό ερυθηματώδη λύκο από τους υγιείς, σε σχέση με προηγούμενες μελέτες. Επιπλέον, η επεκτείναμε τη μέθοδο μέσω της δημιουργίας δικτύων εγκεφάλου εφαρμόζοντας ένα γραμμικό συνδυασμό των δυναμικών χαρακτηριστικών. Η ανάλυση των δικτύων αυτών πραγματοποιήθηκε μέσω της γνωστής γραφοθεωρίας του “μικρού-κόσμου” και οδήγησε σε υψηλά ποσοστά κατηγοριοποίησης.Τέλος, στα πλαίσια της διατριβής αυτής, ασχοληθήκαμε και με την μείωση του χρόνου εξέτασης της τεχνικής σταθμισμένης διάχυσης του μαγνητικού τομογράφου. Η σταθμισμένη διάχυση μαγνητικού τομογράφου είναι μία μη επεμβατική μέθοδος εξέτασης μικρο-αρχιτεκτονικής των ανθρώπινων οργάνων η οποία, ανάλογα με το όργανο, μπορεί να έχει μακρά διάρκεια. Κατά συνέπεια, στοχεύσαμε στην επιτάχυνση του χρόνου εξέτασης για τη μείωση της δυσφορίας των ασθενών εντός του μαγνητικού τομογράφου. Η πιο σημαντική παράμετρος της τεχνικής αυτής είναι η λεγόμενη b-τιμή. Σχεδιάσαμε έναν αλγόριθμο βασισμένο στη μέθοδο των αραιών αναπαραστάσεων, για να δειγματοληπτήσουμε ένα περιορισμένο αριθμό από b-τιμές ανακατασκευάζοντας τις υπόλοιπες μέσω εκπαιδευμένων λεξικών. Η αξιολόγηση πραγματοποιήθηκε σε πραγματικά δεδομένα απεικονίσεων του παγκρέατος και αποδείχθηκε πολλά υποσχόμενη

Speech emotion recognition via graph-based representations

Abstract Speech emotion recognition (SER) has gained an increased interest during the last decades as part of enriched affective computing. As a consequence, a variety of engineering approaches have been developed addressing the challenge of the SER problem, exploiting different features, learning algorithms, and datasets. In this paper, we propose the application of the graph theory for classifying emotionally-colored speech signals. Graph theory provides tools for extracting statistical as well as structural information from any time series. We propose to use the mentioned information as a novel feature set. Furthermore, we suggest setting a unique feature-based identity for each emotion belonging to each speaker. The emotion classification is performed by a Random Forest classifier in a Leave-One-Speaker-Out Cross Validation (LOSO-CV) scheme. The proposed method is compared with two state-of-the-art approaches involving well known hand-crafted features as well as deep learning architectures operating on mel-spectrograms. Experimental results on three datasets, EMODB (German, acted) and AESDD (Greek, acted), and DEMoS (Italian, in-the-wild), reveal that our proposed method outperforms the comparative methods in these datasets. Specifically, we observe an average UAR increase of almost $$18\%$$ 18 % , $$8\%$$ 8 % and $$13\%$$ 13 % , respectively

Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks

Multispectral sensors constitute a core Earth observation image technology generating massive high-dimensional observations. To address the communication and storage constraints of remote sensing platforms, lossy data compression becomes necessary, but it unavoidably introduces unwanted artifacts. In this work, we consider the encoding of multispectral observations into high-order tensor structures which can naturally capture multi-dimensional dependencies and correlations, and we propose a resource-efficient compression scheme based on quantized low-rank tensor completion. The proposed method is also applicable to the case of missing observations due to environmental conditions, such as cloud cover. To quantify the performance of compression, we consider both typical image quality metrics as well as the impact on state-of-the-art deep learning-based land-cover classification schemes. Experimental analysis on observations from the ESA Sentinel-2 satellite reveals that even minimal compression can have negative effects on classification performance which can be efficiently addressed by our proposed recovery scheme

A new synergistic relationship between xylan-active LPMO and xylobiohydrolase to tackle recalcitrant xylan

Background: Hemicellulose accounts for a significant part of plant biomass, and still poses a barrier to the efficient saccharification of lignocellulose. The recalcitrant part of hemicellulose is a serious impediment to the action of cellulases, despite the use of xylanases in the cellulolytic cocktail mixtures. However, the complexity and variety of hemicelluloses in different plant materials require the use of highly specific enzymes for a complete breakdown. Over the last few years, new fungal enzymes with novel activities on hemicelluloses have emerged. In the present study, we explored the synergistic relationships of the xylan-active AA14 lytic polysaccharide monooxygenase (LPMO), PcAA14B, with the recently discovered glucuronoxylan-specific xylanase TtXyn30A, of the (sub)family GH30_7, displaying xylobiohydrolaseactivity, and with commercial cellobiohydrolases, on pretreated natural lignocellulosic substrates.Results: PcAA14B and TtXyn30A showed a strong synergistic interaction on the degradation of the recalcitrant part of xylan. PcAA14B was able to increase the release of xylobiose from TtXyn30A, showing a degree of synergism (DS) of 3.8 on birchwood cellulosic fibers, and up to 5.7 on pretreated beechwood substrates. The increase in activity was dose- and time- dependent. A screening study on beechwood materials pretreated with different methods showed that the effect of the PcAA14B–TtXyn30A synergism was more prominent on substrates with low hemicellulose content, indicating that PcAA14B is mainly active on the recalcitrant part of xylan, which is in close proximity to the underlying cellulose fibers. Simultaneous addition of both enzymes resulted in higher DS than sequential addition. Moreover, PcAA14B was found to enhance cellobiose release from cellobiohydrolases during hydrolysis of pretreated lignocellulosic substrates, as well as microcrystalline cellulose. Conclusions: The results of the present study revealed a new synergistic relationship not only among two recently discovered xylan-active enzymes, the LPMO PcAA14B, and the GH30_7 glucuronoxylan-active xylobiohydrolase TtXyn30A, but also among PcAA14B and cellobiohydrolases. We hypothesize that PcAA14B creates free ends in the xylan polymer, which can be used as targets for the action of TtXyn30A. The results are of special importance for the design of next-generation enzymatic cocktails, able to efficiently remove hemicelluloses, allowing complete saccharification of cellulose in plant biomas

Ανάπτυξη και υλοποίηση αλγορίθμων βίντεο για αξιολόγηση φερτών στη ροή ποταμού

Περίληψη: Σε αυτήν τη διπλωματική εργασία στόχος είναι να γίνει μελέτη στο πεδίο της ψηφιακής επεξεργασίας εικόνας και μέσω αυτής να εντοπιστούν σε πρώτο επίπεδο, οι φερτές ύλες στη ροή του ποταμού για την μετέπειτα ανάλυση της ποιότητας του νερού ενώ σε δεύτερο στάδιο στόχος είναι η εκτίμηση της ταχύτητάς τους. Αυτό αφορά το πρώτο σκέλος αυτής της εργασίας. Το δεύτερο σκέλος αφορά τον εντοπισμό και την εκτίμηση της ταχύτητας των ερυθρών αιμοσφαιρίων για τη βοήθεια σε διάγνωση κάποιων ασθενειών. Η επεξεργασία των εικόνων έγινε στο εργαλείο προγραμματισμού Matlab. Στα πρώτα τέσσερα κεφάλαια γίνεται ανάλυση της βασικής εργασίας δηλαδή της μελέτης της μέσης πραγματικής ταχύτητας των αντικειμένων στη ροή ποταμού, ενώ στο πέμπτο κεφάλαιο γίνεται μικρότερη αλλά σημαντική ανάλυση της ταχύτητας των ερυθρών αιμοσφαιρίων, που ήταν συμπληρωματική εργασία

Changes in resting-state functional connectivity in neuropsychiatric lupus: A dynamic approach based on recurrence quantification analysis

peer reviewedThere is growing interest in dynamic approaches to functional brain connectivity (FC), and their potential applications in understanding atypical brain function. In this study, we assess the relative sensitivity of cross recurrence quantification analysis CRQA) to identify aberrant FC in patients with neuropsychiatric systemic lupus erythematosus (NPSLE) in comparison with conventional static and dynamic bivariate FC measures, as well as univariate (nodal) RQA. This technique was applied to resting-state fMRI data obtained from 45 NPSLE patients and 35 healthy volunteers (HC). Cross recurrence plots were computed for all pairs of 16 frontoparietal brain regions known to be critically involved in visuomotor control and suspected to show hemodynamic disturbance in NPSLE. Multivariate group comparisons revealed that the combination of six CRQA measures differentiated the two groups with large effect sizes (.214>η2>.061) in 40 out of the 120 region pairs. The majority of brain regions forming these pairs also showed group differences on nodal RQA indices (.146>η2>.09) Overall, larger values were found in NPSLE patients vs. HC with the exception of FC formed by the paracentral lobule. Determinism within five pairs of right-hemisphere sensorimotor regions (paracentral lobule, primary somatosensory, primary motor, and supplementary motor areas), correlated positively with visuomotor performance among NPSLE patients (pη2>.061), none of which correlated significantly with visuomotor performance. Indices derived from dynamic, temporal-based FC analyses displayed large effect sizes in 11/120 region pairs (.11>η2>.063). These findings further support the importance of feature-based dFC in advancing current knowledge on correlates of cognitive dysfunction in a clinically challenging disorder, such as NPSLE

Altered hippocampal connectivity dynamics predicts memory performance in neuropsychiatric lupus: a resting-state fMRI study using cross-recurrence quantification analysis

Objective Τo determine whole-brain and regional functional connectivity (FC) characteristics of patients with neuropsychiatric SLE (NPSLE) or without neuropsychiatric manifestations (non-NPSLE) and examine their association with cognitive performance.Methods Cross-recurrence quantification analysis (CRQA) of resting-state functional MRI (rs-fMRI) data was performed in 44 patients with NPSLE, 20 patients without NPSLE and 35 healthy controls (HCs). Volumetric analysis of total brain and specific cortical and subcortical regions, where significant connectivity changes were identified, was performed. Cognitive status of patients with NPSLE was assessed by neuropsychological tests. Group comparisons on nodal FC, global network metrics and regional volumetrics were conducted, and associations with cognitive performance were estimated (at p<0.05 false discovery rate corrected).Results FC in patients with NPSLE was characterised by increased modularity (mean (SD)=0.31 (0.06)) as compared with HCs (mean (SD)=0.27 (0.06); p=0.05), hypoconnectivity of the left (mean (SD)=0.06 (0.018)) and right hippocampi (mean (SD)=0.051 (0.0.16)), and of the right amygdala (mean (SD)=0.091 (0.039)), as compared with HCs (mean (SD)=0.075 (0.022), p=0.02; 0.065 (0.019), p=0.01; 0.14 (0.096), p=0.05, respectively). Hyperconnectivity of the left angular gyrus (NPSLE/HCs: mean (SD)=0.29 (0.26) and 0.10 (0.09); p=0.01), left (NPSLE/HCs: mean (SD)=0.16 (0.09) and 0.09 (0.05); p=0.01) and right superior parietal lobule (SPL) (NPSLE/HCs: mean (SD)=0.25 (0.19) and 0.13 (0.13), p=0.01) was noted in NPSLE versus HC groups. Among patients with NPSLE, verbal episodic memory scores were positively associated with connectivity (local efficiency) of the left hippocampus (r2=0.22, p=0.005) and negatively with local efficiency of the left angular gyrus (r2=0.24, p=0.003). Patients without NPSLE displayed hypoconnectivity of the right hippocampus (mean (SD)=0.056 (0.014)) and hyperconnectivity of the left angular gyrus (mean (SD)=0.25 (0.13)) and SPL (mean (SD)=0.17 (0.12)).Conclusion By using dynamic CRQA of the rs-fMRI data, distorted FC was found globally, as well as in medial temporal and parietal brain regions in patients with SLE, that correlated significantly and adversely with memory capacity in NPSLE. These results highlight the value of dynamic approaches to assessing impaired brain network function in patients with lupus with and without neuropsychiatric symptoms

Feature analysis on river flow video data for floating tracers detection

Summarization: This study focuses on a significant water quality problem that can be posed as the calculation of the distribution of suspended sediments in rivers. More specifically, we propose a method that performs tracking and motion estimation on river floating sediment tracers. The current work analyzes a river flow video sequence and isolates the sediment information in order to determine the temporal extend of the suspended sediment distribution. The method is based on a combination of image processing techniques and is performed through hue and intensity analysis. The results present the river sediment tracers isolated from the river water flow and the river background as well as the river floating tracers velocity vector field.Presented on

Epidemiology of intra-abdominal infection and sepsis in critically ill patients: "AbSeS", a multinational observational cohort study and ESICM Trials Group Project

PURPOSE: To describe the epidemiology of intra-abdominal infection in an international cohort of ICU patients according to a new system that classifies cases according to setting of infection acquisition (community-acquired, early onset hospital-acquired, and late-onset hospital-acquired), anatomical disruption (absent or present with localized or diffuse peritonitis), and severity of disease expression (infection, sepsis, and septic shock). METHODS: We performed a multicenter (n = 309), observational, epidemiological study including adult ICU patients diagnosed with intra-abdominal infection. Risk factors for mortality were assessed by logistic regression analysis. RESULTS: The cohort included 2621 patients. Setting of infection acquisition was community-acquired in 31.6%, early onset hospital-acquired in 25%, and late-onset hospital-acquired in 43.4% of patients. Overall prevalence of antimicrobial resistance was 26.3% and difficult-to-treat resistant Gram-negative bacteria 4.3%, with great variation according to geographic region. No difference in prevalence of antimicrobial resistance was observed according to setting of infection acquisition. Overall mortality was 29.1%. Independent risk factors for mortality included late-onset hospital-acquired infection, diffuse peritonitis, sepsis, septic shock, older age, malnutrition, liver failure, congestive heart failure, antimicrobial resistance (either methicillin-resistant Staphylococcus aureus, vancomycin-resistant enterococci, extended-spectrum beta-lactamase-producing Gram-negative bacteria, or carbapenem-resistant Gram-negative bacteria) and source control failure evidenced by either the need for surgical revision or persistent inflammation. CONCLUSION: This multinational, heterogeneous cohort of ICU patients with intra-abdominal infection revealed that setting of infection acquisition, anatomical disruption, and severity of disease expression are disease-specific phenotypic characteristics associated with outcome, irrespective of the type of infection. Antimicrobial resistance is equally common in community-acquired as in hospital-acquired infection.status: publishe