Evaluation of physiologic complexity in time series using generalized sample entropy and surrogate data analysis

Complexity in time series is an intriguing feature of living dynamical systems, with potential use for identification of system state. Although various methods have been proposed for measuring physiologic complexity, uncorrelated time series are often assigned high values of complexity, errouneously classifying them as a complex physiological signals. Here, we propose and discuss a method for complex system analysis based on generalized statistical formalism and surrogate time series. Sample entropy (SampEn) was rewritten inspired in Tsallis generalized entropy, as function of q parameter (qSampEn). qSDiff curves were calculated, which consist of differences between original and surrogate series qSampEn. We evaluated qSDiff for 125 real heart rate variability (HRV) dynamics, divided into groups of 70 healthy, 44 congestive heart failure (CHF), and 11 atrial fibrillation (AF) subjects, and for simulated series of stochastic and chaotic process. The evaluations showed that, for nonperiodic signals, qSDiff curves have a maximum point (qSDiff(max)) for q not equal 1. Values of q where the maximum point occurs and where qSDiff is zero were also evaluated. Only qSDiff(max) values were capable of distinguish HRV groups (p-values 5.10 x 10(-3); 1.11 x 10(-7), and 5.50 x 10(-7) for healthy vs. CHF, healthy vs. AF, and CHF vs. AF, respectively), consistently with the concept of physiologic complexity, and suggests a potential use for chaotic system analysis. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4758815]

Institutional Complexity and Technology Transfer: A Theoretical and Empirical Analysis

This Doctoral Thesis unfolds into a collection of three distinct papers that share an interest in institutional theory and technology transfer. Taking into account that organizations are increasingly exposed to a multiplicity of demands and pressures, we aim to analyze what renders this situation of institutional complexity more or less difficult to manage for organizations, and what makes organizations more or less successful in responding to it. The three studies offer a novel contribution both theoretically and empirically. In particular, the first paper “The dimensions of organizational fields for understanding institutional complexity: A theoretical framework” is a theoretical contribution that tries to better understand the relationship between institutional complexity and fields by providing a framework. The second article “Beyond institutional complexity: The case of different organizational successes in confronting multiple institutional logics” is an empirical study which aims to explore the strategies that allow organizations facing multiple logics to respond more successfully to them. The third work “ How external support may mitigate the barriers to university-industry collaboration” is oriented towards practitioners and presents a case study about technology transfer in Italy.

Analysis of robot dynamics through complexity measures: an application in automatic design

L'applicazione di misure, derivanti dalla teoria dell'informazione, fornisce un valido strumento per quantificare alcune delle proprietà dei sistemi complessi. Le stesse misure possono essere utilizzate in robotica per favorire l'analisi e la sintesi di sistemi di controllo per robot. In questa tesi si è analizzata la correlazione tra alcune misure di complessità e la capacità dei robot di portare a termine, con successo, tre differenti task. I risultati ottenuti suggeriscono che tali misure di complessità rappresentano uno strumento promettente anche nel campo della robotica, ma che il loro utilizzo può diventare difficoltoso quando applicate a task compositi.

Specific and Nonspecific Psychological Interventions for PTSD Symptoms: A Meta-Analysis With Problem Complexity as a Moderator

CONTEXT The necessity of specific intervention components for the successful treatment of patients with posttraumatic stress disorder is the subject of controversy. OBJECTIVE To investigate the complexity of clinical problems as a moderator of relative effects between specific and nonspecific psychological interventions. METHODS We included 18 randomized controlled trials, directly comparing specific and nonspecific psychological interventions. We conducted moderator analyses, including the complexity of clinical problems as predictor. RESULTS Our results have confirmed the moderate superiority of specific over nonspecific psychological interventions; however, the superiority was small in studies with complex clinical problems and large in studies with noncomplex clinical problems. CONCLUSIONS For patients with complex clinical problems, our results suggest that particular nonspecific psychological interventions may be offered as an alternative to specific psychological interventions. In contrast, for patients with noncomplex clinical problems, specific psychological interventions are the best treatment option.

Comparative Effectiveness and Safety of New-Generation Versus Early-Generation Drug-Eluting Stents According to Complexity of Coronary Artery Disease: A Patient-Level Pooled Analysis of 6,081 Patients.

OBJECTIVES The purpose of this study was to compare the 2-year safety and effectiveness of new- versus early-generation drug-eluting stents (DES) according to the severity of coronary artery disease (CAD) as assessed by the SYNTAX (Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery) score. BACKGROUND New-generation DES are considered the standard-of-care in patients with CAD undergoing percutaneous coronary intervention. However, there are few data investigating the effects of new- over early-generation DES according to the anatomic complexity of CAD. METHODS Patient-level data from 4 contemporary, all-comers trials were pooled. The primary device-oriented clinical endpoint was the composite of cardiac death, myocardial infarction, or ischemia-driven target-lesion revascularization (TLR). The principal effectiveness and safety endpoints were TLR and definite stent thrombosis (ST), respectively. Adjusted hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated at 2 years for overall comparisons, as well as stratified for patients with lower (SYNTAX score ≤11) and higher complexity (SYNTAX score >11). RESULTS A total of 6,081 patients were included in the study. New-generation DES (n = 4,554) compared with early-generation DES (n = 1,527) reduced the primary endpoint (HR: 0.75 [95% CI: 0.63 to 0.89]; p = 0.001) without interaction (p = 0.219) between patients with lower (HR: 0.86 [95% CI: 0.64 to 1.16]; p = 0.322) versus higher CAD complexity (HR: 0.68 [95% CI: 0.54 to 0.85]; p = 0.001). In patients with SYNTAX score >11, new-generation DES significantly reduced TLR (HR: 0.36 [95% CI: 0.26 to 0.51]; p < 0.001) and definite ST (HR: 0.28 [95% CI: 0.15 to 0.55]; p < 0.001) to a greater extent than in the low-complexity group (TLR pint = 0.059; ST pint = 0.013). New-generation DES decreased the risk of cardiac mortality in patients with SYNTAX score >11 (HR: 0.45 [95% CI: 0.27 to 0.76]; p = 0.003) but not in patients with SYNTAX score ≤11 (pint = 0.042). CONCLUSIONS New-generation DES improve clinical outcomes compared with early-generation DES, with a greater safety and effectiveness in patients with SYNTAX score >11.

Impact of Diabetic Status on Outcomes After Revascularization With Drug-Eluting Stents in Relation to Coronary Artery Disease Complexity: Patient-Level Pooled Analysis of 6081 Patients.

BACKGROUND Diabetes mellitus and angiographic coronary artery disease complexity are intertwined and unfavorably affect prognosis after percutaneous coronary interventions, but their relative impact on long-term outcomes after percutaneous coronary intervention with drug-eluting stents remains controversial. This study determined drug-eluting stents outcomes in relation to diabetic status and coronary artery disease complexity as assessed by the Synergy Between PCI With Taxus and Cardiac Surgery (SYNTAX) score. METHODS AND RESULTS In a patient-level pooled analysis from 4 all-comers trials, 6081 patients were stratified according to diabetic status and according to the median SYNTAX score ≤11 or >11. The primary end point was major adverse cardiac events, a composite of cardiac death, myocardial infarction, and clinically indicated target lesion revascularization within 2 years. Diabetes mellitus was present in 1310 patients (22%), and new-generation drug-eluting stents were used in 4554 patients (75%). Major adverse cardiac events occurred in 173 diabetics (14.5%) and 436 nondiabetic patients (9.9%; P<0.001). In adjusted Cox regression analyses, SYNTAX score and diabetes mellitus were both associated with the primary end point (P<0.001 and P=0.028, respectively; P for interaction, 0.07). In multivariable analyses, diabetic versus nondiabetic patients had higher risks of major adverse cardiac events (hazard ratio, 1.25; 95% confidence interval, 1.03-1.53; P=0.026) and target lesion revascularization (hazard ratio, 1.54; 95% confidence interval, 1.18-2.01; P=0.002) but similar risks of cardiac death (hazard ratio, 1.41; 95% confidence interval, 0.96-2.07; P=0.08) and myocardial infarction (hazard ratio, 0.89; 95% confidence interval, 0.64-1.22; P=0.45), without significant interaction with SYNTAX score ≤11 or >11 for any of the end points. CONCLUSIONS In this population treated with predominantly new-generation drug-eluting stents, diabetic patients were at increased risk for repeat target-lesion revascularization consistently across the spectrum of disease complexity. The SYNTAX score was an independent predictor of 2-year outcomes but did not modify the respective effect of diabetes mellitus. CLINICAL TRIAL REGISTRATION URL: http://www.clinicaltrials.gov. Unique identifiers: NCT00297661, NCT00389220, NCT00617084, and NCT01443104.

Termination and cost analysis: Complexity and precision issues

The research in this thesis is related to static cost and termination analysis. Cost analysis aims at estimating the amount of resources that a given program consumes during the execution, and termination analysis aims at proving that the execution of a given program will eventually terminate. These analyses are strongly related, indeed cost analysis techniques heavily rely on techniques developed for termination analysis. Precision, scalability, and applicability are essential in static analysis in general. Precision is related to the quality of the inferred results, scalability to the size of programs that can be analyzed, and applicability to the class of programs that can be handled by the analysis (independently from precision and scalability issues). This thesis addresses these aspects in the context of cost and termination analysis, from both practical and theoretical perspectives. For cost analysis, we concentrate on the problem of solving cost relations (a form of recurrence relations) into closed-form upper and lower bounds, which is the heart of most modern cost analyzers, and also where most of the precision and applicability limitations can be found. We develop tools, and their underlying theoretical foundations, for solving cost relations that overcome the limitations of existing approaches, and demonstrate superiority in both precision and applicability. A unique feature of our techniques is the ability to smoothly handle both lower and upper bounds, by reversing the corresponding notions in the underlying theory. For termination analysis, we study the hardness of the problem of deciding termination for a speci�c form of simple loops that arise in the context of cost analysis. This study gives a better understanding of the (theoretical) limits of scalability and applicability for both termination and cost analysis.

Analysis of spontaneous MEG activity in mild cognitive impairment and Alzheimer's disease using spectral entropies and statistical complexity measures

Alzheimer's disease (AD) is the most common cause of dementia. Over the last few years, a considerable effort has been devoted to exploring new biomarkers. Nevertheless, a better understanding of brain dynamics is still required to optimize therapeutic strategies. In this regard, the characterization of mild cognitive impairment (MCI) is crucial, due to the high conversion rate from MCI to AD. However, only a few studies have focused on the analysis of magnetoencephalographic (MEG) rhythms to characterize AD and MCI. In this study, we assess the ability of several parameters derived from information theory to describe spontaneous MEG activity from 36 AD patients, 18 MCI subjects and 26 controls. Three entropies (Shannon, Tsallis and Rényi entropies), one disequilibrium measure (based on Euclidean distance ED) and three statistical complexities (based on Lopez Ruiz–Mancini–Calbet complexity LMC) were used to estimate the irregularity and statistical complexity of MEG activity. Statistically significant differences between AD patients and controls were obtained with all parameters (p < 0.01). In addition, statistically significant differences between MCI subjects and controls were achieved by ED and LMC (p < 0.05). In order to assess the diagnostic ability of the parameters, a linear discriminant analysis with a leave-one-out cross-validation procedure was applied. The accuracies reached 83.9% and 65.9% to discriminate AD and MCI subjects from controls, respectively. Our findings suggest that MCI subjects exhibit an intermediate pattern of abnormalities between normal aging and AD. Furthermore, the proposed parameters provide a new description of brain dynamics in AD and MCI.

Non uniform embedding based on relevance analysis with reduced computational complexity: application to the detection of pathologies from biosignal recordings

Nonlinear analysis tools for studying and characterizing the dynamics of physiological signals have gained popularity, mainly because tracking sudden alterations of the inherent complexity of biological processes might be an indicator of altered physiological states. Typically, in order to perform an analysis with such tools, the physiological variables that describe the biological process under study are used to reconstruct the underlying dynamics of the biological processes. For that goal, a procedure called time-delay or uniform embedding is usually employed. Nonetheless, there is evidence of its inability for dealing with non-stationary signals, as those recorded from many physiological processes. To handle with such a drawback, this paper evaluates the utility of non-conventional time series reconstruction procedures based on non uniform embedding, applying them to automatic pattern recognition tasks. The paper compares a state of the art non uniform approach with a novel scheme which fuses embedding and feature selection at once, searching for better reconstructions of the dynamics of the system. Moreover, results are also compared with two classic uniform embedding techniques. Thus, the goal is comparing uniform and non uniform reconstruction techniques, including the one proposed in this work, for pattern recognition in biomedical signal processing tasks. Once the state space is reconstructed, the scheme followed characterizes with three classic nonlinear dynamic features (Largest Lyapunov Exponent, Correlation Dimension and Recurrence Period Density Entropy), while classification is carried out by means of a simple k-nn classifier. In order to test its generalization capabilities, the approach was tested with three different physiological databases (Speech Pathologies, Epilepsy and Heart Murmurs). In terms of the accuracy obtained to automatically detect the presence of pathologies, and for the three types of biosignals analyzed, the non uniform techniques used in this work lightly outperformed the results obtained using the uniform methods, suggesting their usefulness to characterize non-stationary biomedical signals in pattern recognition applications. On the other hand, in view of the results obtained and its low computational load, the proposed technique suggests its applicability for the applications under study.

Molecular phylogeny analysis of fiddler crabs: test of the hypothesis of increasing behavioral complexity in evolution.

The current phylogenetic hypothesis for the evolution and biogeography of fiddler crabs relies on the assumption that complex behavioral traits are assumed to also be evolutionary derived. Indo-west Pacific fiddler crabs have simpler reproductive social behavior and are more marine and were thought to be ancestral to the more behaviorally complex and more terrestrial American species. It was also hypothesized that the evolution of more complex social and reproductive behavior was associated with the colonization of the higher intertidal zones. Our phylogenetic analysis, based upon a set of independent molecular characters, however, demonstrates how widely entrenched ideas about evolution and biogeography led to a reasonable, but apparently incorrect, conclusion about the evolutionary trends within this pantropical group of crustaceans. Species bearing the set of "derived traits" are phylogenetically ancestral, suggesting an alternative evolutionary scenario: the evolution of reproductive behavioral complexity in fiddler crabs may have arisen multiple times during their evolution. The evolution of behavioral complexity may have arisen by coopting of a series of other adaptations for high intertidal living and antipredator escape. A calibration of rates of molecular evolution from populations on either side of the Isthmus of Panama suggest a sequence divergence rate for 16S rRNA of 0.9% per million years. The divergence between the ancestral clade and derived forms is estimated to be approximately 22 million years ago, whereas the divergence between the American and Indo-west Pacific is estimated to be approximately 17 million years ago.

An analysis of relational complexity in an air traffic control conflict detection task

Theoretical analyses of air traffic complexity were carried out using the Method for the Analysis of Relational Complexity. Twenty-two air traffic controllers examined static air traffic displays and were required to detect and resolve conflicts. Objective measures of performance included conflict detection time and accuracy. Subjective perceptions of mental workload were assessed by a complexity-sorting task and subjective ratings of the difficulty of different aspects of the task. A metric quantifying the complexity of pair-wise relations among aircraft was able to account for a substantial portion of the variance in the perceived complexity and difficulty of conflict detection problems, as well as reaction time. Other variables that influenced performance included the mean minimum separation between aircraft pairs and the amount of time that aircraft spent in conflict.

Time complexity and convergence analysis of domain theoretic Picard method

We present an implementation of the domain-theoretic Picard method for solving initial value problems (IVPs) introduced by Edalat and Pattinson [1]. Compared to Edalat and Pattinson's implementation, our algorithm uses a more efficient arithmetic based on an arbitrary precision floating-point library. Despite the additional overestimations due to floating-point rounding, we obtain a similar bound on the convergence rate of the produced approximations. Moreover, our convergence analysis is detailed enough to allow a static optimisation in the growth of the precision used in successive Picard iterations. Such optimisation greatly improves the efficiency of the solving process. Although a similar optimisation could be performed dynamically without our analysis, a static one gives us a significant advantage: we are able to predict the time it will take the solver to obtain an approximation of a certain (arbitrarily high) quality.

Improving novice analyst performance in modeling the sequence diagram in systems analysis: A cognitive complexity approach

The Unified Modeling Language (UML) has quickly become the industry standard for object-oriented software development. It is being widely used in organizations and institutions around the world. However, UML is often found to be too complex for novice systems analysts. Although prior research has identified difficulties novice analysts encounter in learning UML, no viable solution has been proposed to address these difficulties. Sequence-diagram modeling, in particular, has largely been overlooked. The sequence diagram models the behavioral aspects of an object-oriented software system in terms of interactions among its building blocks, i.e. objects and classes. It is one of the most commonly-used UML diagrams in practice. However, there has been little research on sequence-diagram modeling. The current literature scarcely provides effective guidelines for developing a sequence diagram. Such guidelines will be greatly beneficial to novice analysts who, unlike experienced systems analysts, do not possess relevant prior experience to easily learn how to develop a sequence diagram. There is the need for an effective sequence-diagram modeling technique for novices. This dissertation reports a research study that identified novice difficulties in modeling a sequence diagram and proposed a technique called CHOP (CHunking, Ordering, Patterning), which was designed to reduce the cognitive load by addressing the cognitive complexity of sequence-diagram modeling. The CHOP technique was evaluated in a controlled experiment against a technique recommended in a well-known textbook, which was found to be representative of approaches provided in many textbooks as well as practitioner literatures. The results indicated that novice analysts were able to perform better using the CHOP technique. This outcome seems have been enabled by pattern-based heuristics provided by the technique. Meanwhile, novice analysts rated the CHOP technique more useful although not significantly easier to use than the control technique. The study established that the CHOP technique is an effective sequence-diagram modeling technique for novice analysts.

Improving Novice Analyst Performance in Modeling the Sequence Diagram in Systems Analysis: A Cognitive Complexity Approach

The Unified Modeling Language (UML) has quickly become the industry standard for object-oriented software development. It is being widely used in organizations and institutions around the world. However, UML is often found to be too complex for novice systems analysts. Although prior research has identified difficulties novice analysts encounter in learning UML, no viable solution has been proposed to address these difficulties. Sequence-diagram modeling, in particular, has largely been overlooked. The sequence diagram models the behavioral aspects of an object-oriented software system in terms of interactions among its building blocks, i.e. objects and classes. It is one of the most commonly-used UML diagrams in practice. However, there has been little research on sequence-diagram modeling. The current literature scarcely provides effective guidelines for developing a sequence diagram. Such guidelines will be greatly beneficial to novice analysts who, unlike experienced systems analysts, do not possess relevant prior experience to easily learn how to develop a sequence diagram. There is the need for an effective sequence-diagram modeling technique for novices. This dissertation reports a research study that identified novice difficulties in modeling a sequence diagram and proposed a technique called CHOP (CHunking, Ordering, Patterning), which was designed to reduce the cognitive load by addressing the cognitive complexity of sequence-diagram modeling. The CHOP technique was evaluated in a controlled experiment against a technique recommended in a well-known textbook, which was found to be representative of approaches provided in many textbooks as well as practitioner literatures. The results indicated that novice analysts were able to perform better using the CHOP technique. This outcome seems have been enabled by pattern-based heuristics provided by the technique. Meanwhile, novice analysts rated the CHOP technique more useful although not significantly easier to use than the control technique. The study established that the CHOP technique is an effective sequence-diagram modeling technique for novice analysts.