Complexity Variability Assessment of Nonlinear Time-Varying Cardiovascular Control

The application of complex systems theory to physiology and medicine has provided meaningful information about the nonlinear aspects underlying the dynamics of a wide range of biological processes and their disease-related aberrations. However, no studies have investigated whether meaningful information can be extracted by quantifying second-order moments of time-varying cardiovascular complexity. To this extent, we introduce a novel mathematical framework termed complexity variability, in which the variance of instantaneous Lyapunov spectra estimated over time serves as a reference quantifier. We apply the proposed methodology to four exemplary studies involving disorders which stem from cardiology, neurology and psychiatry: Congestive Heart Failure (CHF), Major Depression Disorder (MDD), Parkinson?s Disease (PD), and Post-Traumatic Stress Disorder (PTSD) patients with insomnia under a yoga training regime. We show that complexity assessments derived from simple time-averaging are not able to discern pathology-related changes in autonomic control, and we demonstrate that between-group differences in measures of complexity variability are consistent across pathologies. Pathological states such as CHF, MDD, and PD are associated with an increased complexity variability when compared to healthy controls, whereas wellbeing derived from yoga in PTSD is associated with lower time-variance of complexity

Nonlinear dynamics and modeling of heart and brain signals

Ph.DDOCTOR OF PHILOSOPH

Signal Modality Characterization: from Phase Space Reconstruction to Real Applications

La caracterización de la modalidad de la señal es un nuevo concepto objeto de recientes trabajos de investigación cuyo principal propósito es identificar cambios en la naturaleza de señales reales. Con el término naturaleza de las señales se hace referencia al modelo subyacente que genera una señal desde el punto de vista de dos características principales: determinismo y linealidad. En esta tesis se emplea la modalidad de la señal para el procesado avanzado de señales acústicas, y en particular, en ensayos no destructivos de materiales no homogéneos como el hormigón. El problema de la caracterización de la modalidad comienza con la correcta reconstrucción del espacio de fases (Capítulo 2). Este nuevo dominio permite identificar los diferentes estados de una señal, recurrentes o no en función de su naturaleza determinista o aleatoria, respectivamente. En el ámbito de los ensayos no destructivos basados en ultrasonidos, el material se excita con una señal puramente determinista, sin embargo, la naturaleza de la señal recibida depende y es proporcional a la estructura interna del material. Esta hipótesis de trabajo permite plantear la medida del grado de determinismo como una alternativa complementaria a parámetros habituales de ultrasonidos como la atenuación y la velocidad. El nivel de determinismo ha resultado ser proporcional al nivel de porosidad en materiales cementantes (Capítulo 3). También permite la caracterización del nivel de daño de probetas de mortero sometidas a diferentes procesos de daño: ataque externo de sulfato y procesos de carga (Capítulo 4). El estudio de la no linealidad/ complejidad de una serie temporal se plantea inicialmente de forma ciega (sin tener información de la señal de entrada) mediante tests de hipótesis: generando datos surrogados y aplicando un test estadístico. Importantes avances se han logrado adaptando este enfoque a datos no estacionarios, característica habitual de señales no lineales reales. Los principales resultados en este sentido se han conseguido en la caracterización de la complejidad de señales oscilatorias de duración limitada (Capítulo 5). El concepto de la modalidad de la señal también se ha empleado para realizar un detallado estudio del fenómeno no lineal de espectroscopía acústica por impacto. Este análisis ha permitido entender las variables involucradas y plantear así un modelo matemático que caracterice el fenómeno. La comprensión del fenómeno y el modelo han permitido plantear un nuevo algoritmo de procesado equivalente a la técnica habitual NIRAS, pero óptimo en su aplicación. Esta alternativa de procesado puede suponer significativos avances sobre todo en aplicaciones industriales donde el tiempo y el esfuerzo son variables óptimas (Capítulo 6). Esta tesis demuestra que la caracterización de la modalidad de la señal no solo supone una alternativa a la caracterización de complejos fenómenos reales, sino que abre una nueva perspectiva de trabajo dentro del ámbito del procesado de señal. La medida del determinismo y el algoritmo FANSIRAS han demostrado que la modalidad de la señal es una interesante herramienta para futuros trabajos de caracterización de materiales cementantes.The characterization of the modality of a signal is a new concept, which has been the subject of recent research. Its main purpose is to identify any changes in the nature of a real signal. The term `nature of a signal' refers to the underlying model that generates the signal from the point of view of two main characteristics: determinism and linearity. In this thesis, the modality of a signal is used for the advanced processing of acoustic signals, and in particular, in non-destructive tests of non-homogeneous materials, such as concrete. The problem of the characterization of the modality begins with the correct reconstruction of the phase space (Chapter 2). This new domain allows identifying the different states of a signal, as to whether they are recurrent or not, depending on whether they are deterministic, respectively, random. In the field of non-destructive testing based on ultrasound, the material is excited with a purely deterministic signal. However, the nature of the received signal depends on the internal structure of the material. This working hypothesis allows us to propose measuring the degree of determinism as a complementary alternative to the usual ultrasound parameters such, as attenuation and speed. The level of determinism has been found to be proportional to the level of porosity in cementitious materials (Chapter 3). It also allows characterizing the level of damage of mortar test pieces subjected to different kinds of damaging processes: external attack by sulphates, and loading processes (Chapter 4). The study of the non-linearity or complexity of a time series is initially presented blindly (without having information about the input signal) through hypothesis tests: generating surrogate data and applying a statistical test. Significant progress has been made in adapting this approach to nonstationary data, a common feature of real non-linear signals. The main results in this regard have been achieved in the characterization of the complexity of oscillatory signals of limited duration (Chapter 5). The concept of signal modality has also been used to perform a detailed study of the non-linear phenomenon of acoustic impact spectroscopy. This analysis has allowed understanding the variables involved, and thus, proposing a mathematical model that characterizes the phenomenon. The understanding of the phenomenon and the model have allowed proposing a new processing algorithm equivalent to the usual NIRAS technique, but optimal in its application. This processing alternative may mean significant advances, especially in industrial applications where time and e ort are variables to be optimized (Chapter 6). This thesis demonstrates that the characterization of the modality of a signal not only presents an alternative to the characterization of complicated real phenomena, but it also opens a new research perspective within the field of signal processing. The measure of determinism and the FANSIRAS algorithm have shown that the modality of a signal is an interesting tool for future research into the characterization of cementitious materials.La caracterització de la modalitat del senyal és un nou concepte, objecte de recents treballs de recerca amb el propòsit d'identificar canvis en la natura de senyals reals. Amb el terme natura dels senyals es fa referència al model subjacent que genera un senyal des del punt de vista de dues característiques principals: determinisme i linealitat. En aquesta tesi es fa servir la modalitat del senyal per al processament avançat de senyals acústics i, en particular, en assajos no destructius de materials no homogenis com ara el formigó. El problema de la caracterització de la modalitat comença amb la correcta reconstrucció de l'espai de fase (Capítol 2). Aquest nou domini permet identificar els diferents estats d'un senyal, recurrents o no en funció de la seva natura determinista o aleatòria, respectivament. Dins l'àmbit dels assajos no destructius basats en ultrasons, el material s'excita amb un senyal purament determinista, tanmateix, la natura del senyal rebut depèn i és proporcional a l'estructura interna del material. Aquesta hipòtesi de treball permet plantejar la mesura del grau de determinisme com una alternativa complementària a paràmetres habituals dels ultrasons com ara l'atenuació i la velocitat. El nivell de determinisme ha resultat ésser proporcional al nivell de porositat en materials cementants (Capítol 3). També permet la caracterització del nivell de dany de provetes de morter sotmeses a diferents processos de dany: atac extern de sulfat i processos de càrrega (Capítol 4). L'estudi de la no linealitat/ complexitat d'una sèrie temporal es planteja inicialment de forma cega (sense tindre cap informació del senyal d'entrada) mitjançant tests d'hipòtesi: generant dades subrogades i aplicant un test estadístic. Avanços importants s'han aconseguit adaptant aquest enfoc a dades no estacionàries, característica habitual de senyals no lineals reals. Els principals resultats en aquest sentit s'han aconseguit en la caracterització de la complexitat de senyals oscil·latoris de durada limitada (Capítol 5). El concepte de modalitat del senyal també s'ha emprat per realitzar un detallat estudi del fenomen no lineal d'espectroscòpia acústica per impacte. Aquesta anàlisi ha permet entendre les variables involucrades i plantejar llavors un nou algoritme de processament equivalent a la tècnica habitual NIRAS, però òptim en la seva aplicació. Aquesta alternativa de processament pot suposar significatius avanços sobretot en aplicacions industrials, on el temps i l'esforç són variables òptimes (Capítol 6). Aquesta tesi demostra que la caracterització de la modalitat del senyal no solament suposa una alternativa a la caracterització de complexes fenòmens reals, sinó que obri una nova perspectiva de treball dins l'àmbit del processament de senyal. La mesura del determinisme i l'algoritme FANSIRAS han demostrat que la modalitat del senyal és una ferramenta interessant per a futurs treballs de caracterització de materials cementants.Carrión García, A. (2018). Signal Modality Characterization: from Phase Space Reconstruction to Real Applications [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/106366TESI

Assessing Variability of EEG and ECG/HRV Time Series Signals Using a Variety of Non-Linear Methods

Time series signals, such as Electroencephalogram (EEG) and Electrocardiogram (ECG) represent the complex dynamic behaviours of biological systems. The analysis of these signals using variety of nonlinear methods is essential for understanding variability within EEG and ECG, which potentially could help unveiling hidden patterns related to underlying physiological mechanisms. EEG is a time varying signal, and electrodes for recording EEG at different positions on the scalp give different time varying signals. There might be correlation between these signals. It is important to know the correlation between EEG signals because it might tell whether or not brain activities from different areas are related. EEG and ECG might be related to each other because both of them are generated from one co-ordinately working body. Investigating this relationship is of interest because it may reveal information about the correlation between EEG and ECG signals. This thesis is about assessing variability of time series data, EEG and ECG, using variety of nonlinear measures. Although other research has looked into the correlation between EEGs using a limited number of electrodes and a limited number of combinations of electrode pairs, no research has investigated the correlation between EEG signals and distance between electrodes. Furthermore, no one has compared the correlation performance for participants with and without medical conditions. In my research, I have filled up these gaps by using a full range of electrodes and all possible combinations of electrode pairs analysed in Time Domain (TD). Cross-Correlation method is calculated on the processed EEG signals for different number unique electrode pairs from each datasets. In order to obtain the distance in centimetres (cm) between electrodes, a measuring tape was used. For most of our participants the head circumference range was 54-58cm, for which a medium-sized I have discovered that the correlation between EEG signals measured through electrodes is linearly dependent on the physical distance (straight-line) distance between them for datasets without medical condition, but not for datasets with medical conditions. Some research has investigated correlation between EEG and Heart Rate Variability (HRV) within limited brain areas and demonstrated the existence of correlation between EEG and HRV. But no research has indicated whether or not the correlation changes with brain area. Although Wavelet Transformations (WT) have been performed on time series data including EEG and HRV signals to extract certain features respectively by other research, so far correlation between WT signals of EEG and HRV has not been analysed. My research covers these gaps by conducting a thorough investigation of all electrodes on the human scalp in Frequency Domain (FD) as well as TD. For the reason of different sample rates of EEG and HRV, two different approaches (named as Method 1 and Method 2) are utilised to segment EEG signals and to calculate Pearson’s Correlation Coefficient for each of the EEG frequencies with each of the HRV frequencies in FD. I have demonstrated that EEG at the front area of the brain has a stronger correlation with HRV than that at the other area in a frequency domain. These findings are independent of both participants and brain hemispheres. Sample Entropy (SE) is used to predict complexity of time series data. Recent research has proposed new calculation methods for SE, aiming to improve the accuracy. To my knowledge, no one has attempted to reduce the computational time of SE calculation. I have developed a new calculation method for time series complexity which could improve computational time significantly in the context of calculating a correlation between EEG and HRV. The results have a parsimonious outcome of SE calculation by exploiting a new method of SE implementation. In addition, it is found that the electrical activity in the frontal lobe of the brain appears to be correlated with the HRV in a time domain. Time series analysis method has been utilised to study complex systems that appear ubiquitous in nature, but limited to certain dynamic systems (e.g. analysing variables affecting stock values). In this thesis, I have also investigated the nature of the dynamic system of HRV. I have disclosed that Embedding Dimension could unveil two variables that determined HRV

Nonlinear, multidimensional transformations and their applications to signal processing

Modeling a system based on time series is a complicated problem in general, especially when the time series is nonlinear and chaotic. The goal of the thesis is to introduce a method of prediction and modeling that exploits the property of recurrence in dynamical systems. A time series is said to be recurrent if keeps on visiting a particular neighborhood in the state space. The thesis demonstrates that the inherent redundancy structure of a well known topological technique known as delay embedding can be coupled with recurrence property to develop a new method of prediction. The modeling procedure empirically finds the recurrence neighborhoods from the signal, which are then subdivided into various equivalence classes based on their recurrence timings. A set of affine maps are then derived across these equivalence classes. This gives is a possibility of simplifying the dynamics in terms of affine transformations in small neighborhoods. The delay-embedding (done in a dimension much higher than the inherent dimension of the dynamics) is used as a scaffolding to analyze the global structure of the system. A projection to a lower dimension was followed to take care of the fundamental issues related to high dimensional models that describe a low dimensional dynamics. Local analysis of the system was done in the low dimensional projected space. A topological conjugacy of the recurrence neighborhoods in both the lower and the higher dimensional spaces are demonstrated. The proposed model uses a nonlinear generalization of a well known linear algebra technique named Singular Value Decomposition (SVD) for data analysis. The method of nonlinear SVD and its uses (i) to determine nonlinearity in a time series and (ii) to empirically arrive at an upper bound for the dimension of a manifold where the data resides are demonstrated. The proposed method of prediction and modeling was used for the analysis of (i) data generated by the Duffing oscillator and (ii) an Electrocardiogram (ECG) record. It is shown that the entire nonlinear structure can be deduced from one or few overlapping neighborhoods for these data. A method of stability analysis by studying the properties of affine maps specific to the neighborhoods are demonstrated for both these data. The thesis gives a theoretical justification for a well known experimental observation that the heart rate variability– a variability in beat-to-beat intervals of the heart is a necessity for healthy functioning of the heart. The relevance and contribution of the introduced method for biomedical signal processing is justified by using it successfully for analyzing a set of multi-channel physiological data

Autonomic Nervous System Dynamics for Mood and Emotional-State Recognition: Wearable Systems, Modeling, and Advanced Biosignal Processing

This thesis aims at investigating how electrophysiological signals related to the autonomic nervous system (ANS) dynamics could be source of reliable and effective markers for mood state recognition and assessment of emotional responses. In-depth methodological and applicative studies of biosignals such as electrocardiogram, electrodermal response, and respiration activity along with information coming from the eyes (gaze points and pupil size variation) were performed. Supported by the current literature, I found that nonlinear signal processing techniques play a crucial role in understanding the underlying ANS physiology and provide important quantifiers of cardiovascular control dynamics with prognostic value in both healthy subjects and patients. Two main applicative scenarios were identified: the former includes a group of healthy subjects who was presented with sets of images gathered from the International Affective Picture System hav- ing five levels of arousal and five levels of valence, including both a neutral reference level. The latter was constituted by bipolar patients who were followed for a period of 90 days during which psychophysical evaluations were performed. In both datasets, standard signal processing techniques as well as nonlinear measures have been taken into account to automatically and accurately recognize the elicited levels of arousal and valence and mood states, respectively. A novel probabilistic approach based on the point-process theory was also successfully applied in order to model and characterize the instantaneous ANS nonlinear dynamics in both healthy subjects and bipolar patients. According to the reported evidences on ANS complex behavior, experimental results demonstrate that an accurate characterization of the elicited affective levels and mood states is viable only when non- linear information are retained. Moreover, I demonstrate that the instantaneous ANS assessment is effective in both healthy subjects and patients. Besides mathematics and signal processing, this thesis also contributes to pragmatic issues such as emotional and mood state mod- eling, elicitation, and noninvasive ANS monitoring. Throughout the dissertation, a critical review on the current state-of-the-art is reported leading to the description of dedicated experimental protocols, reliable mood models, and novel wearable systems able to perform ANS monitoring in a naturalistic environment

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

Developing and Validating Open Source Tools for Advanced Neuroimaging Research

Almost all scientific research relies on software. This is particularly true for research that uses neuroimaging technologies, such as functional magnetic resonance imaging (fMRI). These technologies generate massive amounts of data per participant, which must be processed and analyzed using specialized software. A large portion of these tools are developed by teams of researchers, rather than trained software developers. In this kind of ecosystem, where the majority of software creators are scientists, rather than trained programmers, it becomes more important than ever to rely on community-based development, which may explain why most of this software is open source. It is in the development of this kind of research-oriented, open source software that I have focused much of my graduate training, as is reflected in this dissertation. One software package I have helped to develop and maintain is tedana, a Python library for denoising multi-echo fMRI data. In chapter 2, I describe this library in a short, published software paper. Another library I maintain as the primary developer is NiMARE, a Python library for performing neuroimaging meta-analyses and derivative analyses, such as automated annotation and functional decoding. In chapter 3, I present NiMARE in a hybrid software paper with embedded tutorial code exhibiting the functionality of the library. This paper is currently hosted as a Jupyter book that combines narrative content and code snippets that can be executed online. In addition to research software development, I have focused my graduate work on performing reproducible, open fMRI research. To that end, chapter 4 is a repli- cation and extension of a recent paper on multi-echo fMRI denoising methods Power et al. (2018a). This replication was organized as a registered report, in which the introduction and methods were submitted for peer review before the analyses were performed. Finally, chapter 5 is a conclusion to the dissertation, in which I reflect on the work I have done and the skills I have developed throughout my training

The drivers of Corporate Social Responsibility in the supply chain. A case study.

Purpose: The paper studies the way in which a SME integrates CSR into its corporate strategy, the practices it puts in place and how its CSR strategies reflect on its suppliers and customers relations. Methodology/Research limitations: A qualitative case study methodology is used. The use of a single case study limits the generalizing capacity of these findings. Findings: The entrepreneur’s ethical beliefs and value system play a fundamental role in shaping sustainable corporate strategy. Furthermore, the type of competitive strategy selected based on innovation, quality and responsibility clearly emerges both in terms of well defined management procedures and supply chain relations as a whole aimed at involving partners in the process of sustainable innovation. Originality/value: The paper presents a SME that has devised an original innovative business model. The study pivots on the issues of innovation and eco-sustainability in a context of drivers for CRS and business ethics. These values are considered fundamental at International level; the United Nations has declared 2011 the “International Year of Forestry”