Sleep disordered breathing (SDB) is a highly prevalent but an under-diagnosed disease. Among adults in the ages between 30 to 60 years, 24% of males and 9% of females show conditions of SDB, while 82% of men and 93% of women with moderate to severe SDB remain undiagnosed. Polysomnography (PSG) is the reference diagnostic test for SDB. During PSG, a number of physiological signals are recorded during an overnight sleep and then manually scored for sleep/wake stages and SDB events to obtain the reference diagnosis. The manual scoring of SDB events is an extremely time consuming and cumbersome task with high inter- and intra-rater variations. PSG is a labour intensive, expensive and patient inconvenient test. Further, PSG facilities are limited leading to long waiting lists. There is an enormous clinical need for automation of PSG scoring and an alternative automated ambulatory method suitable for screening the population. During the work of this thesis, we focus (1) on implementing a framework that enables more reliable scoring of SDB events which also lowers manual scoring time, and (2) implementing a reliable automated screening procedure that can be used as a patient-friendly home based study. The recordings of physiological measurements obtained during patients’ sleep of- ten suffer from data losses, interferences and artefacts. In a typical sleep scoring session, artifact-corrupted signal segments are visually detected and removed from further consideration. We developed a novel framework for automated artifact detection and signal restoration, based on the redundancy among respiratory flow signals. The signals focused on are the airflow (thermistor sensors) and nasal pressure signals that are clinically significant in detecting respira- tory disturbances. We treat the respiratory system as a dynamical system, and use the celebrated Takens embedding theorem as the theoretical basis for sig- nal prediction. In this study, we categorise commonly occurring artefacts and distortions in the airflow and nasal pressure measurements into several groups and explore the efficacy of the proposed technique in detecting/recovering them. Results we obtained from a database of clinical PSG signals indicated that theproposed technique can detect artefacts/distortions with a sensitivity >88% and specificity >92%. This work has the potential to simplify the work done by sleep scoring technicians, and also to improve automated sleep scoring methods. During the next phase of the thesis we have investigated the diagnostic ability of single – and dual–channel respiratory flow measuring devices. Recent studies have shown that single channel respiratory flow measurements can be used for automated diagnosis/screening for sleep disordered breathing (SDB) diseases. Improvements for reliable home-based monitoring for SDB may be achieved with the use of predictors based on recurrence quantification analysis (RQA). RQA essentially measures the complex structures present in a time series and are relatively independent of the nonlinearities present in the respiratory measurements such as those due to breathing nonlinearities and sensor movements. The nasal pressure, thermistor-based airflow, abdominal movement and thoracic movement measurements obtained during Polysomnography, were used in this study to implement an algorithm for automated screening for SDB diseases. The algorithm predicts SDB-affected measurement segments using twelve features based on RQA, body mass index (BMI) and neck circumference using mixture discriminant analysis (MDA). The rate of SDB affected segments of data per hour of recording (RDIS) is used as a measure for the diagnosis of SDB diseases. The operating points to be chosen were the prior probability of SDB affected data segments (π1) and the RDIS threshold value, above which a patient is predicted to have a SDB disease. Cross-validation with five-folds, stratified based on the RDI values of the recordings, was used in estimating the operating points. Sensitivity and specificity rates for the final classifier were estimated using a two-layer assessment approach with the operating points chosen at the inner layer using five-fold cross-validation and the choice assessed at the outer layer using repeated learning-testing. The nasal pressure measurement showed higher accuracy compared to other respiratory measurements when used alone. The nasal pressure and thoracic movement measurements were identified as the best pair of measurements to be used in a dual channel device. The estimated sensitivity and specificity (standard error) in diagnosing SDB disease (RDI ≥ 15) are 90.3(3.1)% and 88.3(5.5)% when nasal pressure is used alone and together with the thoracic movement it was 89.5(3.7)% and 100.0(0.0)%. Present results suggest that RQA of a single respiratory measurement has potential to be used in an automated SDB screening device, while with dual-channel more reliable accuracy can be expected. Improvements may be possible by including other RQA based features and optimisation of the parameters.

abstract: This increasing role of highly automated and intelligent systems as team members has started a paradigm shift from human-human teaming to Human-Autonomy Teaming (HAT). However, moving from human-human teaming to HAT is challenging. Teamwork requires skills that are often missing in robots and synthetic agents. It is possible that adding a synthetic agent as a team member may lead teams to demonstrate different coordination patterns resulting in differences in team cognition and ultimately team effectiveness. The theory of Interactive Team Cognition (ITC) emphasizes the importance of team interaction behaviors over the collection of individual knowledge. In this dissertation, Nonlinear Dynamical Methods (NDMs) were applied to capture characteristics of overall team coordination and communication behaviors. The findings supported the hypothesis that coordination stability is related to team performance in a nonlinear manner with optimal performance associated with moderate stability coupled with flexibility. Thus, we need to build mechanisms in HATs to demonstrate moderately stable and flexible coordination behavior to achieve team-level goals under routine and novel task conditions. / Dissertation/Thesis / Doctoral Dissertation Engineering 2017

Orientador: Prof. Dr. Filipe Ieda Fazanaro / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Engenharia da Informação, 2018. / A detecção de atividade de voz é um problema importante em muitas aplicações de fala/áudio, incluindo codificação e reconhecimento automático de fala; vários algoritmos foram propostos na literatura explorando diferentes métricas de sinais (como a energia do sinal). Neste trabalho, é apresentada uma metodologia alternativa para detecção de atividade vocal (VAD) de um discurso ou sinal de áudio com base nas informações fornecidas pelos gráficos de recorrência do sinal. O método proposto foi capaz de classificar corretamente sinais limpos e com baixos níveis de ruído, apresentando desempenho próximo ao algoritmo incluído no codec G.729, que é comumente usado em aplicativos de Voz sobre IP (VoIP). / Voice activity detection is an important problem in many speech/audio applications, including coding and automatic speech recognition; several algorithms have been proposed in the literature to explore different signal metrics (such as signal energy). In this work, an alternative methodology for the Voice Activity Detection (VAD) of a discourse or audio signal is presented based on the information provided by the signals¿ recurrence plots. The proposed method was able to correctly classify clean signals and with low levels of noise, obtained performance similar to the algorithm included in the G.729 codec, which is commonly used in VoIP applications.

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For most of the naturally occurring signals, especially biomedical signals, the underlying physical process generating the signal is often not fully known, making it difficult to obtain a parametric model. Therefore, signal processing techniques are used to analyze the signal for non-parametrically characterizing the underlying system from which the signals are produced. Most of the real life systems are nonlinear and time varying, which poses a challenge while characterizing them. Additionally, multiple sensors are used to extract signals from such systems, resulting in multichannel signals which are inherently coupled. In this thesis, we counter this challenge by using Recurrence Plot based techniques for characterizing biomedical systems such as heart or brain, using signals such as heart rate variability (HRV), electroencephalogram(EEG) or functional magnetic resonance imaging (fMRI), respectively, extracted from them. In time series analysis, it is well known that a system can be represented by a trajectory in an N-dimensional state space, which completely represents an instance of the system behavior. Such a system characterization has been done using dynamical invariants such as correlation dimension, Lyapunov exponent etc. Takens has shown that when the state variables of the underlying system are not known, one can obtain a trajectory in ‘phase space’ using only the signals obtained from such a system. The phase space trajectory is topologically equivalent to the state space trajectory. This enables us to characterize the system behavior from only the signals sensed from them. However, estimation of correlation dimension, Lyapunov exponent, etc, are vulnerable to non-stationarities in the signal and require large number of sample points for accurate computation, both of which are important in the case of biomedical signals. Alternatively, a technique called Recurrence Plots (RP) has been proposed, which addresses these concerns, apart from providing additional insights. Measures to characterize RPs of single and two channel data are called Recurrence Quantification Analysis (RQA) and cross RQA (CRQA), respectively. These methods have been applied with a good measure of success in diverse areas. However, they have not been studied extensively in the context of experimental biomedical signals, especially multichannel data. In this thesis, the RP technique and its associated measures are briefly reviewed. Using the computational tools developed for this thesis, RP technique has been applied on select single channel, multichannel and multimodal (i.e. multiple channels derived from different modalities) biomedical signals. Connectivity analysis is demonstrated as post-processing of RP analysis on multichannel signals such as EEG and fMRI. Finally, a novel metric, based on the modification of a CRQA measure is proposed, which shows improved results. For the case of single channel signal, we have considered a large database of HRV signals of 112 subjects recorded for both normal and abnormal (anxiety disorder and depression disorder) subjects, in both supine and standing positions. Existing RQA measures, Recurrence Rate and Determinism, were used to distinguish between normal and abnormal subjects with an accuracy of 58.93%. A new measure, MLV has been introduced, using which a classification accuracy of 98.2% is obtained. Correlation between probabilities of recurrence (CPR) is a CRQA measure used to characterize phase synchronization between two signals. In this work, we demonstrate its utility with application to multimodal and multichannel biomedical signals. First, for the multimodal case, we have computed running CPR (rCPR), a modification proposed by us, which allows dynamic estimation of CPR as a function of time, on multimodal cardiac signals (electrocardiogram and arterial blood pressure) and demonstrated that the method can clearly detect abnormalities (premature ventricular contractions); this has potential applications in cardiac care such as assisted automated diagnosis. Second, for the multichannel case, we have used 16 channel EEG signals recorded under various physiological states such as (i) global epileptic seizure and pre-seizure and (ii) focal epilepsy. CPR was computed pair-wise between the channels and a CPR matrix of all pairs was formed. Contour plot of the CPR matrix was obtained to illustrate synchronization. Statistical analysis of CPR matrix for 16 subjects of global epilepsy showed clear differences between pre-seizure and seizure conditions, and a linear discriminant classifier was used in distinguishing between the two conditions with 100% accuracy. Connectivity analysis of multichannel EEG signals was performed by post-processing of the CPR matrix to understand global network-level characterization of the brain. Brain connectivity using thresholded CPR matrix of multichannel EEG signals showed clear differences in the number and pattern of connections in brain connectivity graph between epileptic seizure and pre-seizure. Corresponding brain headmaps provide meaningful insights about synchronization in the brain in those states. K-means clustering of connectivity parameters of CPR and linear correlation obtained from global epileptic seizure and pre-seizure showed significantly larger cluster centroid distances for CPR as opposed to linear correlation, thereby demonstrating the efficacy of CPR. The headmap in the case of focal epilepsy clearly enables us to identify the focus of the epilepsy which provides certain diagnostic value. Connectivity analysis on multichannel fMRI signals was performed using CPR matrix and graph theoretic analysis. Adjacency matrix was obtained from CPR matrices after thresholding it using statistical significance tests. Graph theoretic analysis based on communicability was performed to obtain community structures for awake resting and anesthetic sedation states. Concurrent behavioral data showed memory impairment due to anesthesia. Given the fact that previous studies have implicated the hippocampus in memory function, the CPR results showing the hippocampus within the community in awake state and out of it in anesthesia state, demonstrated the biological plausibility of the CPR results. On the other hand, results from linear correlation were less biologically plausible. In biological systems, highly synchronized and desynchronized systems are of interest rather than moderately synchronized ones. However, CPR is approximately a monotonic function of synchronization and hence can assume values which indicate moderate synchronization. In order to emphasize high synchronization/ desynchronization and de-emphasize moderate synchronization, a new method of Correlation Synchronization Convergence Time (CSCT) is proposed. It is obtained using an iterative procedure involving the evaluation of CPR for successive autocorrelations until CPR converges to a chosen threshold. CSCT was evaluated for 16 channel EEG data and corresponding contour plots and histograms were obtained, which shows better discrimination between synchronized and asynchronized states compared to the conventional CPR. This thesis has demonstrated the efficacy of RP technique and associated measures in characterizing various classes of biomedical signals. The results obtained are corroborated by well known physiological facts, and they provide physiologically meaningful insights into the functioning of the underlying biological systems, with potential diagnostic value in healthcare.

This thesis examines three decomposition techniques and their usability for economic and financial time series. The stock index DAX30 and the exchange rate from British pound to US dollar are used as representative economic time series. Additionally, autoregressive and conditional heteroscedastic simulations are analysed as benchmark processes to the real data. Discrete wavelet transform (DWT) uses wavelike functions to adapt the behaviour of time series on different time scales. The second method is the singular spectral analysis (SSA), which is applied to extract influential reconstructed modes. As a third algorithm, empirical mode decomposition (END) leads to intrinsic mode functions, who reflect the short and long term fluctuations of the time series. Some problems arise in the decomposition process, such as bleeding at the DWT method or mode mixing of multiple EMD mode functions. Conclusions to evaluate the predictability of the time series are drawn based on entropy - and recurrence - analysis. The cyclic behaviour of the decompositions is examined via the coefficient of variation, based on the instantaneous frequency. The results show rising predictability, especially on higher decomposition levels. The instantaneous frequency measure leads to low values for regular oscillatory cycles, irregular behaviour results in a high variation coefficient. The singular spectral analysis show frequency - stable cycles in the reconstructed modes, but represents the influences of the original time series worse than the other two methods, which show on the contrary very little frequency - stability in the extracted details.

La contribution relative des différentes entrées sensorielles dans le contrôle postural a souvent été étudiée dans le cadre de la posture érigée fondamentale. L’objectif de nos travaux a été de déterminer cette contribution dans deux postures relativement proches dans leur configuration : l’appui tendu renversé (ATR) et la verticale inversée (VI) en milieu aquatique. Si la vision est souvent considérée comme information principale dans le contrôle postural de la station érigée, la configuration structurelle (champ visuel restreint en ATR, immersion de l’œil en VI) de ces deux postures laisse imaginer un fonctionnement différent. La perturbation des différents capteurs sensoriels impliqués dans le contrôle postural a permis d’observer la réorganisation mise en place par le système nerveux central (SNC) pour y remédier. De plus la prise en compte d’un facteur perceptif comme la dépendance au champ visuel nous a paru être un élément pertinent pour tenter d’observer d’éventuelles différences interindividuelles dans les comportements des sujets. La perturbation du contrôle postural a été évaluée au travers d’une analyse cinématique et d’une analyse stabilométrique de l’ATR (analyse classique et non-linéaire). Les résultats de ces analyses laissent entrevoir des différences de stratégie entre les sujets dépendants et indépendants au champ visuel dans le contrôle de l’appui tendu renversé, ce qui ne semble pas être le cas en verticale inversée / The relative contribution of the different sensory inputs in erect postural control has often been studied. The aim of this work is to determine this contribution in two positions relatively close in their configuration: the handstand and the upside-down posture in water. If vision is often considered as the main information in postural control, the structural configuration (restricted visual field in handstand, eye’s immersion in upside-down posture) of these two postures lets imagine a different operation. Disruption of sensory receptors involved in postural control has allowed observing the reorganization implemented by the central nervous system (CNS). In addition, the visual field dependence appeared to be a relevant factor to observe interindividual behavioral differences. Disturbance of postural control was assessed through a kinematic analysis and a stabilometric analysis of the handstand (classical analysis and non-linear analysis). Results of these analyzes suggest different strategy according to visual field dependence in the control of the handstand, which does not seem to be the case in upside-down posture

The aim of this thesis is to describe the variability of cardiac rhythm and familiarity with the methods of the analysis, ie by monitoring changes in heart rhythm electrogram signal recording and using the methods in the time domain using recurrent diagram. The work describes the quantification of the methods and possibilities of quantifiers in the evaluation of heart rate variability analysis. It also describes the clinical significance of heart rate variability and diagnostic capabilities changes of heart rate variability caused by ischemic heart disease. The practical part describes how to create applications in Matlab to calculate the quantifiers analysis of heart rate variability in the time domain using recurrent diagram. The calculation was made of the positions R wave elektrogram signal isolated rabbit hearts. The calculated values of quantifiers both methods were statistically evaluated and discussed.

This thesis examines three decomposition techniques and their usability for economic and financial time series. The stock index DAX30 and the exchange rate from British pound to US dollar are used as representative economic time series. Additionally, autoregressive and conditional heteroscedastic simulations are analysed as benchmark processes to the real data. Discrete wavelet transform (DWT) uses wavelike functions to adapt the behaviour of time series on different time scales. The second method is the singular spectral analysis (SSA), which is applied to extract influential reconstructed modes. As a third algorithm, empirical mode decomposition (END) leads to intrinsic mode functions, who reflect the short and long term fluctuations of the time series. Some problems arise in the decomposition process, such as bleeding at the DWT method or mode mixing of multiple EMD mode functions. Conclusions to evaluate the predictability of the time series are drawn based on entropy - and recurrence - analysis. The cyclic behaviour of the decompositions is examined via the coefficient of variation, based on the instantaneous frequency. The results show rising predictability, especially on higher decomposition levels. The instantaneous frequency measure leads to low values for regular oscillatory cycles, irregular behaviour results in a high variation coefficient. The singular spectral analysis show frequency - stable cycles in the reconstructed modes, but represents the influences of the original time series worse than the other two methods, which show on the contrary very little frequency - stability in the extracted details.:1. Einleitung 2. Datengrundlage 2.1. Auswahl und Besonderheiten ökonomischer Zeitreihen 2.2. Simulationsstudie mittels AR-Prozessen 2.3. Simulationsstudie mittels GARCH-Prozessen 3. Zerlegung mittels modernen Techniken der Zeitreihenanalyse 3.1. Diskrete Wavelet Transformation 3.2. Singulärsystemanalyse 3.3. Empirische Modenzerlegung 4. Bewertung der Vorhersagbarkeit 4.1. Entropien als Maß der Kurzzeit-Vorhersagbarkeit 4.2. Rekurrenzanalyse 4.3. Frequenzstabilität der Zerlegung 5. Durchführung und Interpretation der Ergebnisse 5.1. Visuelle Interpretation der Zerlegungen 5.2. Beurteilung mittels Charakteristika 6. Fazit

L'analyse de séries temporelles biomédicales chaotiques tirées de systèmes dynamiques non-linéaires est toujours un challenge difficile à relever puisque dans certains cas bien spécifiques les techniques existantes basées sur les multi-fractales, les entropies et les graphes de récurrence échouent. Pour contourner les limitations des invariants précédents, de nouveaux descripteurs peuvent être proposés. Dans ce travail de recherche nos contributions ont porté à la fois sur l’amélioration d’indicateurs multifractaux (basés sur une fonction de structure) et entropiques (approchées) mais aussi sur des indicateurs de récurrences (non biaisés). Ces différents indicateurs ont été développés avec pour objectif majeur d’améliorer la discrimination entre des signaux de complexité différente ou d’améliorer la détection de transitions ou de changements de régime du système étudié. Ces changements agissant directement sur l’irrégularité du signal, des mouvements browniens fractionnaires et des signaux tirés du système du Lorenz ont été testés. Ces nouveaux descripteurs ont aussi été validés pour discriminer des fœtus en souffrance de fœtus sains durant le troisième trimestre de grossesse. Des mesures statistiques telles que l’erreur relative, l’écart type, la spécificité, la sensibilité ou la précision ont été utilisées pour évaluer les performances de la détection ou de la classification. Le fort potentiel de ces nouveaux invariants nous laisse penser qu’ils pourraient constituer une forte valeur ajoutée dans l’aide au diagnostic s’ils étaient implémentés dans des logiciels de post-traitement ou dans des dispositifs biomédicaux. Enfin, bien que ces différentes méthodes aient été validées exclusivement sur des signaux fœtaux, une future étude incluant des signaux tirés d’autres systèmes dynamiques nonlinéaires sera réalisée pour confirmer leurs bonnes performances. / The analysis of biomedical time series derived from nonlinear dynamic systems is challenging due to the chaotic nature of these time series. Only few classical parameters can be detected by clinicians to opt the state of patients and fetuses. Though there exist valuable complexity invariants such as multi-fractal parameters, entropies and recurrence plot, they were unsatisfactory in certain cases. To overcome this limitation, we propose in this dissertation new entropy invariants, we contributed to multi-fractal analysis and we developed signal-based (unbiased) recurrence plots based on the dynamic transitions of time series. Principally, we aim to improve the discrimination between healthy and distressed biomedical systems, particularly fetuses by processing the time series using our techniques. These techniques were either validated on Lorenz system, logistic maps or fractional Brownian motions modeling chaotic and random time series. Then the techniques were applied to real fetus heart rate signals recorded in the third trimester of pregnancy. Statistical measures comprising the relative errors, standard deviation, sensitivity, specificity, precision or accuracy were employed to evaluate the performance of detection. Elevated discernment outcomes were realized by the high-order entropy invariants. Multi-fractal analysis using a structure function enhances the detection of medical fetal states. Unbiased cross-determinism invariant amended the discrimination process. The significance of our techniques lies behind their post-processing codes which could build up cutting-edge portable machines offering advanced discrimination and detection of Intrauterine Growth Restriction prior to fetal death. This work was devoted to Fetal Heart Rates but time series generated by alternative nonlinear dynamic systems should be further considered.