15 research outputs found

    Statistical Frequency-Dependent Analysis of Trial-to-Trial Variability in Single Time Series by Recurrence Plots

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    International audienceFor decades, research in neuroscience has supported the hypothesis that brain dynamics exhibits recurrent metastable states connected by transients, which together encode fundamental neural information processing. To understand the system’s dynamics it is important to detect such recurrence domains, but it is challenging to extract them from experimental neuroscience datasets due to the large trial-to-trial variability. The proposed methodology extracts recurrent metastable states in univariate time series by transforming datasets into their time-frequency representations and computing recurrence plots based on instantaneous spectral power values in various frequency bands. Additionally, a new statistical inference analysis compares different trial recurrence plots with corresponding surrogates to obtain statistically significant recurrent structures. This combination of methods is validated by applying it to two artificial datasets. In a final study of visually-evoked Local Field Potentials in partially anesthetized ferrets, the methodology is able to reveal recurrence structures of neural responses with trial-to-trial variability. Focusing on different frequency bands, the delta-band activity is much less recurrent than alpha-band activity. Moreover, alpha-activity is susceptible to pre-stimuli, while delta-activity is much less sensitive to pre-stimuli. This difference in recurrence structures in different frequency bands indicates diverse underlying information processing steps in the brain

    Seismic characterisation based on time-frequency spectral analysis

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    We present high-resolution time-frequency spectral analysis schemes to better resolve seismic images for the purpose of seismic and petroleum reservoir characterisation. Seismic characterisation is based on the physical properties of the Earth's subsurface media, and these properties are represented implicitly by seismic attributes. Because seismic traces originally presented in the time domain are non-stationary signals, for which the properties vary with time, we characterise those signals by obtaining seismic attributes which are also varying with time. Among the widely used attributes are spectral attributes calculated through time-frequency decomposition. Time-frequency spectral decomposition methods are employed to capture variations of a signal within the time-frequency domain. These decomposition methods generate a frequency vector at each time sample, referred to as the spectral component. The computed spectral component enables us to explore the additional frequency dimension which exists jointly with the original time dimension enabling localisation and characterisation of patterns within the seismic section. Conventional time-frequency decomposition methods include the continuous wavelet transform and the Wigner-Ville distribution. These methods suffer from challenges that hinder accurate interpretation when used for seismic interpretation. Continuous wavelet transform aims to decompose signals on a basis of elementary signals which have to be localised in time and frequency, but this method suffers from resolution and localisation limitations in the time-frequency spectrum. In addition to smearing, it often emerges from ill-localisation. The Wigner-Ville distribution distributes the energy of the signal over the two variables time and frequency and results in highly localised signal components. Yet, the method suffers from spurious cross-term interference due to its quadratic nature. This interference is misleading when the spectrum is used for interpretation purposes. For the specific application on seismic data the interference obscures geological features and distorts geophysical details. This thesis focuses on developing high fidelity and high-resolution time-frequency spectral decomposition methods as an extension to the existing conventional methods. These methods are then adopted as means to resolve seismic images for petroleum reservoirs. These methods are validated in terms of physics, robustness, and accurate energy localisation, using an extensive set of synthetic and real data sets including both carbonate and clastic reservoir settings. The novel contributions achieved in this thesis include developing time-frequency analysis algorithms for seismic data, allowing improved interpretation and accurate characterisation of petroleum reservoirs. The first algorithm established in this thesis is the Wigner-Ville distribution (WVD) with an additional masking filter. The standard WVD spectrum has high resolution but suffers the cross-term interference caused by multiple components in the signal. To suppress the cross-term interference, I designed a masking filter based on the spectrum of the smoothed-pseudo WVD (SP-WVD). The original SP-WVD incorporates smoothing filters in both time and frequency directions to suppress the cross-term interference, which reduces the resolution of the time-frequency spectrum. In order to overcome this side-effect, I used the SP-WVD spectrum as a reference to design a masking filter, and apply it to the standard WVD spectrum. Therefore, the mask-filtered WVD (MF-WVD) can preserve the high-resolution feature of the standard WVD while suppressing the cross-term interference as effectively as the SP-WVD. The second developed algorithm in this thesis is the synchrosqueezing wavelet transform (SWT) equipped with a directional filter. A transformation algorithm such as the continuous wavelet transform (CWT) might cause smearing in the time-frequency spectrum, i.e. the lack of localisation. The SWT attempts to improve the localisation of the time-frequency spectrum generated by the CWT. The real part of the complex SWT spectrum, after directional filtering, is capable to resolve the stratigraphic boundaries of thin layers within target reservoirs. In terms of seismic characterisation, I tested the high-resolution spectral results on a complex clastic reservoir interbedded with coal seams from the Ordos basin, northern China. I used the spectral results generated using the MF-WVD method to facilitate the interpretation of the sand distribution within the dataset. In another implementation I used the SWT spectral data results and the original seismic data together as the input to a deep convolutional neural network (dCNN), to track the horizons within a 3D volume. Using these application-based procedures, I have effectively extracted the spatial variation and the thickness of thinly layered sandstone in a coal-bearing reservoir. I also test the algorithm on a carbonate reservoir from the Tarim basin, western China. I used the spectrum generated by the synchrosqueezing wavelet transform equipped with directional filtering to characterise faults, karsts, and direct hydrocarbon indicators within the reservoir. Finally, I investigated pore-pressure prediction in carbonate layers. Pore-pressure variation generates subtle changes in the P-wave velocity of carbonate rocks. This suggests that existing empirical relations capable of predicting pore-pressure in clastic rocks are unsuitable for the prediction in carbonate rocks. I implemented the prediction based on the P-wave velocity and the wavelet transform multi-resolution analysis (WT-MRA). The WT-MRA method can unfold information within the frequency domain via decomposing the P-wave velocity. This enables us to extract and amplify hidden information embedded in the signal. Using Biot's theory, WT-MRA decomposition results can be divided into contributions from the pore-fluid and the rock framework. Therefore, I proposed a pore-pressure prediction model which is based on the pore-fluid contribution, calculated through WT-MRA, to the P-wave velocity.Open Acces

    Assessing neural network dynamics under normal and altered states of consciousness with MEG : methodological challenges and proposed solutions for atypical power spectra

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    Cette dernière décennie a vu un certain nombre d'avancées significatives en mathématiques, en apprentissage computationnel et en traitement de signal, qui n'ont pas encore été pleinement exploitées en neurosciences. En particulier, l'évaluation de la connectivité dans les réseaux neuronaux peut grandement bénéficier de ces travaux. Nous proposons ici d'exploiter ces outils pour combler partiellement le fossé considérable qui existe encore entre la recherche connectomique à grande échelle (largement centrée sur des mesures indirectes de l'activité cérébrale comme l'Imagerie par résonance magnétique fonctionnelle (IRMf)) et les mesures physiologiques plus directes de l'activité cérébrale. Il est particulièrement important de combler ce fossé pour l'étude des propriétés physiologiques associées à divers états de conscience normaux et anormaux, notamment les troubles psychiatriques, le sommeil, l'anesthésie ou les états induits par les drogues. Les travaux récents sur l'induction d'états de conscience altérés par des agonistes non sélectifs de la sérotonine, tels que la psilocybine et le Diéthyllysergamide (LSD), en sont de bons exemples. Au cours des cinq dernières années, une résurgence rapide de la recherche sur la neurobiologie des tryptamines psychédéliques s'est produite, après une interruption d'un demi-siècle. Bien que ces substances présentent un grand potentiel pour éclairer des aspects jusqu'ici non interrogés du fonctionnement normal et anormal du cerveau, l'ampleur et le caractère inhabituel des changements qu'elles provoquent posent de sérieux défis aux chercheurs. La découverte de méthodes convaincantes et évolutives pour étudier ces données est d'une grande importance si nous voulons tirer parti de la fenêtre unique que ces substances atypiques offrent sur les aspects centraux de la conscience et des fonctions cérébrales anormales. Dans la présente thèse, nous résumons l'état actuel de la neuro-imagerie électrophysiologique en ce qui concerne l'étude des tryptamines psychédéliques, et nous démontrons un certain nombre de lacunes évidentes dans la recherche électrophysiologique actuelle sur les psychédéliques. Nous offrons également quelques modestes contributions méthodologiques au domaine. L'utilité de ces contributions est soutenue par quelques résultats empiriques intrigants, bien que préliminaires. Dans le premier chapitre, nous présentons l'histoire de la recherche neuroscientifique sur le LSD. Il a été rapporté que le LSD induit des déplacements de pics dans les spectres de puissance, en même temps que des diminutions de l'amplitude des pics. Le fait que ces effets soient liés entre eux et que la plupart des recherches menées jusqu'à présent n'aient pas cherché à les distinguer est uniformément négligé dans la littérature, ce qui, selon nous, peut conduire à de fausses interprétations. Le chapitre 2 examine certains des avantages plausibles ainsi que les obstacles sérieux à la recherche sur la connectivité du cerveau entier par magnétoencéphalographie (MEG), et propose plusieurs stratégies pour surmonter ces limites méthodologiques. Celles-ci comprennent des stratégies d'imagerie de source convaincantes, des développements nouveaux et récents dans la décomposition spectrale, des mesures de connectivité insensibles à la conduction volumique, et des implémentations évolutives de métriques de couplage interfréquence bien établies. Nous montrons que ces techniques peuvent être étendues à une grille corticale et sous-corticale de plus haute résolution que celle qui existe actuellement. Nous discutons également d'une mise en œuvre allégée de statistiques non paramétriques adaptées à ces données. Le troisième chapitre a pour but de démontrer l'efficacité de ces procédures, en montrant les résultats empiriques d'une étude de la connectivité du cerveau entier sous LSD par MEG. Le quatrième et dernier chapitre discute de ces résultats, ainsi que des précautions nécessaires et des orientations futures prometteuses pour ce type de recherche. Il propose des approches computationnelles supplémentaires qui pourraient étendre la portée de ces recherches et, plus généralement, de l'électrophysiologie du cerveau entier. Dans l'ensemble, le cadre méthodologique proposé dans ce travail surmonte les limitations endémiques précédentes, non seulement dans la recherche sur les psychédéliques, mais aussi dans la recherche électrophysiologique en général, et jette une lumière nouvelle sur sur les mécanismes centraux qui sous-tendent ces états de conscience anormaux, ainsi que sur les importantes précautions à prendre dans la recherche électrophysiologique.The past decade has seen a number of significant advances in mathematics, computational learning, and signal processing, which have yet to be deployed in neuroscience. In particular the assessment of connectivity in neural networks has much to gain from this work. Here we propose these tools be leveraged to partially bridge the considerable gap that still exists between large-scale connectomics research (largely centered around indirect measures of brain activity such as fMRI), and more direct, physiological measures of brain activity. Bridging this gap is especially important to the study of physiological properties associated with various normal and abnormal states of consciousness including Psychiatric conditions, sleep, anaesthesia or drug-induced states. Exemplary of such research, is recent work surrounding the induction of altered states of consciousness by non-selective serotonin agonists such as Psilocybin and LSD. During the past five years, a rapid resurgence of research into the neurobiology of Psychedelic tryptamines has transpired, following a half-century hiatus. While these substances hold great potential to illuminate hitherto uninterrogated aspects of normal and abnormal brain function, the scope and unusual character of the changes they illicit pose serious challenges to researchers. Uncovering cogent and scalable methods for investigating such data is a matter of great importance if we are to leverage the unique window such atypical substances provide into central aspects of consciousness and abnormal brain function. In the present thesis, we summarize the current state of electrophysiological neuroimaging as it pertains to the study of Psychedelic tryptamines, and demonstrate a number of clear shortcomings in current electrophysiological research on Psychedelics. We also offer some modest methodological contributions to the field. The utility of these contributions is supported by some intriguing, albeit preliminary, empirical findings. In the first chapter, we present the history of neuroscientific research on LSD. LSD has been reported to induce peak shifts in power spectra, alongside decreases in peak amplitude. The fact that these effects are inter-related and most research so far has not sought to disambiguate them is uniformly overlooked in the literature, which we believe may lead to false interpretations. Chapter Two discusses some of the plausible advantages as well as serious barriers to whole-brain connectivity research in MEG, proposing several strategies to overcome these methodological limitations. These include cogent source imaging strategies, novel and recent developments in spectral decomposition, connectivity measures insensitive to volume conduction, and scalable implementations of well-established cross-frequency coupling metrics. We show that these techniques can be extended to a higher resolution cortical and subcortical grid than previously shown. We also discuss a lightweight implementation of non-parametric statistics suitable to such data. Chapter Three serves to demonstrate the efficacy of these procedures, showing empirical results from a whole-brain study of connectivity under LSD in MEG. The fourth and final chapter discusses these results, as well as necessary precautions and promising future directions for this kind of research. It proposes additional computational approaches that might extend the scope of such research and whole-brain electrophysiology more generally. Taken together, the methodological framework proposed in this work overcomes previous limitations endemic not only in Psychedelics research, but electrophysiological research broadly, and sheds new light on central mechanisms underlying these abnormal states of consciousness, as well as important precautions in electrophysiological research

    A contribution to unobtrusive video-based measurement of respiratory signals

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    Due to the growing popularity of video-based methods for physiological signal measurement, and taking into account the technological advancements of these type of devices, this work proposes a series of new novel methods to obtain the respiratory signal from a distance, based on video analysis. This thesis aims to improve the state of the art video methods for respiratory measurement, more specifically, by presenting methods that can be used to obtain respiratory variability or perform respiratory rhythm measurements. Moreover, this thesis also aims to present a new implementation of a time-frequency signal processing technique, to improve its computational efficiency when applied to the respiratory signals. In this document a first approach to video-based methods for respiratory signal measurement is performed, to assert the feasibility of using a consumer-grade camera, not only to measure the mean respiratory rate or frequency, but to assert if this hardware could be used to acquire the raw respiratory signal and the respiratory rhythm as well. In this regard a new video-based method was introduced that measures the respiratory signal of a subject at a distance, with the aid of a custom pattern placed on the thorax of the subject. Given the results from the first video-based method, a more broad approach was taken by comparing three different types of video hardware, with the aim to characterise if they could be used for respiratory signal acquisition and respiratory variability measurements. The comparative analysis was performed in terms of instantaneous frequency, as it allowed to characterise the methods in terms of respiratory variability and to compare them in the same terms with the reference method. Subsequently, and due to the previous obtained results, a new method was proposed using a stereo depth camera with the aim to tackle the limitations of the previous study. The proposed method uses an hybrid architecture were the synchronized infrared frame and depth point-cloud from the same camera are acquired. The infrared frame is used to detect the movements of the subject inside the scene, and to recompute on demand a region of interest to obtain the respiratory signal from the depth point-cloud. Furthermore, in this study an opportunistic approach is taken in order to process all the obtained data, as it is also the aim of this study to verify if using a more realistic approach to respiratory signal analysis in real-life conditions, would influence the respiratory rhythm measurement. Even though the depth camera method proved reliable in terms of respiratory rhythm measurement, the opportunistic approach relied on visual inspection of the obtained respiratory signal to properly define each piece. For this reason, a quality indicator had to be proposed that could objectively identify whenever a respiratory signal contained errors. Furthermore, from the idea to characterise the movements of a subject, and by changing the measuring point from a frontal to a lateral perspective to avoid most of the occlusions, a new method based on obtaining the movement of the thoraco-abdominal region using dense optical flow was proposed. This method makes us of the phase of the optical flow to obtain the respiratory signal of the subject, while using the modulus to compute a quality index. Finally, regarding the different signal processing methods used in this thesis to obtain the instantaneous frequency, there were none that could perform in real-time, making the analysis of the respiratory variability not possible in real-life systems where the signals have to be processed in a sample by sample basis. For this reason, as a final chapter a new implementation of the synchrosqueezing transform for time-frequency analysis in real-time is proposed, with the aim to provide a new tool for non-contact methods to obtain the variability of the respiratory signal in real-time.A causa de la creixent popularitat en la mesura de senyals fisiològics amb mètodes de vídeo, i tenint en compte els avenços tecnològics d'aquests dispositius, aquesta tesi proposa una sèrie de nous mètodes per tal d'obtenir la respiració a distància mitjançant l'anàlisi de vídeo. Aquesta tesi té com a objectiu millorar l'estat de l'art referent a la mesura de senyal respiratòria mitjançant els mètodes que en ella es descriuen, així com presentar mètodes que puguin ser usats per obtenir la variabilitat o el ritme respiratori. A més, aquesta tesi té com a objectiu presentar una nova implementació d'un mètode de processat de senyal temps-freqüencial, per tal de millorar-ne l'eficiència computacional quant s’aplica a senyals respiratoris. En aquest document, es realitza una primera aproximació a la mesura de senyal respiratòria mitjançant mètodes de vídeo per tal de verificar si és factible utilitzar una càmera de consum, no només per mesurar el senyal respiratori, sinó verificar si aquest tipus de hardware també pot ser emprat per obtenir el ritme respiratori. En aquest sentit, es presenta en aquest document un nou mètode d'adquisició de senyal respiratòria a distància basat en vídeo, el qual fa ús d'un patró ubicat al tòrax del subjecte per tal d'obtenir-ne la respiració. Un cop obtinguts els resultats del primers resultats, s'han analitzat tres tipus diferents de càmeres, amb la finalitat de caracteritzar-ne la viabilitat d'obtenir el senyal respiratori i la seva variabilitat. L'estudi comparatiu s'ha realitzat en termes de freqüència instantània, donat que permet caracteritzar els mètodes en termes de variabilitat respiratòria i comparar-los, en les mateixes condicions, amb el mètode de referencia. A continuació, s'ha presentat un nou mètode basat en una càmera de profunditat estèreo amb la finalitat de millorar i corregir les limitacions anteriors. El nou mètode proposat es basa en una arquitectura hibrida la qual utilitza els canals de vídeo infraroig i de profunditat de forma sincronitzada. El canal infraroig s'utilitza per detectar els moviments del subjecte dins l'escena i calcular, sota demanda, una regió d'interès que s'utilitza posteriorment en el canal de profunditat per extreure el senyal respiratori. A més a més, en aquest estudi s'ha utilitzat una aproximació oportunista en el processat del senyal respiratori, donat que també és un dels objectius d'aquest estudi, verificar si el fet d'utilitzar una aproximació més realista en l'adquisició de senyal, pot influir en la mesura del ritme respiratori. Tot i que el mètode anterior es mostra fiable en termes de mesura del ritme respiratori, la selecció oportunista del senyal necessita d’inspecció visual per tal de definir correctament cada fragment. Per aquest motiu, era necessari definir un índex de qualitat el qual permetés identificar de forma objectiva cada tram de senyal, així com detectar si el senyal conté errors. Partint de la idea de caracteritzar el moviment del subjecte de l'estudi anterior, i modificant el punt de mesura frontal cap a un de lateral per tal d'evitar oclusions, es proposa un nou mètode basat en l'obtenció del moviment toràcic-abdominal a partir del flux òptic del senyal de vídeo. Aquest mètode recupera el senyal respiratori del subjecte a partir de la fase del flux òptic, tot calculant un índex de qualitat a partir del mòdul. Finalment, i tenint en compte els diferents mètodes de processat utilitzats en aquesta tesi per tal de obtenir la freqüència instantània, es pot apreciar que cap d'ells és capaç de funcionar en temps real, fent inviable l'anàlisi de la variabilitat respiratòria en sistemes reals amb processat mostra a mostra. Per aquest motiu, en el capítol final d'aquesta tesi, s'ha proposat una nova implementació de la transformació "synchrosqueezing" per tal de realitzar l’anàlisi temporal-freqüencial en temps real, i proveir d'una nova eina per tal d'obtenir la variabilitat respiratòria en temps real, amb mètodes sense contacte

    Multichannel analysis of normal and continuous adventitious respiratory sounds for the assessment of pulmonary function in respiratory diseases

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    Premi extraordinari doctorat UPC curs 2015-2016, àmbit d’Enginyeria IndustrialRespiratory sounds (RS) are produced by turbulent airflows through the airways and are inhomogeneously transmitted through different media to the chest surface, where they can be recorded in a non-invasive way. Due to their mechanical nature and airflow dependence, RS are affected by respiratory diseases that alter the mechanical properties of the respiratory system. Therefore, RS provide useful clinical information about the respiratory system structure and functioning. Recent advances in sensors and signal processing techniques have made RS analysis a more objective and sensitive tool for measuring pulmonary function. However, RS analysis is still rarely used in clinical practice. Lack of a standard methodology for recording and processing RS has led to several different approaches to RS analysis, with some methodological issues that could limit the potential of RS analysis in clinical practice (i.e., measurements with a low number of sensors, no controlled airflows, constant airflows, or forced expiratory manoeuvres, the lack of a co-analysis of different types of RS, or the use of inaccurate techniques for processing RS signals). In this thesis, we propose a novel integrated approach to RS analysis that includes a multichannel recording of RS using a maximum of five microphones placed over the trachea and the chest surface, which allows RS to be analysed at the most commonly reported lung regions, without requiring a large number of sensors. Our approach also includes a progressive respiratory manoeuvres with variable airflow, which allows RS to be analysed depending on airflow. Dual RS analyses of both normal RS and continuous adventitious sounds (CAS) are also proposed. Normal RS are analysed through the RS intensity–airflow curves, whereas CAS are analysed through a customised Hilbert spectrum (HS), adapted to RS signal characteristics. The proposed HS represents a step forward in the analysis of CAS. Using HS allows CAS to be fully characterised with regard to duration, mean frequency, and intensity. Further, the high temporal and frequency resolutions, and the high concentrations of energy of this improved version of HS, allow CAS to be more accurately characterised with our HS than by using spectrogram, which has been the most widely used technique for CAS analysis. Our approach to RS analysis was put into clinical practice by launching two studies in the Pulmonary Function Testing Laboratory of the Germans Trias i Pujol University Hospital for assessing pulmonary function in patients with unilateral phrenic paralysis (UPP), and bronchodilator response (BDR) in patients with asthma. RS and airflow signals were recorded in 10 patients with UPP, 50 patients with asthma, and 20 healthy participants. The analysis of RS intensity–airflow curves proved to be a successful method to detect UPP, since we found significant differences between these curves at the posterior base of the lungs in all patients whereas no differences were found in the healthy participants. To the best of our knowledge, this is the first study that uses a quantitative analysis of RS for assessing UPP. Regarding asthma, we found appreciable changes in the RS intensity–airflow curves and CAS features after bronchodilation in patients with negative BDR in spirometry. Therefore, we suggest that the combined analysis of RS intensity–airflow curves and CAS features—including number, duration, mean frequency, and intensity—seems to be a promising technique for assessing BDR and improving the stratification of BDR levels, particularly among patients with negative BDR in spirometry. The novel approach to RS analysis developed in this thesis provides a sensitive tool to obtain objective and complementary information about pulmonary function in a simple and non-invasive way. Together with spirometry, this approach to RS analysis could have a direct clinical application for improving the assessment of pulmonary function in patients with respiratory diseases.Los sonidos respiratorios (SR) se generan con el paso del flujo de aire a través de las vías respiratorias y se transmiten de forma no homogénea hasta la superficie torácica. Dada su naturaleza mecánica, los SR se ven afectados en gran medida por enfermedades que alteran las propiedades mecánicas del sistema respiratorio. Por lo tanto, los SR proporcionan información clínica relevante sobre la estructura y el funcionamiento del sistema respiratorio. La falta de una metodología estándar para el registro y procesado de los SR ha dado lugar a la aparición de diferentes estrategias de análisis de SR con ciertas limitaciones metodológicas que podrían haber restringido el potencial y el uso de esta técnica en la práctica clínica (medidas con pocos sensores, flujos no controlados o constantes y/o maniobras forzadas, análisis no combinado de distintos tipos de SR o uso de técnicas poco precisas para el procesado de los SR). En esta tesis proponemos un método innovador e integrado de análisis de SR que incluye el registro multicanal de SR mediante un máximo de cinco micrófonos colocados sobre la tráquea yla superficie torácica, los cuales permiten analizar los SR en las principales regiones pulmonares sin utilizar un número elevado de sensores . Nuestro método también incluye una maniobra respiratoria progresiva con flujo variable que permite analizar los SR en función del flujo respiratorio. También proponemos el análisis combinado de los SR normales y los sonidos adventicios continuos (SAC), mediante las curvas intensidad-flujo y un espectro de Hilbert (EH) adaptado a las características de los SR, respectivamente. El EH propuesto representa un avance importante en el análisis de los SAC, pues permite su completa caracterización en términos de duración, frecuencia media e intensidad. Además, la alta resolución temporal y frecuencial y la alta concentración de energía de esta versión mejorada del EH permiten caracterizar los SAC de forma más precisa que utilizando el espectrograma, el cual ha sido la técnica más utilizada para el análisis de SAC en estudios previos. Nuestro método de análisis de SR se trasladó a la práctica clínica a través de dos estudios que se iniciaron en el laboratorio de pruebas funcionales del hospital Germans Trias i Pujol, para la evaluación de la función pulmonar en pacientes con parálisis frénica unilateral (PFU) y la respuesta broncodilatadora (RBD) en pacientes con asma. Las señales de SR y flujo respiratorio se registraron en 10 pacientes con PFU, 50 pacientes con asma y 20 controles sanos. El análisis de las curvas intensidad-flujo resultó ser un método apropiado para detectar la PFU , pues encontramos diferencias significativas entre las curvas intensidad-flujo de las bases posteriores de los pulmones en todos los pacientes , mientras que en los controles sanos no encontramos diferencias significativas. Hasta donde sabemos, este es el primer estudio que utiliza el análisis cuantitativo de los SR para evaluar la PFU. En cuanto al asma, encontramos cambios relevantes en las curvas intensidad-flujo yen las características de los SAC tras la broncodilatación en pacientes con RBD negativa en la espirometría. Por lo tanto, sugerimos que el análisis combinado de las curvas intensidad-flujo y las características de los SAC, incluyendo número, duración, frecuencia media e intensidad, es una técnica prometedora para la evaluación de la RBD y la mejora en la estratificación de los distintos niveles de RBD, especialmente en pacientes con RBD negativa en la espirometría. El método innovador de análisis de SR que se propone en esta tesis proporciona una nueva herramienta con una alta sensibilidad para obtener información objetiva y complementaria sobre la función pulmonar de una forma sencilla y no invasiva. Junto con la espirometría, este método puede tener una aplicación clínica directa en la mejora de la evaluación de la función pulmonar en pacientes con enfermedades respiratoriasAward-winningPostprint (published version

    Non-Oscillatory Pattern Learning for Non-Stationary Signals

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    This paper proposes a novel non-oscillatory pattern (NOP) learning scheme for several oscillatory data analysis problems including signal decomposition, super-resolution, and signal sub-sampling. To the best of our knowledge, the proposed NOP is the first algorithm for these problems with fully non-stationary oscillatory data with close and crossover frequencies, and general oscillatory patterns. NOP is capable of handling complicated situations while existing algorithms fail; even in simple cases, e.g., stationary cases with trigonometric patterns, numerical examples show that NOP admits competitive or better performance in terms of accuracy and robustness than several state-of-the-art algorithms

    Contributions au traitement des images multivariées

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    Ce mémoire résume mon activité pédagogique et scientifique en vue de l’obtention de l’habilitation à diriger des recherches

    A Statistical Perspective of the Empirical Mode Decomposition

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    This research focuses on non-stationary basis decompositions methods in time-frequency analysis. Classical methodologies in this field such as Fourier Analysis and Wavelet Transforms rely on strong assumptions of the underlying moment generating process, which, may not be valid in real data scenarios or modern applications of machine learning. The literature on non-stationary methods is still in its infancy, and the research contained in this thesis aims to address challenges arising in this area. Among several alternatives, this work is based on the method known as the Empirical Mode Decomposition (EMD). The EMD is a non-parametric time-series decomposition technique that produces a set of time-series functions denoted as Intrinsic Mode Functions (IMFs), which carry specific statistical properties. The main focus is providing a general and flexible family of basis extraction methods with minimal requirements compared to those within the Fourier or Wavelet techniques. This is highly important for two main reasons: first, more universal applications can be taken into account; secondly, the EMD has very little a priori knowledge of the process required to apply it, and as such, it can have greater generalisation properties in statistical applications across a wide array of applications and data types. The contributions of this work deal with several aspects of the decomposition. The first set regards the construction of an IMF from several perspectives: (1) achieving a semi-parametric representation of each basis; (2) extracting such semi-parametric functional forms in a computationally efficient and statistically robust framework. The EMD belongs to the class of path-based decompositions and, therefore, they are often not treated as a stochastic representation. (3) A major contribution involves the embedding of the deterministic pathwise decomposition framework into a formal stochastic process setting. One of the assumptions proper of the EMD construction is the requirement for a continuous function to apply the decomposition. In general, this may not be the case within many applications. (4) Various multi-kernel Gaussian Process formulations of the EMD will be proposed through the introduced stochastic embedding. Particularly, two different models will be proposed: one modelling the temporal mode of oscillations of the EMD and the other one capturing instantaneous frequencies location in specific frequency regions or bandwidths. (5) The construction of the second stochastic embedding will be achieved with an optimisation method called the cross-entropy method. Two formulations will be provided and explored in this regard. Application on speech time-series are explored to study such methodological extensions given that they are non-stationary
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