Ο βήχας είναι ένα κοινό σύμπτωμα σε πολλές ασθένειες του αναπνευστικού συστήματος. Αν και λειτουργεί ως προστατευτικός μηχανισμός απομάκρυνσης εκκρίσεων από την αναπνευστική οδό, η αυξημένη συχνότητα και έντασή του μπορεί να έχουν επίδραση στην ποιότητα ζωής του ασθενούς. Είναι το βασικότερο σύμπτωμα για το οποίο κάποιος επισκέπτεται τον γιατρό. Η σωστή εκτίμησή του είναι απαραίτητη τόσο για τον προσδιορισμό της αποτελεσματικότητας της θεραπείας αλλά και για την δοκιμή νέων θεραπειών και τη μελέτη των μηχανισμών του. Μέχρι στιγμής η διάγνωσή του βασίζεται σε υποκειμενικές καταγραφές, απλώς ζητώντας από τον ασθενή την εκτίμησή του για την ένταση, τη διάρκεια και τη συχνότητά του. Ένα σύστημα αυτόματης ανίχνευσης του σήματος του βήχα θα επέτρεπε την επικύρωση της παρουσίας και της συχνότητας του βήχα καθώς και την αποτελεσματικότητα της αγωγής. Τα συστήματα καταγραφής του βήχα δεν είναι καινούρια διαδικασία. Η πρώτη καταγραφή έγινε τη δεκαετία του '60 σε νοσηλευόμενους ασθενείς με τη χρήση μαγνητοφώνων και με χειροκίνητη καταγραφή των γεγονότων του βήχα. Στη συνέχεια με την εξέλιξη της τεχνολογίας κατασκευάστηκαν φορητές συσκευές που βασίστηκαν στην ταυτόχρονη καταγραφή ήχου και ηλεκτρομυογραφήματος (EMG σήματα, ανίχνευση κίνησης του θώρακα) για να ανιχνευθούν τα γεγονότα του βήχα όπου ακόμα τα σήματα έπρεπε να καταγραφούν και να μετρηθούν χειροκίνητα. Με τις παραπέρα ανακαλύψεις στις τεχνικές ψηφιακής καταγραφής, συμπίεσης και αποθήκευσης η διαδικασία αναγνώρισης και καταγραφής του βήχα μπορεί να αυτοματοποιηθεί με τη χρήση κατάλληλων αλγορίθμων. Στην εργασία αυτή κατασκευάζεται ένα ενσωματωμένο σύστημα καταγραφής, αποθήκευσης και επεξεργασίας του σήματος του βήχα. Για την επεξεργασία του αναπτύσσεται μια βασική μέθοδος βασισμένη την ενέργειά του. Στο πρώτο κεφάλαιο, γίνεται αναφορά στα χαρακτηριστικά του ηχητικού σήματος και παρατίθενται τα βασικά στάδια της ανάλυσής του. Στο δεύτερο, δίνεται ο ορισμός του βήχα, οι αιτίες που τον προκαλούν και η φυσιολογία του. Στη συνέχεια, παρουσιάζονται μέθοδοι για την ανίχνευσή του. Στο τρίτο κεφάλαιο, αναφέρονται οι βασικές έννοιες των μικροελεγκτών και παρατίθενται τα βασικά χαρακτηριστικά του μικροεπεξεργαστή ARM7TDMI, του μικροελεγκτή ADuC 7026 και της αναπτυξιακής πλατφόρμας μVision της Keil που χρησιμοποιήσαμε για την ανάπτυξη της εφαρμογής μας. Στο τελευταίο κεφάλαιο, παρουσιάζονται κάποιες λειτουργίες προγραμματισμού και δυνατότητες του μικροελεγκτή που χρησιμοποιούνται στην παρούσα εργασία. Στη συνέχεια αναπτύσσεται η εφαρμογή για την ανίχνευση του σήματος του βήχα. Στα παραρτήματα, παρατίθενται παραδείγματα για τον βασικό προγραμματισμό του ADuC 7026 και των περιφερειακών του. / Cough is a common symptom in many diseases of the respiratory system. Although cough protects humans by removing secretions through respiratory track, its increased frequency and intensity may impact on patient’s quality of life. Besides, cough is the main symptom that makes people to visit doctor. Doctor’s accurate assessment is necessary for determining treatment according to each patient as well as the testing of new treatments and the comprehensive study of cough. So far, the diagnosis of cough is based on subjective factors such as patient’s assessment of its intensity, duration and frequency. An automatic detection system of the cough signal would allow to determine about the presence and frequency of cough and an effective treatment. The attempt to record cough is not a new process. The first recording to patients was made in 60’s by the use of tape recorders and by manually recording the symptoms of cough. As technology has evolved, portable device was created that was based on simultaneous recording of sound and electromyography (EMG signals, detection of the movement of chest) in order to detect the symptoms of cough. In that case, the recording was also made manually. With the evolution of digital recording, compression and storage, the recognition and recording of cough can be automated by using the appropriate algorithms. In that study, an embedded system is made in order to record, store and process the signal of cough. A basic method based on energy is developed for the process. In the fisrt chapter, the characteristics of sound signal and the key stages of the analysis are presented. In the second chapter, cough is defined, its causes and the clinical symptoms of cough are analyzed. Then, methods for its detection are specified. In the third chapter, the basic concepts of microcontrollers are given as well as the main characteristics of microprocessor ARM7TDMI and of microcontroller ADuC 7026 are presented and the platform μVision of Keil that we used for the development of the application is analyzed. In the final chapter, some programming functions and properties of microcontroller are presented and are used for the current study. Then, the application of detection of cough signal is developed. In annex, examples for the basic programming of ADuC 7026 and its peripherals are given.

Ce travail développe des techniques non-supervisées de détection et de localisation en ligne de ruptures dans les signaux enregistrés dans un environnement bruité. Ces techniques reposent sur l'association d'une approche algébrique avec la TVE. L'approche algébrique permet d'appréhender aisément les ruptures en les caractérisant en termes de distributions de Dirac retardées et leurs dérivées dont la manipulation est facile via le calcul opérationnel. Cette caractérisation algébrique, permettant d'exprimer explicitement les instants d'occurrences des ruptures, est complétée par une interprétation probabiliste en termes d'extrêmes : une rupture est un évènement rare dont l'amplitude associée est relativement grande. Ces évènements sont modélisés dans le cadre de la TVE, par une distribution de Pareto Généralisée. Plusieurs modèles hybrides sont proposés dans ce travail pour décrire à la fois le comportement moyen (bruit) et les comportements extrêmes (les ruptures) du signal après un traitement algébrique. Des algorithmes entièrement non-supervisés sont développés pour l'évaluation de ces modèles hybrides, contrairement aux techniques classiques utilisées pour les problèmes d'estimation en question qui sont heuristiques et manuelles. Les algorithmes de détection de ruptures développés dans cette thèse ont été validés sur des données générées, puis appliqués sur des données réelles provenant de différents phénomènes, où les informations à extraire sont traduites par l'apparition de ruptures. / This work develops non supervised techniques for on-line detection and location of change-points in noisy recorded signals. These techniques are based on the combination of an algebraic approach with the Extreme Value Theory (EVT). The algebraic approach offers an easy identification of the change-points. It characterizes them in terms of delayed Dirac distributions and their derivatives which are easily handled via operational calculus. This algebraic characterization, giving rise to an explicit expression of the change-points locations, is completed with a probabilistic interpretation in terms of extremes: a change point is seen as a rare and extreme event. Based on EVT, these events are modeled by a Generalized Pareto Distribution.Several hybrid multi-components models are proposed in this work, modeling at the same time the mean behavior (noise) and the extremes ones (change-points) of the signal after an algebraic processing. Non supervised algorithms are proposed to evaluate these hybrid models, avoiding the problems encountered with classical estimation methods which are graphical ad hoc ones. The change-points detection algorithms developed in this thesis are validated on generated data and then applied on real data, stemming from different phenomenons, where change-points represent the information to be extracted.

Em Biomedicina, a detecção e a quanticação de anormalidades presentes num sinal são desejáveis. Uma estratégia de codicação baseada em extração de características, tais como picos ou frequências, pode não capturar todas as irregularidades. Assim, uma representação baseada em funções de base denidas com conhecimento a priori do sinal pode ser mais precisa para aplicações biomédicas. A escolha das funções base depende da natureza siológica do sinal e de suas peculiaridades. Sinais de eletrocardiograma (ECG) e eletroencefalograma (EEG) exibem características bem denidas. ECG, por exemplo, é um sinal elétrico composto de uma forma de onda especíca (P, QRS e T). Se as características de um sinal a ser sintetizado são bem compreendidas, é possível derivar uma assinatura para o sinal. Uma codicação apropriada permite a extração de parâmetros relevantes para sua análise, tais como anormalidades num ciclo cardíaco representadas por uma alteração no sinal de ECG, ou então uma excitação das ondas cerebrais representada por uma modicação no sinal de EEG. O objetivo deste projeto é introduzir uma nova técnica de codicação de sinais, que representa um sinal pela soma de funções sigmoides para aproximar iterativamente o sinal medido, com foco em aplicações biomédicas. Funções sigmoides tendem a reproduzir bem as grandes variações presentes em sinais biomédicos, daí a escolha de usá-las na codicação deste tipo de sinal. Serão explorados o nível de compressão dos dados, bem como a taxa de convergência. A técnica desenvolvida será comparada com técnicas convencionais de codicação e sua robustez será avaliada. Uma estratégia de codicação ótima pode trazer benefícios não só para a compressão, mas também na criação de assinaturas de sinais representando tanto condições siológicas normais como patológicas. / In Biomedicine, detection and quantication of abnormalities present in a signal are desired. An encoding strategy based on feature extraction, such as peaks or frequencies, may not capture all irregularities. Thus, a function-based representation, constructed using a priori knowledge of signal characteristics, may be more accurate for biomedical applications. The choice of the basis function depends on the physiological nature of the signal and its specic features. Electrocardiogram (ECG) and electroencephalogram (EEG) signals exhibit well-dened characteristics. ECG, for instance, is an electrical signal composed of specic waveform (P, QRS, and T). If the characteristics of a signal to be synthesized are well understood, its possible to derive a signal signature. An appropriate encoding allows the extraction of parameters relevant for its analysis, such as, abnormalities in a cardiac cycle represented by an alteration in the ECG signal, or an excitation of the brain waves represented by a modication of the EEG. The objective of this project is to introduce a novel signal encoding technique that represents a signal by a sum of sigmoidal functions to iteratively approximate the measured signal, targeted at biomedical applications. Sigmoidal functions tend to reproduce well large variations in biomedical signals, hence their use for coding this type of signal. We explore the data compression level as well as the convergence rate. We also compare it to conventional encoding techniques and assess the robustness of this model. An optimal encoding strategy may bring not only benets in compression, but also in the creation of signatures for signals representing both physiological and pathological conditions.

Made available in DSpace on 2014-12-17T14:55:52Z (GMT). No. of bitstreams: 1 JoaoMTL_DISSERT.pdf: 5377292 bytes, checksum: 39e8ffd078c1ad7b51b4db49d8d4f654 (MD5) Previous issue date: 2011-06-30 / The great diversity in the architecture of biomedical devices, coupled with their different communication protocols, has hindered the implementation of systems that need to make access to these devices. Given these differences, the need arises to provide access to such a transparent manner. In this sense, this paper proposes an embedded architecture, service-oriented, for access to biomedical devices, as a way to abstract the mechanism for writing and reading data on these devices, thereby contributing to the increase in quality and productivity of biomedical systems so as to enable that, the focus of the development team of biomedical software, is almost exclusively directed to its functional requirements / A grande diversidade na arquitetura de dispositivos biom?dicos, aliada aos seus diferentes protocolos de comunica??o, tem dificultado a implementa??o de sistemas que necessitam realizar o acesso a esses dispositivos. Diante dessas diferen?as, surge a necessidade de prover o acesso a esses de forma transparente. Neste sentido, o presente trabalho prop?e uma arquitetura embarcada, orientada a servi?o, para acesso a dispositivos biom?dicos, como forma de abstrair o mecanismo de escrita e leitura de dados nesses dispositivos, contribuindo desta maneira, para o aumento na qualidade e produtividade dos sistemas biom?dicos, de forma a possibilitar com que, o foco da equipe de desenvolvimento de softwares biom?dicos, seja quase que exclusivamente voltado aos seus requisitos funcionais

[ES] Esta tesis aborda el problema de fusión de datos en el ámbito de la neurociencia. El objetivo principal de este estudio es la fusión de modalidades, con énfasis en la fusión bimodal de señales biomédicas fMRI+EEG y de ECG+EEG. Las técnicas de fusión de datos tienen como objetivo alcanzar la exactitud y precisión en la toma de decisiones que sería más difícil con una sola modalidad. Hemos hecho una extensa revisión bibliográfica que contempla la fusión temprana y la fusión tardía de la siguiente manera: fusión temprana a nivel de sensores; fusión temprana a nivel de características; fusión tardía a nivel de scores; y fusión tardía a nivel de decisiones. En cada uno de esos apartados se presenta una tabla comparativa con las debilidades y fortalezas de cada método, así como los trabajos más citados. También hemos hecho aportes teóricos en esta área abordando el tema de la comparación entre la fusión temprana y la fusión tardía (soft y hard) para un problema multimodal de dos clases, dando elementos sobre la opción más adecuada a la hora de seleccionar la fusión temprana o tardía. Para este análisis hemos asumido inicialmente el conocimiento de los modelos utilizados., para después considerar modelos donde hay que estimar una serie de parámetros a partir de un conjunto de entrenamiento. El análisis se ha hecho para datos incorrelados y se ha extendido a datos con matrices de covarianza arbitrarias. Hemos realizado un estudio experimental como complemento del capítulo teórico. A partir de cuatro experimentos diferentes se destaca la efectividad de la fusión de datos multimodales para la mejora del rendimiento de los clasificadores. Los métodos de fusión y los clasificadores probados mostraron consistentemente un rendimiento superior en términos de métricas como el F1 score, la precisión, AUC y APR, en comparación con el uso de una sola modalidad de datos. Los resultados logrados subrayan la importancia de la fusión de datos en aplicaciones neurocientíficas y abren nuevas posibilidades para el desarrollo de sistemas de diagnóstico más precisos y robustos. / [CA] Aquesta tesi aborda el problema de la fusió de dades en l'àmbit de la neurociència. L'objectiu principal d'aquest estudi és la fusió de modalitats, amb èmfasi en la fusió bimodal de senyals biomèdiques fMRI+EEG i d'ECG+EEG. Les tècniques de fusió de dades tenen com a objectiu assolir l'exactitud i precisió en la presa de decisions que seria més difícil amb una sola modalitat. Hem fet una extensa revisió bibliogràfica que contempla la fusió primerenca i la fusió tardana de la següent manera: fusió primerenca a nivell de sensors; fusió primerenca a nivell de característiques; fusió tardana a nivell de puntuacions; i fusió tardana a nivell de decisions. En cadascun d'aquests apartats es presenta una taula comparativa amb les debilitats i fortaleses de cada mètode, així com els treballs més citats. També hem fet aportacions teòriques en aquesta àrea abordant el tema de la comparació entre la fusió primerenca i la fusió tardana (suau i dura) per a un problema multimodal de dues classes, donant elements sobre l'opció més adequada a l'hora de seleccionar la fusió primerenca o tardana. Per a aquesta anàlisi, hem assumit inicialment el coneixement dels models utilitzats, per després considerar models on cal estimar una sèrie de paràmetres a partir d'un conjunt d'entrenament. L'anàlisi s'ha fet per a dades incorrelades i s'ha estès a dades amb matrius de covariància arbitràries. Hem realitzat un estudi experimental com a complement del capítol teòric. A partir de quatre experiments diferents es destaca l'efectivitat de la fusió de dades multimodals per a la millora del rendiment dels classificadors. Els mètodes de fusió i els classificadors provats van mostrar constantment un rendiment superior en termes de mètriques com el F1 score, la precisió, AUC i APR, en comparació amb l'ús d'una sola modalitat de dades. Els resultats obtinguts subratllen la importància de la fusió de dades en aplicacions neurocientífiques i obrin noves possibilitats per al desenvolupament de sistemes de diagnòstic més precisos i robusts. / [EN] This thesis addresses the problem of data fusion in the field of neuroscience. The main objective of this study is to explore multimodal fusion, with an emphasis on bimodal fusion of biomedical signals such as fMRI+EEG and ECG+EEG. Data fusion techniques aim to achieve accuracy and precision in decision-making that would be more challenging with a single modality. We have conducted an extensive literature review covering early fusion and late fusion, as follows: early fusion at the sensor level, early fusion at the feature level, late fusion at the score level, and late fusion at the decision level. In each of these sections, we present a comparative table outlining the strengths and weaknesses of each method, as well as the most cited works. We have also made theoretical contributions to this area by addressing the comparison between early and late fusion (both soft and hard) for a two-class multimodal problem, providing insights into the most suitable choice between early and late fusion. For this analysis, we initially assumed knowledge of the models used, then considered scenarios where a series of parameters must be estimated from a training set. The analysis was conducted for uncorrelated data and extended to data with arbitrary covariance matrices. We conducted an experimental study to complement the theoretical chapter. Based on four different experiments, the effectiveness of multimodal data fusion in enhancing classifier performance was highlighted. The tested fusion methods and classifiers consistently demonstrated superior performance in terms of metrics such as F1 score, precision, AUC, and APR compared to using a single data modality. The results emphasize the importance of data fusion in neuroscientific applications and open up new possibilities for developing more accurate and robust diagnostic systems. / Pereira González, LM. (2024). Nuevas contribuciones en aplicaciones de fusión multimodal de bioseñales [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/213614