Thesis (Ph.D.)--University of Delaware, 2006. / Principal faculty advisor: Kenneth E. Barner, Dept. of Electrical and Computer Engineering. Includes bibliographical references.

Thesis (Ph.D.)--Memorial University of Newfoundland, 2003. / Bibliography: leaves 232-247.

Thesis (M.Eng.)--Memorial University of Newfoundland, 2003. / Bibliography: leaves 254-261.

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 110-118).

Thesis (Ph. D.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains xiv, 85 p. : ill. Includes abstract. Includes bibliographical references (p. 83-85).

Thesis (M.S.)--Rochester Institute of Technology, 2006. / Typescript. Includes bibliographical references (leaves 50-53).

Thesis (M.Sc.)--York University, 2005. Graduate Programme in Industrial & Applied Mathematics. / Typescript. Includes bibliographical references (leaves 88-92). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url%5Fver=Z39.88-2004&res%5Fdat=xri:pqdiss &rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:MR11806

Στην παρούσα μεταπτυχιακή διπλωματική εργασία η κυματιδιακή ανάλυση (wavelet analysis) εφαρμόζεται σε ηλεκτροεγκεφαλικές καταγραφές μαρτύρων με επιληψία με σκοπό τη μελέτη των δυναμικών αλλαγών της ηλεκτρικής δραστηριότητας στο πεδίο του χρόνου και της συχνότητας. Συγκεκριμένα, χρησιμοποιείται ο Διακριτός Μετασχηματισμός Κυματιδίου (ΔΜΚ) το συγκριτικό πλεονέκτημα του οποίου συνίσταται στην ικανότητά του να επεξεργάζεται μη στάσιμα σήματα, όπως αυτά των ηλεκτροεγκεφαλικών καταγραφών, με βέλτιστη διακριτική ικανότητα στο πεδίο συχνότητας-χρόνου. Ο ΔΜΚ ενός σήματος αποτελεί τη δισδιάστατη αναπαράστασή του (χρόνος-συχνότητα), πάνω στην οποία μπορεί να βασιστεί ο υπολογισμός ποσοτικών δεικτών δυναμικών αλλαγών. Για το σκοπό αυτό χρησιμοποιήθηκαν δυο διαφορετικές προσεγγίσεις: α) ο καθορισμός του χρόνου και η διερεύνηση του συχνοτικού περιεχομένου οδήγησε στον προσδιορισμό μεγεθών εντροπίας και στατιστικής πολυπλοκότητας χαρακτηρίζοντας σφαιρικά το σήμα και β) ο καθορισμός του συχνοτικού περιεχομένου και, ξεχωριστά για κάθε εύρος συχνοτήτων, η διερεύνηση στο χρόνο επέτρεψε τον υπολογισμό ενός στατιστικού μεγέθους απόστασης, της απόκλισης κατά Jensen-Shannon (JSD) χαρακτηρίζοντας τοπικά το σήμα. Η αντιπαραβολή των αποτελεσμάτων αποκαλύπτει μείωση της εντροπίας με σύγχρονη αύξηση της πολυπλοκότητας συνηγορώντας υπέρ μιας κατάστασης υψηλής τάξης και οργάνωσης κατά τη διάρκεια της επιληπτικής κρίσης. Επιπλέον, το μέτρο απόστασης JSD αναδεικνύει μορφολογικές διαφοροποιήσεις, χαρακτηριστικές των διαφόρων σταδίων της κρίσης για τους φυσιολογικούς ρυθμούς του εγκεφάλου δ, θ, α, β και γ. / In this project wavelet analysis is applied to EEG signals of epileptic subjects for the estimation of dynamical changes of the electrical activity in time and frequency. To this end, the discrete wavelet transform (DWT) was used. The DWT of 1D-signal provides a 2D- representation (time-frequency plane), which can be used to define useful quantifiers for characterization of dynamical changes. In particular, two different approaches were used: a) fixing the time and scanning the plane on the frequency-coordinate. Wavelet coefficients at all considered frequency bands were used for definition of entropy and statistical complexity quantifiers. They provide a global description of the signal dynamical changes taken into account the interrelation of all the frequency bands contained in the signal and b) fixing the frequency and scanning in the time-coordinates. Wavelet coefficients corresponding to a given time interval were used to define the Jensen-Shannon divergence JSD, a statistical distance measure. They, in turn, provide a local description of the frequency band dynamical changes. In the first case, the decrease of entropy in association to the increase of complexity during seizure reflected the presence of brain states that are characterized by both order and maximal complexity during the epileptic seizures. Furthermore, the study of JSD in each frequency band separately revealed morphological and dynamical changes (brain rhythms δ, θ, α, β and γ) that can be matched to time instants typical of the transitions between the different stages of the epileptic seizure.

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Made available in DSpace on 2014-06-11T19:30:51Z (GMT). No. of bitstreams: 0 Previous issue date: 2005-02-01Bitstream added on 2014-06-13T20:00:56Z : No. of bitstreams: 1 duarte_maq_dr_ilha.pdf: 2208096 bytes, checksum: 7daf91683010b0f39c715c9cc1ded5d8 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Neste trabalho é feito um estudo sobre os métodos de redução de ruído aditivo em sinais de voz baseados em wavelets e, através deste estudo, propõe-se um novo método de redução de ruído em sinais de voz no domínio wavelet. O princípio básico da maioria dos métodos de redução de ruído baseados em wavelets é a determinação e aplicação de um limiar, que permite bons resultados para sinais contaminados por ruído branco, mas não são eficientes no processamento de sinais contaminados por ruído colorido, que é o tipo de ruído mais comum em situações reais. Nesses métodos, o limiar, geralmente, é calculado nos intervalos de silêncio e aplicado em todo o sinal. Os coeficientes no domínio wavelet são comparados com este limiar e aqueles que estão abaixo deste valor são eliminados, fazendo assim uma aplicação linear deste limiar. Esta eliminação acaba causando descontinuidades no tempo e na freqüência no sinal processado. Além disso, a forma com que o limiar é calculado pode degradar os trechos de voz do sinal processado, principalmente nos casos em que o limiar depende fortemente da última janela do último trecho de silêncio. O método proposto neste trabalho também é baseado em corte por limiar, mas em vez de uma aplicação linear do limiar, ele faz uma aplicação não-linear, o que evita as descontinuidades causadas por outros algoritmos. O limiar é calculado nos trechos de silêncio e não depende apenas da última janela do último trecho de silêncio, mas sim de todas as janelas, já que este limiar é uma média de todos os limiares calculados neste trecho. Isto faz com que a redução do ruído seja mais uniforme e introduza menos distorções no sinal processado. Além disso, nos trechos de voz ainda é calculado um novo limiar que também será usado, em conjunto com o limiar calculado no silêncio. Isto faz com que a energia da janela que... . / In this work a study of additive noise reduction in speech based on wavelets is presented and, based on this study a new noise reduction method in speech in the wavelet domain is proposed. The basic idea of most methods of noise reduction based on wavelets is the determination and application of a threshold, that produces good results for signals contaminated by white noise, but they are not very efficient in processing signals contaminated by colored noise, which is more common in real situations. In those methods, the threshold, generally, is calculated in the silence intervals and applied to the whole signal. The coefficients in the wavelet domain are compared with this threshold and those that are below this value are eliminated, making a linear application of this threshold. This elimination causes discontinuities in time and frequency of the processed signal. Besides, the way that the threshold is computed can degrade the voice segments of the processed signal, principally when the threshold depends strongly on the last window of the last silence segment. The proposed method in this work is also based in thresholding, but, instead of a linear application of the threshold, it makes a non-linear application, which avoids the discontinuities caused by other algorithms. The threshold is calculated in the silence segments and is not dependent only on the last window of the last silence segment, but of all the windows, since this threshold is an average of all thresholds calculated in this segment. It makes noise reduction more uniform and introduces less distortion in the processed signal. Besides, in the voice segments a new threshold is calculated that will be also used with the threshold calculated in the silence. It makes that the energy of the window that is being processed is also considered. This way, it is... (Complete abstract, click electronic address below).