In an unsupervised learning problem, one is given an unlabelled dataset and hopes to find some hidden structure; the prototypical example is clustering similar data. Such problems often arise in machine learning and statistics, but also in signal processing, theoretical computer science, and any number of quantitative scientific fields. The distinguishing feature of unsupervised learning is that there are no privileged variables or labels which are particularly informative, and thus the greatest challenge is often to differentiate between what is relevant or irrelevant in any particular dataset or problem. In the course of this thesis, we study a number of problems which span the breadth of unsupervised learning. We make progress in Gaussian mixtures, independent component analysis (where we solve the open problem of underdetermined ICA), and we formulate and solve a feature selection/dimension reduction model. Throughout, our goal is to give finite sample complexity bounds for our algorithms -- these are essentially the strongest type of quantitative bound that one can prove for such algorithms. Some of our algorithmic techniques turn out to be very efficient in practice as well. Our major technical tool is tensor spectral decomposition: tensors are generalisations of matrices, and often allow access to the "fine structure" of data. Thus, they are often the right tools for unravelling the hidden structure in an unsupervised learning setting. However, naive generalisations of matrix algorithms to tensors run into NP-hardness results almost immediately, and thus to solve our problems, we are obliged to develop two new tensor decompositions (with robust analyses) from scratch. Both of these decompositions are polynomial time, and can be viewed as efficient generalisations of PCA extended to tensors.

This thesis presents an investigation of statistical analytical methods applied to the analysis of the shape of the arterial pressure waveform. The arterial pulse is analysed by a selection of both supervised and unsupervised methods of learning. Supervised learning methods are generally better known as regression. Unsupervised learning methods seek patterns in data without the specification of a target variable. The theoretical relationship between arterial pressure and wave shape is first investigated by study of a transmission line model of the arterial tree. A meta-database of pulse waveforms obtained by the SphygmoCor"??" device is then analysed by the unsupervised learning technique of Self Organising Maps (SOM). The map patterns indicate that the observed arterial pressures affect the wave shape in a similar way as predicted by the theoretical model. A database of continuous arterial pressure obtained by catheter line during sleep is used to derive supervised models that enable estimation of arterial pressures, based on the measured wave shapes. Independent component analysis (ICA) is also used in a supervised learning methodology to show the theoretical plausibility of separating the pressure signals from unwanted noise components. The accuracy and repeatability of the SphygmoCor?? device is measured and discussed. Alternative regression models are introduced that improve on the existing models in the estimation of central cardiovascular parameters from peripheral arterial wave shapes. Results of this investigation show that from the information in the wave shape, it is possible, in theory, to estimate the continuous underlying pressures within the artery to a degree of accuracy acceptable to the Association for the Advancement of Medical Instrumentation. This could facilitate a new role for non-invasive sphygmographic devices, to be used not only for feature estimation but as alternatives to invasive arterial pressure sensors in the measurement of continuous blood pressure.

Το ηλεκτροεγκεφαλογράφημα (ΗΕΓ) είναι μια καταγραφή διαφορών δυναμικού στο τριχωτό της κεφαλής που προέρχονται από τη βιοηλεκτρική δραστηριότητα του εγκεφάλου. Με ιστορία άνω των 70 ετών, η αξία του ΗΕΓ σαν κλινική εξέταση είναι δεδομένη, με σημαντικό πλεονέκτημα το γεγονός ότι είναι μια μη επεμβατική μέθοδος. Ωστόσο, το πλήθος των ιστών που παρεμβάλλονται ανάμεσα στον εγκέφαλο και το τριχωτό της κεφαλής, σε συνδυασμό με το μικρό ύψος των εγκεφαλικών ρυθμών (τάξης μV) κάνουν τις ΗΕΓ καταγραφές επιρρεπείς σε πλήθος παρασίτων εξωεγκεφαλικής προέλευσης (artifacts). Όσον αφορά την κλινική εξέταση το πρόβλημα των artifacts είναι αντιμετωπίσιμο σε κάποιο βαθμό. Άλλωστε, για την κλινική εξέταση έχουν λογική μια απαίτηση ακινησίας και ηρεμίας του εξεταζομένου, που δεν είναι όμως πάντα δυνατή, σε ηλεκτρομαγνητικά θωρακισμένο χώρο, το κόστος του οποίου είναι αποσβέσιμο σε μακροπρόθεσμο χρονικό ορίζοντα. Σε τελική ανάλυση, η διάρκεια καταγραφής ενός ΗΕΓ μπορεί να επιμηκυνθεί τόσο όσο χρειάζεται ο κλινικός ιατρός ώστε να εξάγει ασφαλή διάγνωση. Τέτοιου είδους περιορισμοί όμως, μάλλον φαντάζουν εκτός λογικής σε φιλόδοξες εμπορικές εφαρμογές στον τομέα του Brain Computer Interface. Οι λύσεις σε αυτόν τον τομέα πρέπει να είναι φθηνές, να δουλεύουν ικανοποιητικά στο συνηθισμένο οικιακό ή εργασιακό περιβάλλον και να μην περιορίζουν τον χρήστη. Η προσέγγιση λοιπόν δεν πρέπει να είναι τόσο στον περιορισμό των artifacts, όσο στην αναγνώριση και αφαίρεσή τους. Στην παρούσα εργασία η αφαίρεση των artifacts προσεγγίζεται σαν ένα πρόβλημα Τυφλού Διαχωρισμού Σημάτων. Εφαρμόζονται τεχνικές Ανάλυσης Ανεξαρτήτων Συνιστωσών με σκοπό το διαχωρισμό των artifacts σε ξεχωριστές Ανεξάρτητες Συνιστώσες κάνοντας εύκολη στη συνέχεια την αφαίρεση τους Η παραπάνω προσέγγιση εκτός της προαναφερθείσας εφαρμογής στον τομέα του Brain Computer Interface, έχει σαφώς και κλινική αξία. Θα μπορούσε να εφαρμοστεί για παράδειγμα σε μη συνεργάσιμους ασθενείς (π.χ. μικρά παιδιά) ή σε θορυβώδη εξωτερικά περιβάλλοντα αποσυνδέοντας το ηλεκτροεγκεφαλογράφημα από την απαίτηση ενός καλά ελεγχόμενου, ηλεκτρομαγνητικά θωρακισμένου χώρου. / --

Αντικείμενο της παρούσας διπλωματικής εργασίας είναι η υλοποίηση του αλγορίθμου FAST-ICA, ο οποίος εφαρμόζει μια μέθοδο Ανάλυσης Ανεξάρτητων Συνιστωσών (ICA), στον μικροελεγκτή ADuC7020 της Analog Devices. Η εργασία αυτή περιλαμβάνει τέσσερα κεφάλαια. Στο πρώτο κεφάλαιο ορίζεται το θεωρητικό υπόβαθρο πάνω στο οποίο στηρίζονται οι μέθοδοι Ανάλυσης Ανεξάρτητων Συνιστωσών και παρουσιάζονται κάποιες απλές εφαρμογές. Στο δεύτερο κεφάλαιο εξηγείται λεπτομερώς η μέθοδος Ανάλυσης Ανεξάρτητων Συνιστωσών που εφαρμόζει ένας συγκεκριμένος αλγόριθμος, ο FAST-ICA. Στο τρίτο κεφάλαιο γίνεται εισαγωγή σε στοιχειώδεις έννοιες όπως αυτές του μικροελεγκτή και ενσωματωμένου συστήματος, και παρουσιάζεται λεπτομερώς ο μικροελεγκτής ADuC7020 καθώς και η λειτουργία των περιφερειακών του. Τέλος, στο τέταρτο κεφάλαιο περιγράφεται αναλυτικά ο προγραμματισμός του μικροελεγκτή ADuC7020 και γίνεται επεξήγηση του τελικού προγράμματος στο οποίο εφαρμόστηκε ο αλγόριθμός FASTICA. / The aim of this thesis is the implementation of the FAST-ICA algorithm, which performs a method called Independent Component Analysis, in the ADuC7020 microcontroller of Analog Devices. The thesis consists of four chapters. In the first chapter, we define the theoretical background on which, the methods for Independent Component Analysis are based. Some simple applications are also introduced in this chapter. In the second chapter, a detailed report is given on the particular methods that are included in the FAST-ICA algorithm. In the third chapter, basic concepts are presented, such as the concept of the microcontroller. In this chapter, there is also an extensive analysis on the ADuC7020 microcontroller and the functions of its main peripherals. Finally, in chapter four we explain the programming of the microcontroller as well as the main program of the FAST-ICA algorithm.

Recording and monitoring vital signs is an essential aspect of home-based healthcare. Using contact sensors to record physiological signals can cause discomfort to patients, especially after prolonged use. Hence, remote physiological measurement approaches have attracted considerable attention as they do not require physical contact with the patient’s skin. Several studies proposed techniques to measure Heart Rate (HR) and Heart Rate Variability (HRV) by detecting the Blood Volume Pulse (BVP) from human facial video recordings while the subject is in a resting condition. In this thesis, we focus on the measurement of HR. We adopt an algorithm that uses the Independent Component Analysis (ICA) to separate the source (physiological) signal from noise in the RGB channels of a facial video. We generalize existing methods to support subject movement during video recording. When a subject is moving, the face may be turned away from the camera. We utilize multiple cameras to enable the algorithm to monitor the vital sign continuously, even if the subject leaves the frame or turns away from a subset of the system’s cameras. Furthermore, we improve the accuracy of existing methods by implementing a light equalization scheme to reduce the effect of shadows and unequal facial light on the HR estimation, a machine learning method to select the most accurate channel outputted by the ICA module, and a regression technique to adjust the initial HR estimate. We systematically test our method on eleven subjects using four cameras. The proposed method decreases the RMSE by 27% compared to the state of the art in the rest condition. When the subject is in motion, the proposed method achieves a RMSE of 1.12 bpm using a single camera and RMSE of 0.96 bpm using multiple camera.

Abstract Functional magnetic resonance imaging (fMRI) has become a leading tool in the evaluation of the human brain function. In fMRI the activation induced blood oxygenation changes in the brain can be detected with an inherent blood oxygen level dependent (BOLD) contrast. Even small blood oxygen fluctuations in a resting brain can be depicted with the BOLD contrast. This thesis focuses on characterizing spontaneous oxygenation fluctuations of the brain by using BOLD fMRI. The effects of anesthetics on blood oxygen fluctuations were assessed in 38 children and 12 adults. The spatial distribution, frequency, synchrony, and statistical independence of the spontaneous oxygenation changes were analyzed. The role of imaging artifacts in the generation of BOLD signal fluctuations was investigated. The study aimed to develop and compare methods of detecting the nondeterministic oxygenation fluctuations of the brain. VLF BOLD signal fluctuation in the brain cortex is induced by physiological oscillation instead of imaging artifacts. This study shows for the first time how the power and synchrony of very low frequency (VLF <  0.05 Hz) blood oxygen fluctuation significantly increases after sedation. In deeper anesthesia, the VLF fluctuation overpowers other sources of blood oxygen variation as a sign of reduced blood flow and altered hemodynamic control. Regional hemodynamic mechanisms induce non-Gaussian features on the VLF blood oxygen fluctuation that can be depicted effectively with independent component analysis. Combined use of frequency, time, and spatial domain analysis guarantees a more complete picture of brain oxygenation fluctuations. The results of this thesis have a dualistic impact on fMRI research. First of all, VLF fluctuation alters the BOLD activation and connectivity results after sedation. Thus it has to be accounted for in the fMRI of sedated subjects. Secondly, by using the methods developed in this thesis, VLF fluctuation and other physiological BOLD signal sources can now be used in characterizing physiological alterations and pathology of the brain.

This thesis is dedicated to the development of new energy detectors employed in the detection of unknown signals in the presence of non-Gaussian and non-independent noise samples. To this end, an extensive study has been conducted on di erent energy detection structures, and novel techniques have been proposed which are capable of dealing with these problematic situations. The energy detector is proposed as an optimum solution to detect uncorrelated Gaussian signals, or as a generalized likelihood ratio test to detect entirely unknown signals. In both cases, the background noise must be uncorrelated Gaussian. However, energy detectors degrade when the noise does not ful ll these characteristics. Therefore, two extensions are proposed. The rst is the extended energy detector, which deals with the problem of non-Gaussian noise; and the second is the preprocessed extended energy detector, used when the noise also possesses non-independent samples. A generalization of the matched subspace lter is likewise proposed based on a modi cation of the Rao test. In order to evaluate the expected improvement of these extensions with respect to the classical energy detector, a signalto- noise ratio enhancement factor is de ned and employed to illustrate the improvement achieved in detection. Furthermore, we demonstrate how the uncertainty introduced by the unknown signal duration can decrease the performance of the energy detector. In order to improve this behavior, a multiple energy detector, based on successive subdivisions of the original observation interval, is presented. This novel detection technique leads to a layered structure of energy detectors whose observation vectors are matched to di erent intervals of signal duration. The corresponding probabilities of false alarm and detection are derived for a particular subdivision strategy, and the required procedures for their general application to other possible cases are indicated. The experiments reveal the advantages derived from utilizing this novel structure, making it a worthwhile alternative to the single detector when a signi cant mismatch is present between the original observation length and the actual duration of the signal. / Moragues Escrivá, J. (2011). New energy detector extensions with application in sound based surveillance systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/11520 / Palancia

京都大学 / 0048 / 新制・論文博士 / 博士(医学) / 乙第13387号 / 論医博第2219号 / 新制||医||1048(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 溝脇 尚志, 教授 髙橋 良輔, 教授 渡邉 大 / 学位規則第4条第2項該当 / Doctor of Medical Science / Kyoto University / DFAM

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The cognitive processing of a stimulus does not only depend on the physical properties of the stimulus itself but also on the larger context in which it occurs. In this thesis I will present a number of studies that investigate this context-dependency at different levels of cognition. In particular these levels include (1) sensory processing within a modality, (2) sensory integration across modalities and (3) the relation between sensory perception and motor behavior. Accordingly the chapters in this thesis are partitioned into three larger parts, each of which relates to one of these levels. The first study in Part 1 investigates the role of neural oscillations during perceptual grouping. By measuring EEG during contour integration we were not only able to identify the neural sources involved in this process but also to demonstrate local and long-range synchronization of oscillatory activity within frontoparietal networks. This study is then followed by a more general discussion about the properties of oscillatory activity and how they might relate to event-related potentials. The focus of Part 2 will then be on cross-modal interactions and their possible utilization for real-life applications. First we show that simultaneously presented auditory and tactile cues lead to interactions on both a behavioral and neural level. Subsequently we demonstrate how the observed perceptual effects can be used to optimize auditory and tactile localization performance. Finally we propose a setup for utilizing tactile information to enhance the perceptual interpretation of 360° visual scenes. The third and last part of this thesis is dedicated to problems and applications of measuring EEG in the presence of eye movements. Therefore we use eye tracking to investigate and characterize EEG artifacts resulting from ocular activity. Subsequently we develop an algorithm that allows objectively and reliably identifying these artifacts and removing them from the data without affecting the signal from neural sources. Employing this algorithm we then demonstrate that combined EEG and eye tracking can be used for monitoring and shaping both the gaze behavior and the related brain activity in ASD patients. Next to studying cognition with regard to perceptual and behavioral context, this thesis also focuses on the question how the context-relevant signal components can be identified and extracted from the EEG. In the studies presented here we applied a variety of different strategies to approach this problem. These range from resorting to prior knowledge and analyzing only activity from predetermined cortical sources on the one hand, to purely data driven approaches based on logistic regression or eye tracking information on the other hand.