Atrial Fibrillation affects millions of people worldwide. It is associated with an impaired quality of life and an increased risk of stroke, cardiac failure and mortality. Treatments exist, but early detection and treatment is crucial, due to the progressive nature of the disease. Algorithms can help with early detection. Machine learning algorithms are commonly trained to diagnose based on ECG data, but the interpretability is low. A physiological model that simulates the heart gives more insight into the situation of the patient. Current approaches, like the IPFM model, simulate only the SA node and generate RR intervals as output, while completely neglecting the interaction between the AV and SA node. By using an IPFM model and including the AV node as well, an extended and more accurate physiological model was built to more accurately detect Atrial Fibrillation. The AV node model is able to estimate PR intervals when the P waves are annotated. This result shows that the model extension is able to capture information about the signal conduction. When the SA node model and the AV node model are cascaded and only the R peaks are considered, the classification accuracy does not improve compared to the SA node model alone. The R peaks alone do not contain sufficient information for accurate parameter estimation. The parameters governing the behavior of the AV node seem different for NSR compared to AF, but more data is needed to confirm this. The ability of the model to predict PR intervals gives hope that the inclusion of P wave data should improve the performance of the classification with the extended physiological model.

When a person suffers from severe to profound hearing loss, a cochlear implant (CI) can aid in restoring auditory perception and speech comprehension. To obtain good speech comprehension, fitting of a CI to the user’s specific characteristics is crucial. Fitting can be a time-consuming process which requires the cooperation of a CI user and is dependent on the used methods (e.g., T-level measurements). Besides transmitting electrical stimuli, a CI can also record the response of the auditory nerve fibres to a stimulus. This response is called the electrically evoked compound action potential (eCAP). eCAP responses can be measured objectively without a user’s active cooperation and could potentially aid in the fitting of a CI. For this purpose, the eCAP thresholds are of main interest. eCAP thresholds can be determined manually by a clinical specialist, or automatically by an automatic threshold detection method. Automatic eCAP threshold detection can therefore be of aid in a completely objective and uniform CI fitting method. The goal of this study was to compare different automatic eCAP threshold detection methods (in combination with different averaging methods and different artefact reduction methods) based on the precision and accuracy of these methods. Five different automatic eCAP threshold detection methods have been examined in this study: sigmoid amplitude growth function (AGF), linear AGF, signal-to-noise ratio (SNR), cross-covariance between adjacent levels and cross-covariance with maximum level. The two different averaging methods that have been examined are standard averaging (SA) and FineGrain averaging (FG), the two different artefact reduction methods are alternating polarity (AP) and forward masking (FM). In total, 20 different combinations have been examined. The success rates of these 20 combinations have been determined, threshold confidence intervals (TCIs) were calculated as a measure of precision and the correlations between eCAP thresholds and T-levels were determined as a measure of accuracy of the different (combinations of) methods. The combination of FG FM resulted in the highest success rates for different threshold detection methods, and the threshold detection method SNR had the overall highest success rates. A two-way ANOVA revealed that both artefact reduction/averaging method and threshold detection method have a significant effect on the TCIs. The combination of FG FM had the best resultsregarding the TCIs, and the sigmoid AGF threshold detection method was the threshold detection method with the lowest mean TCI. A similar two-way ANOVA was performed for the correlation between eCAP thresholds and T-levels, revealing the same results as for the TCIs that both artefact reduction/averaging method and threshold detection method have a significant effect on the correlation coefficients. FG FM was again the best performing combination, and the sigmoid AGF threshold detection method resulted in the highest correlation coefficients. Based on these results, it can be stated that the FG FM combination for averaging and artefact reduction was the overall best combination. When comparing the different automatic threshold detection methods, the sigmoid AGF method resulted in eCAP thresholds with the highest precision and accuracy. Future research should focus on obtaining more data, further refinements of the different automatic eCAP threshold detection methods and the use of the determined eCAP thresholds in the clinical fitting of a CI.

In recent years, the large increase in connected devices and the data that is collected by these devices has caused a heightened interest in distributed processing. Many practical distributed networks are of heterogeneous nature. Because of this, algorithms operating within these networks need to be simple, robust against network dynamics and resource efficient. Additionally, if privacy preservation methods are properly implemented, they add to the power of distributed processing by making it possible to leverage the data of many different users, without infringing the privacy of the individuals involved. In this study we focus on the primal-dual method of multipliers (PDMM), which is a promising dis- tributed optimisation algorithm that seems to be suitable for distributed optimisation in heterogeneous networks. Most theoretical work that can be found in existing literature focuses on synchronous ver- sions of PDMM. However, in heterogeneous networks, asynchronous algorithms are favourable over synchronous algorithms. So far, simulation results have indicated that asynchronous PDMM converges and can even converge in the presence of transmission losses. In this work we analyse the properties of stochastic PDMM, which is a general framework that can model variations of PDMM such as asynchronous PDMM and PDMM with transmission losses. We build upon previous empirical results of PDMM and formulate theoretical proofs to substantiate these results. After defining stochastic PDMM and proving its convergence, we compare a number of PDMM variations that have been mentioned throughout the literature. Lastly, we derive a lower bound for the variance of the auxiliary variable in the context of stochastic PDMM, assuming uniform updating probabilities. This lower bound indicates that subspace based privacy preservation is applicable to certain instances of stochastic PDMM, like asynchronous PDMM. The main result of this work is a theoretical proof that shows that stochastic PDMM converges almost surely if the updating probabilities of each auxiliary variable are nonzero. Two important conclusions that follow from this proof are the almost sure convergence of asynchronous PDMM and unicast PDMM with transmission losses. Another useful result is the fact that subspace based privacy preservation is effective when using asynchronous PDMM.

Clock synchronization among the nodes of a wireless acoustic sensor network (WASN) is a significant issue that affects the performance of multi-channel noise reduction schemes. Since independent sensors are utilized, each accompanied by its internal clock, clock offsets are inevitable, even if the mismatch in the sampling frequencies is negligible. In this thesis, clock offsets are mathematically modeled and the problem of multi-channel linear filtering for speech enhancement is addressed through signal subspace methods. For this purpose, the generalized eigenvalue decomposition (GEVD) of the cross-power spectral density (CPSD) matrices of the noise and target speech processes is capitalized. Beamformers based on this technique are proved to be invariant to sensor clock offsets when used in a blind manner, exploiting only network measurements. This result is confirmed through experiments in a simulated environment.

Atrial fibrillation (AF) is the most commonly occurring arrhythmia in clinical practice and can have a significant impact on the current and future wellbeing of the patient. By placing an unipolar sensor array directly on the epicardium during an open-heart surgery to measure the electrical activity of the atrium, more insight about this disease can be obtained. One method to obtain these insights is by applying common signal processing models to the measured electrogram (EGM). This thesis further investigates this application and argues why the most common array processing signal models are fundamentally incompatible with this application and proposes two different signal models to rectify the identified discrepancies. The proposed signal models are subsequently analyzed and the conclusion is drawn that both novel signal models better fit the EGM signals, but one signal model in particular shows promising results. This potential is exemplified by using this signal model to formulate a novel LAT estimation technique that can compete with state of the art LAT estimation methods in terms of estimation accuracy and execution time. This result shows the potential of the proposed signal model and opens the door to explore more applications in the future.

This Bachelor graduation project has the goal to create a device which is able of automatic volume control, to be used for enhancing speech intelligibility. To tackle the intelligibility of speech through Public Address Systems (PA Systems), an Intelligibility-Enhancing Automatic Volume Control system was proposed. The total system to be made must be able to alter a clean speech signal according to a noise estimation. Then the altered, enhanced, signal should be amplified before being sent to an existing Public Address System. A subsystem is added in order to dampen the outside noise in a car-like environment. The whole project is divided into three parts: Noise Statistics Estimation, Intelligibility Enhancement and Amplifier and Noise Cancellation. These parts have been performed by three different subgroups. In this report, the Amplifier and Noise Cancellation is discussed. The other parts are explained in the respective reports [1,2]. The Amplifier and Noise Cancellation group will amplify the enhanced audio signal with the use of a pre-amplifier. This group also introduces an additional noise cancellation subsystem for usage in enclosed spaces, like a car. It does so by inverting the recorded environment noise below 500 Hz, and adding this to the to be amplified signal before sending it to the PA System. This thesis is divided in two main design sections: the design of the pre-amplifier and the design of the active noise cancellation circuit. At the heart of both circuits lies a LM386 Audio Operational Amplifier (Op-Amp) but they both have different objectives. The pre-amplifier is designed to have a flat transfer function in audio range, 20 Hz to 20 kHz. The output level of the pre-amplifier is a standard level for consumer electronics, being 447 mVpp. The pre-amplifier inverts the signal from the microphone and the audio signal to achieve noise cancellation. The noise cancellation circuit features a microphone amplifier and a Low Pass Filter (LPF). The microphone amplifier amplifies the signal so that the microphone circuit’s output level is at the same level of the audio input of the pre-amplifier (200 mVpp). The filter makes sure only sounds below 500 Hz are passed to the pre-amplifier. With the inverting capabilities of the pre-amplifier and both signals being completely out of phase, a theoretical cancellation of sound signals is possible. Because of the LPF used in the microphone amplifier this cancellation is done for signals below 500 Hz. At the end of the project, a system was built which met most of the requirements. Some of the requirements can not be satisfied due to incapability of the test equipment available. The system does amplify the signal to the desired amplitude and is capable of slightly cancelling noise in a car. However, improvements of the product are needed to function more optimally.

Several algorithms to enhance the intelligibility of speech in near-end noise were analyzed and implemented. The algorithms considered were assessed based on the intrusive instrumental intelligibility metric SIIB_Gauss. An implementation based on the direct optimization for this metric is assessed, as well as an implementation based on human induced speech modifications, including increased sound intensity, flattening of the spectral tilt, increased vowel duration and increased consonant-vowel ratio. Another implemented algorithm is the amplification of the transient component of speech. Results show that for increased vowel duration a decrease in intelligibility was found in SIIB_Gauss value as well as in informal listening tests. The other implementations did show an increase in intelligibility according to SIIB_Gauss at SNRs between -4 dB and 6 dB in both stationary and fluctuating noise, under a power constraint. Finally, the implementations were combined into a system that automatically selects the optimal algorithm to use under the given noise conditions. It is shown that this combined system is able to increase intelligibility of speech in the presence of non-fluctuating noise, fluctuating noise, speech shaped noise, and competing speaker noise.

Malignant Melanoma (MM) is the most aggressive type of skin-cancer. Current diagnostic tools for the detection of malignancies of the skin (MM cancer) include histological, optical, ultrasound, and impedance-based techniques. The inadequacies of the first three practices are overwhelmed by the Electrical Impedance Spectroscopy (EIS) method. EIS overcomes reported spatiotemporal tradeoffs as a label-free and optics-free analytical method. Yet, MM’s enhanced heterogeneity and metastatic potential still results in misdiagnosis, or late diagnosis leading to stages characterized by high mortality rates. Important biological information and processing ability on single-cell level is missing. Single-cell dynamics recorded with a high-throughput system, contain important biological information on the heterogeneous subpopulations which are responsible for the MM aggressiveness.This project aims to investigate experimentally the possibility and capabilities of such a bioassay development, create working protocols and generate a fundamental basis for analysis and interpretation of the big-data-sets which derive from Impedance monitoring from a high-throughput transducer.Experiments were performed, employing two diverse, human-derived, MM cancer cell-lines, and using a high-throughput HD-MEA system with a 1024-channel impedance readout unit developed at IMEC, in Belgium. The measurements were realized at 1kHz aiming to extract Rseal information. The main proposal presents an experimental protocol of mid-term and long-term experiments Temporal and spatial resolutions were enhanced (Control System Automation), allowing for implementation of an experimental set to test the assay’s capabilities and determine any necessary additions to make the assay more robust for research (i.e. Z-Map, templates and scripts for OriginLab and Matlab, statistical methods for validation of findings on the big-data sets, optimizations in the experimental process, etc).Experimental results proved the optics-free specification of the assay with the utilization of an Impedance colormap, which was validated by comparing it to confocal images after measurements. Electrode-size and normalization techniques were deemed crucial factors that affected the variance of the results for the largest and smallest electrode sizes. Confluence level (70% or 10%) of the cell on the chip was an added biasing factor. IM measurements at 1kHz for two MM cell-lines (MM087 & MM029) showed a good possibility for classification (long-term experiments), while the biological findings on the heterogeneity information (mid-term and long-term experiments) cannot be considered conclusive until a full-scale analysis is conducted on the data. A proposal for a new type of analysis on the Impedance data is presented; it is an example-based approach to extract the classification and heterogeneity information of normalized IM measurements, by obtaining and analyzing the trends of impedance variance over time, per electrode.The bioassay proposal that was developed and tested in this project shows potential for both classification and heterogeneity studies for MM cancer. Further experimentation and development of validation techniques is required.

The most common serious heart rhythm disease is atrial fibrillation. It is not fatal on its own but does increase the risk of heart failures and strokes. There is little understanding about the mechanisms behind the disease, so more insight is desired. Using an array of electrodes, measurements are being performed of the electrical atrial activity directly on the heart tissue. These signals are, however, not clean and suffer from far-field interference coming from the ventricles. During normal sinus rhythm these atrial and ventricular activities are separated in time and easy to distinguish. In case of atrial fibrillation this is not always the case. Luckily, there is a major difference between both signals: the ventricular signal comes from far away and arrives therefore approximately simultaneously at all electrodes. A simple, but effective way to remove this ventricular activity is to use a bipolar electrode. It produces the difference between two normal unipolar electrodes, thus removing the common ventricular signal component. The bipolar electrode, however, distorts the atrial signal component, which in some orientations can even lead to removing it altogether. This bipolar electrode is known as a differential beamformer from the field of array signal processing. There are more complex beamformers that can keep the atrial component undistorted and therefore produce better results than the bipolar electrode. This thesis proposes a Fourier-domain signal model for all available electrodes relying on an atrial and ventricular transfer function. It is possible to estimate these transfer functions from the data blindly. Three beamformers are derived utilizing the signal model and the transfer functions. The bipolar electrode is extended to multiple electrodes like the other beamformers as well. Experiments with simulated data show that the complex beamformers indeed keep the atrial activity undistorted and are still able to remove the ventricular activity effectively when using multiple electrodes, except for very complex data where the signal model is not valid. For low numbers of electrodes the beamformers are not useful, they hardly remove the ventricular activity while keeping the atrial component undistorted, where the bipolar electrode does the opposite. The electrograms are also used to estimate local activation times of the cells underneath the electrodes which says something about the health of the cardiac tissue. Besides the mentioned filtering, this thesis proposes a method to estimate those moments in time by looking at the time-domain version of the atrial transfer function, called the atrial impulse response. For simple data, it performs well compared to state-of-the-art methods, but for more complicated data, it does not.

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia occurring in around 0.5% of the world population. AF is characterized by the rapid and irregular beating of the atrial chambers of the heart, which can cause lead to strokes and other heart-failures. To prevent these consequences the early detection of AF is paramount. Using photoplethysmography (PPG) heart activity can be measured from which the inter-beat-interval (IBI), the time between heart beats, can be estimated. Using data collected by a PPG sensor the aim is to classify the heart activity as either AF or Normal Sinus Rhythm in real time using machine learning and collect the outcomes for further analysis by medical professionals. For this a classification method is suggested which is able to be implemented on an ARM based processor. Using a Support Vector Machine and 10 features derived from the IBI's and the PPG signal this algorithm achieves the following accuracy metrics: balanced accuracy = 0.853, sensitivity = 0.850, specificity = 0.856 and Matthews Correlation Coefficient (MCC) = 0.643. Compared to similar studies these results are substandard and should be improved.

When symptoms of atrial fibrillation (AF), a common cardiac arrhythmia, are experienced, a Holter monitor or event recorder is used for official diagnosis. Apart from the fact that these devices are experienced as inconvenient, AF can already manifest damage in a pre-symptomatic phase. This thesis is aimed at developing a method for recording heart activity using a wearable device to permit convenient early detection of AF. For this, heart activity is measured continuously by means of photoplethysmography (PPG). A classification algorithm is used to detect AF episodes in the PPG recording. If the algorithm suspects AF, a limb lead I ECG recording is requested from the user. The ECG recording can be analyzed by a clinician for official diagnosis. The Maxim Integrated Max86150 chip is used for the implementation of PPG and ECG. Acceleration data is gathered by means of the Adafruit MMA8451 accelerometer to allow for detection of motion artefacts. These sensors and the data they retrieve are controlled and processed by the ARM Cortex-M7 microcontroller. From the results, PPG recordings have a higher quality when infrared light is used as compared to when red light is used. However, both types of recordings are of sufficient quality for monitoring the heart rate accurately when in stasis. Although complete functionality of the system could not be verified, the results are promising for future work.

Introduction: Potentials measured at the epicardial surface contain information regarding the conductive properties of the atrial tissue. The current lack of morphological categorization during atrial fibrillation (AF) provokes the usage of unsupervised learning methods to evaluate time series electrograms across the atrial surface. Analysis of regional potential morphology increases insight in individual tissue characteristics, possibly aiding in future selection of patients with AF benefitting from additional treatment with. Methods: single potentials (SP) are extracted from four defined regions: right atrium (RA), left atrium (LA), pulmonary veins (PV) and Bachmann’s bundle (BB). The methods are divided in two parts. In part one, the clusters are determined. Choice of algorithm, evaluation of distance metrics and evaluation in reduced dimensionality are performed sequentially to determine the eventual clustering setup. The clustering setup is repeated for different types of input data, originating from different regional structures (all four regions, solely LA and solely the RA) to avoid deterioration of cluster formation from single region(s). In the second part, the clusters are adapted to evaluate morphological characteristics in the included regions. Results: Data from a total of 23 patients was used, containing 128 files and 468588 SPs. K-means using the first 8 principal components was deemed the most suitable clustering method with the current data, identifying a total of five clusters. The clusters showed evident different morphology, mostly distinguishable by RS-ratio and potential duration. RA showed a predominant S to an RS-wave pattern, comparable to PV. Both showed similar contribution of fast and slow S-wave morphology. LA showed a RS to R-wave morphology. The cluster showing a slow S-wave morphology was substantially increased across BB compared to other regions but also when compared to other clusters in BB. Conclusion: Looking at spatial cluster occurrence through an RS-ratio perspective, the beforehand expected deviation from AF compared to sinus rhythm (SR) was not observed. The observed pattern during AF suggests more elaborate contribution of atrial tissue than often targeted in current daily practice. The methodology and defined groups of potentials facilitate evaluation of potential morphology at an individual level. Translation to an endocardial perspective could assist in selecting patients eligible for additional treatment when technologically feasible. Future research should be focused on including potentials containing multiple deflections and estimation of progression state of the atrial tissue.

This thesis is focused on Wireless Acoustic Sensor Networks (WASNs) used for beamforming in a speech enhancement task. Since each node in a WASN has its own clock, clock offsets and clock skews between the nodes are inevitable. Clock offsets and clock skew can be detrimental to the beamformer performance. In this thesis we focus on the effect of clock skew on the beamformer performance. Existing methods for clock skew compensation for the speech enhancement application do this explicitly. In this thesis we investigate the possibility to formulate the beamformer such that explicit clock skew compensation is not necessary. Instead, we propose an algorithm for implicit clock skew compensation, which takes advantage of the Generalized Eigenvalue Decomposition (GEVD) to construct beamformers (e.g. Minimum Variance Distortionless Response (MVDR)), recently proposed in the literature. Using the GEVD, no explicit compensation has to be applied to the received data. Compared to the state-of-the-art, where clock skew estimation/compensation algorithms are used, this reduces the computational complexity for beamformer processing. The algorithm depends on exact knowledge of the noisy correlation matrix across the microphones. In practice, this matrix is unknown and estimation will reduce the performance of the proposed algorithm. We therefore quantify the error made in the estimation of the correlation matrix using the standard Welch method and also look at a recursive smoothing based method for correlation matrix estimation. Compared to a selected state-of-the-art algorithm, the proposed algorithm shows similar or better performance using this recursive smoothing method. For future work on this subject, more study can be done on correlation matrix estimation methods, as these play a key role in clock skew invariant beamforming.

In this era of data deluge, we are overwhelmed with massive volumes of extremely complex datasets. Data generated today is complex because it lacks a clear geometric structure, comes in great volumes, and it often contains information from multiple domains. In this thesis, we address these issues and propose two theoretical frameworks to handle such multidomain dataset. To begin with, we extend the recently developed geometric deep learning framework to multidomain graph signals, e.g., time-varying signals, defining a new type of convolutional layer that will allow us to deal with graph signals defined on top of several domains, e.g., electroencephalograms or traffic networks. After discussing its properties and motivating its use, we show how this operation can be efficiently implemented to run on a GPU and demonstrate its generalization abilities on a synthetic dataset. Next, we consider the problem of designing sparse sampling strategies for multidomain signals, which can be represented using tensors. To keep the framework general, we do not restrict ourselves to multidomain signals defined on irregular domains. Nonetheless, this particularization to multidomain graph signals is also presented. To do so, we leverage the multidomain structure of tensor signals and propose to acquire samples using a Kronecker-structured sensing function, thereby circumventing the curse of dimensionality. For designing such sensing functions, we develop several low-complexity greedy algorithms based on submodular optimization methods that compute near-optimal sampling sets. To validate the developed theory, we present several numerical examples, ranging from multi-antenna communications to graph signal processing.

A common cardiac arrhythmia is atrial fibrillation, which is becoming more widespread worldwide. Currently there is some understanding about the mechanisms behind atrial fibrillation, however more insight into the conduction of the atrial tissue is desired. Therefore, invasive mapping studies have been performed where an array of electrodes is used to record the electrical activity on the heart’s surface during open-chest surgery. The moment in time when the tissue under an electrode depolarizes, called the local activation time can be used to reconstruct the propagation pattern of the signal that triggers the tissue to contract. In this thesis, the application of the cross-correlation for estimation of the local activation time of the atria is investigated. Specifically, the benefits of not only cross-correlating electrode pairs that are close, but also pairs that are far away are evaluated. A framework is constructed, based on a graph, that defines these higher order neighboring pairs of electrodes. This is compared to the golden standard of using the steepest deflection of an electrogram, as well as to other methods using the cross-correlation. Experiments are done on simulated electrograms where the true activation times are available, as well as on natural data recorded from patients. Finally some future research is proposed to investigate for which morphologies the proposed cross-correlation based methods may be most effective.

Compared to metals, composite materials offer higher stiffness, more resilience to corrosion, have lighter weights, and their mechanical properties can be tailored by their layup configuration. Despite these features, composite materials are susceptible to a diversity of damages, including matrix cracks, delamination, and fibre breakage. If these damages are not detected and mended, they can spread and result in the failure of the whole structure. In particular, when the structure is under fatigue and vibrations during flight, this process can expedite. Moreover, if such damages occur in the internal layers of the composite material, they will be difficult to detect and to characterise. There is thus a huge demand for reliable and accurate structural health monitoring methods to identify these defects. Such methods either try to monitor the structural integrity of the composite during service, or they are used for studying a desired configuration of a composite material during fatigue and tensile tests. This thesis provides structural health monitoring solutions that can potentially be used for both these categories. The structural health monitoring applications developed in this thesis range from accurate strain and displacement measurement, to detection of cracks and the identification of damages in composites. In this thesis, fibre Bragg grating (FBG) sensors were chosen for this purpose. The miniature size and small diameter of these sensors makes them an ideal candidate for embedding them between composite layers, without severely altering the mechanical properties of the host composite material. They can thus provide us with direct information about the current state of the laminated composite, potentially at any depth. This is especially useful for acquiring information about the internal layers of the composite material, as barely visible impact damages and micro-cracks often form beneath the surface of the material without being visible on its exterior. In spite of their interesting physical characteristics, applications of FBG sensors are typically limited to point strain or temperature sensors. Further, it is often assumed that the strain field along the sensor length is uniform. For this reason, there is currently a gap in the field of structural health monitoring in retrieving meaningful information about the non-uniform strain field to which the FBG sensor is subjected in damaged structures. The focus of this thesis is on analysing the response of FBG sensors to highly non-uniform strain fields, which are a characteristic of the existence of damage in composites. To tackle this problem, first a new model for the analysis of FBG responses to nonuniform strain fields will be presented. Using this model, two algorithms are presented to accurately estimate the average of such non-uniform axial strain fields, which conventional strain estimation algorithms fail to deliver. In fact, it is shown that the state-of-the-art strain estimation methods using FBG sensors can lead to errors of up to a few thousand microstrains, and the presented algorithms in this thesis can compensate for such errors. It was also shown that these methods are robust against spectral noise from the interrogation system, which can pave the way for more affordable FBG based strain estimation solutions. Another contribution of this thesis is the demonstration of two new algorithms for thedetection of matrix cracks, and for accurate monitoring of the delamination growth in composites, using conventional FBG sensors. These algorithms are in particular useful for studying the mechanical behaviour of laminated composites in laboratory setups. For instance, the matrix crack detection algorithm is capable of characterising internal transverse cracks along the FBG length during tensile tests. Along the same lines, the delamination growth monitoring algorithm can accurately localise the delamination crack tip along the FBG length in mode-I tensile and fatigue tests. These algorithms can perform in real-time, which makes them ideal for dynamic measurement of crack propagation under fatigue, and their spatial resolution and accuracy is superior to the other state-of-the-art damage detection techniques. Finally, to enhance the precision of the damage detection schemes presented in this thesis, two different methods are proposed to accurately determine the active gauge length of the FBG sensor, and its position along the optical fibre. This information is generally not provided for commercial FBG sensors with such accuracy, which can adversely affect the precision of crack tip localisation algorithms. Following the algorithms provided in this thesis, the sensor position can be marked on the optical fibre with micrometer accuracy. @en

Atrial fibrillation (AF) is a frequently encountered cardiac arrhythmia characterized by rapid and irregular atrial activity, which increases the risk of strokes, heart failure and other heart-related complications. The mechanisms of AF are complicated. Although various mechanisms were proposed in previous research, the precise mechanisms of AF are not clear yet and the optimal therapy for AF patients are still under debated. A higher success rate of AF treatments requires a deeper understanding of the problem of AF and potentially a better screening of the patients. In order to study AF, instead of using human body surface ECGs, we use the epicardial electrograms (EGMs) obtained directly from the epicardial sites of the human atria during open heart surgery. This data is measured using a high-resolution mapping array and exhibits irregular properties during AF. Although different studies have analyzed electrograms in time and frequency domain, there remain many open questions that require alternative and novel tools to investigate AF. Experience in signal processing suggests that incorporating the spatial dimension into the time-frequency analysis on the multi-electrode electrograms may provide improved insights on the atrial activity. However, the electrophysiologcial models for describing spatial propagation are relatively complex and non-linear such that conventional signal processing methods are less suitable for a joint space, time, and frequency domain analysis. It is also difficult to use very detailed electrophysiologcial models to extract tissue parameters related to AF from the high-dimensional data. In this dissertation, we wish to propose a radically different approach to study and analyze the EGMs from a higher abstraction level and from different perspectives to get more understanding of the characteristics of AF. We also aim to develop a simplified electrophysiological model that can capture the spatial structure of the data and propose an efficient method to estimate the tissue parameters, which are helpful to analyze the electropathology of the tissue, e.g., cell activation time or conductivity. In the first part of this study, we put forward a graph-time spectral analysis framework to analyze EGMs during normal heart rhythm and AF with a higher-level model. To capture the frequency content along both time domain and graph domain, we propose the joint graph and short-time Fourier transform, which allows us to evaluate the temporal and spatial variation of EGMs and capture the interaction between space and time. The spectral analysis of the EGMs helps us to recognize atrial fibrillation impact on the atrial activity and identify the differences between the atrial activity and the ventricular activity. We find that the difference in graph smoothness between the atrial and ventricular activities enables us to better extract the atrial activity from the noisy measurements. The second part of this study is to find a simplified but accurate enough electrophysiological model for the high dimensional EGMs and to make more efficient use of the data to detect the arrythmogenic substrate that causes abnormalities in atrial tissue. In this dissertation, we develop the cross power spectral density matrix (CPSDM) model of the multi-electrode EGMs and make use of an effective method called confirmatory factor analysis (CFA) to jointly estimate the model parameters. The conductivity, the activation time, and the anisotropy ratio are useful parameters to determine abnormalities in cardiac tissue and are therefore the target parameters to be estimated. With the reasonable assumptions that the conductivity parameters and the anisotropy parameters are constant across different frequencies and heart beats, and the activation time of cells are constant across different frequencies, we propose simultaneous CFA (SCFA) to jointly estimate these parameters using multiple frequencies and multiple heart beats. The identifiability conditions which need to be satisfied in the CFA problem are used to find the relationship between the desired resolution and the required amount of data. Evaluations on the simulated data and the clinical data demonstrate that the proposed method can localize the conduction blocks in the tissue and reconstruct the clinical EGMs well using the estimated parameters. @en