Neuropathic pain (NP) affects approximately seven to ten percent of the general population. Seventeen percent of NP patients scored their life as “worse than death”. A myriad of causes may underlie NP, such as stroke or spinal cord injury. Also, damage or disease of the peripheral nervous system (PNS) may result in NP. One of the main issues of NP caused by a peripheral nerve injury (PNI) is the development of a neuroma, which is a tumor-like mass at the proximal end of a severed nerve that can become very painful. Neuromas show unique neurophysiological characteristics. Cell membrane alternations lead to different ion channel distributions, which in turn result in subthreshold oscillations (SO) and ectopic discharges (ED). It is assumed that this behavior could lead to NP generation. Electrical neurostimulation (ENS) is used to treat patients, thereby applying pre-programmed stimulation patterns to the affected nerves. However, the pain-provoking signals which run through the nerves are not detected and analyzed before ENS is provided. Furthermore, it is questionable whether (the currently applied) pre-programmed ENS defuses these signals anyway. In addition, pre-programmed ENS is not effective at all moments of the pain experience caused by fluctuations in signal intensity. As the clinical results are discouraging, and in view of the high costs, the popularity of this technology is currently waning. Optimization of this potentially powerful technique is needed to improve the outcome and make this technology useful to implement in the treatment strategy of patients with intractable otherwise difficult to treat pain syndromes. Theoretically, optimization of stimulation technology is possible by actually neutralizing SO and ED, which should lead to mitigating the generation of NP. We propose an approach to neutralize SO and ED consisting of several steps. Firstly, the nerve activity is real-time monitored. Secondly, a decision mechanism (called a ‘controller’) is developed that constructs electrical neurostimulation (ENS) patterns to neutralize SO and ED. Finally, these patterns are actually applied to the nerve by an electrical stimulator...

The need for smart traffic control has grown over the last years. Initiated by an increased amount of traffic. Network-wide traffic control is becoming a more interesting field for traffic control. Mainly because computer power has increased and optimisation techniques improved. Network-wide traffic aims to improve the overall traffic state by looking at the entire problem instead of sub-problems. Besides improving traffic conditions, network-wide traffic control could support road operators in simplifying their work by taking over some tasks and keeping track of the situation. For this research, we compare a pragmatic, user-friendly and transparent control method versus a Model Predictive Control (MPC) approach. For the MPC controller, the second-order macroscopic METANET model is chosen. The METANET model describes a network as a directed graph. We test both controllers on two small scale freeway traffic networks. The control measures that are implemented are ramp-metering and rerouting. The simulations are done in a SUMO environment. The key performance index (KPI) used for comparison is Total Time Spend (TTS). The resulting optimisation problem is a Mixed Non-linear Integer Problem (MINLP). This problem is solved with a heuristic method by a Genetic Algorithm (GA). Simulations for the two networks in a demand scenario around critical density are analysed over 20 iterations. The results prove the potential of both algorithms since both improved the TTS significantly. The NM excels in ease of implementation and ease of understanding for non-experts. While the MPC outperforms the NM in TTS reduction, it is harder to configure and understand for non-experts. The MPC is successfully tested on its capability to prevent undesired behaviour from happening by adding penalties to the objective function. For future research, larger networks need to be investigated, with a focus on simplifying the resulting optimisation problem. It is expected that a piecewise affine approximation is a promising method.

A satellite remote sensing technique, Interferometric Synthetic Aperture Radar (InSAR), is able to provide surface displacement information on a millimeter level. In this study, data from the TerraSAR-X satellite collected in the years 2009-2018 over the area of Amsterdam is used. Even though radar data is a subject to multi-step processing, there are still several problems observed that can make the interpretation of the time-series difficult for users who are not experts in the radar remote sensing field. In this study we focus on unwrapping errors, partial decorrelation, and incorrectly fitted models. The unwrapping errors are handled as outlier detection problem and the rest as a time-series segmentation task. In order to address these issues, a data-driven approach is proposed. We show a method to detect unwrapping errors based on spatially neighboring points. A GUI is developed to collect expert knowledge in a form of assessing the time-series correctness, marking unwrapping errors, and dividing time-series into separate segments. This information is later used in the evaluation of several outlier detection and segmentation algorithms. We propose a supervised learning approach based on neural networks in order to use expert knowledge. Due to not enough labelled data available, a simulation is developed and used for training of the networks. We present two different approaches, one based on multi-label classification and one on binary classification. For each of them fully-connected neural networks and convolutional neural networks are compared.

General anesthesia (GA) is an important medical procedure that induces unconsciousness to patients during surgery. Consciousness is a salient feature of the brain, whose neurophysiological features are difficult to be distinguished from unconsciousness. Though it can be defined as an event arising due to interactions in the nervous system, it entirely is not a reliable mechanism. Thus, tracking changes in the brain waves caused by GA is a challenging problem in neuroscience. The exact mechanism to quantify the state of the brain and to distinguish between conscious and unconscious brain is still difficult. Specific features to characterize the state of the brain from the patterns of the brain signal is challenging. Present-day depth of anesthesia monitors index values does not quantify the state of the brain. An alternative approach is to use dynamical systems theory to assess the underlying dynamics of the brain with imaging technology (e.g., electroencephalographic and electrocorticographic data). Previous results from the literature suggest that stability can play a role in the characterization of unconsciousness. This thesis proposes a detailed study that focus on dynamical systems properties that go beyond stability. In particular, the proposed methodology aims to assess which regions of the brain intervene in the process of consciousness and unconsciousness, as well as quantify how often they interact with each other. Specifically, the approach seeks to leverage the eigenstructure of the underlying approximation of the neural activity captured from intracranial electrocorticographic data. Our results show that it is possible to differentiate between anesthetic stages of the brain using eigendecomposition. This was possible through a framework that provides a regularised way to sparsify the state estimates of electrocorticographic (ECoG) signal to get a model for analysis of changes in the brain waves affected by GA. Later to look at the eigenvalues and eigenvectors, which gives the frequency of oscillation and direction between different regions of the brain, respectively. It was also observed that the pattern in the evolution of eigenvalues during different anesthetic stages could be able to interpret if the subject was under anesthesia or not.

Resting-state functional magnetic resonance imaging (rs-fMRI) is one of the promising non-invasive technology that helps in the detection of neurodegenerative and neurological disorders, localisation of the different areas of the brain and understanding the connectivity between them. It involves the acquisition of time series of MR images while the brain is at "resting-state" and serves as a biomarker for various neurological conditions. The acquired resting-state blood-oxygen-level-dependent (BOLD) data, however, can be corrupted with various artefacts (head movements, physiological movements like breathing etc.). Several preprocessing pipelines have been developed to counter the effect of these known artefacts. However, there might be some artefacts that could not have been attenuated from the signal and might lead to incorrect assessment. Therefore, the thesis proposes model-based filtering of the minimally preprocessed (free from known artefacts) resting-state BOLD signal. It was observed that these signals have long memory dependencies. Hence, the usage of autoregressive fractional integrative process filters is proposed for this purpose. Furthermore, the utility of the approach is demonstrated by removing the effect of white noise from a synthetic signal with statistical properties similar to the resting-state BOLD signal. Afterwards, the proposed method is implemented on the minimally preprocessed resting-state BOLD data of 98 subjects from the Human Connectome Project. The results suggest that the proposed filter, in contrast to the low-pass filter, attenuates the higher frequencies but do not eliminate them. Additionally, four different evaluation measures (power spectrum, functional connectivity using Pearson's correlation and coherence, and eigenmode analysis) were considered. The results provide evidence that the proposed method can be used as an additional step in the already existing preprocessing pipeline to mitigate some artefacts that could not have been filtered out. Besides, the results also provide evidence that the proposed scheme is suitable to capture the dynamics of resting-state BOLD signals.

Meditation is a contemplative practice that is believed to entail attentional and emotional regulation. One of the biggest challenges in developing personalized, accessible healthcare options with meditation is finding understandable features that signify whether someone is meditating. Specifically, there is no consensus on a feature resulting from the electroencephalogram (EEG) in the current body of literature on meditation. In this thesis, I propose a dynamic systems analysis on EEG data to obtain a dynamic feature capable of distinguishing meditation from an eyes-closed resting baseline. I gathered the EEG data at TNO (Dutch Organisation for Applied Scientific Research) from twenty-two participants during a sixteen-minute loving-kindness meditation and two two-minute baselines. The proposed methodology characterizes temporal and spatial characteristics of the EEG simultaneously by approximating the EEG dynamics with a linear model on short time windows. I assess changes among three features: the frequency and magnitude of the oscillatory dynamics and the corresponding active electrodes. The analysis can identify changes in EEG dynamics for each individual. Across all participants, regions associated with vision and language processing were active throughout the experiment. Notably, attention-related regions were more involved during meditation than rest. Moreover, the results show a shift in active regions throughout the meditation and the baselines for several participants. Moreover, the thesis investigates the sensitivity of the analytical approach to changes in the electrode subset used for the analysis. For each participant, I constructed a subset of electrodes that were most involved in the changing EEG dynamics. The personalized subset was most sensitive to changes between meditation and rest, compared to other subsets based on commercial wearable EEG headsets. Finally, I compare the findings of the dynamic systems approach to a conventional analytical approach, and the participants’ emotional ratings inquired in subjective questionnaires. Unfortunately, from the current data, there appears to be no relationship between the proposed features and the conventional measures or the subjective questionnaires.

Time-sensitive medical emergencies resulting from the entry of harmful agents into the body require immediate and urgent medical attention to prevent irreversible symptoms or fatalities. However, the current process for urgent care relies on human intervention, which can introduce significant delays and potentially compromise the effectiveness of treatment. Recent advancements in harmful agent detection sensors and ingestible device technology present an opportunity to explore the development of an ingestible device that can autonomously detect and immediately treat medical emergencies caused by harmful agents. The concept of an autonomous device for urgent care therapy necessitates a system that can achieve gastric retention and facilitate on-demand delivery of macromolecule drugs. Despite numerous proposals for gastric residence and macromolecule drug delivery systems, their controllability capabilities fall short. One of the primary challenges is ensuring the correct orientation of the device for effective injection into the stomach wall. Researchers have developed an ingestible device guided by a microactuator, designed for gastric retention and injection. However, the current prototype of the device lacks a control system, essentially being a body without a brain. This thesis delves into the design and implementation of a control system for an urgent care therapy device. The proposed control system is designed by individually examining the sensor system, the actuator system, and the decision making system. The viability of the proposed control system is demonstrated using the example of gamma radiation poisoning. The system integrates three distinct sensors: a harmful agent sensor, a power monitor, and an inertial measurement unit (IMU). The collected data is fed into the decision making system, which is capable of detecting the urgent care condition, battery depletion, and device misorientation. If the urgent care therapy need has been confirmed, the actuator control system will steer the device to correct its orientation and trigger the injection of the macromolecule drug into the stomach wall. The proposed system provides orientation estimation with an accuracy of 2 degrees and features an adaptable sensor and needle system, enhancing its adaptability across various applications. The device's battery life extends up to 19 days, underscoring its potential as a viable autonomous tool for urgent care therapy. This research concludes that the proposed autonomous urgent care therapy device is a feasible solution in the treatment of time-sensitive medical emergencies resulting from the entry of harmful agents into the body, complementing existing healthcare strategies.