The dorsal root ganglion (DRG) is an interesting target for neurosurgical pain treatments since it contains the somata of sensory neurons and is relatively accessible, just outside of the central nervous system. Current interventions, such as radiofrequency ablation or neurostimulation, target the DRG with rigid instruments, only tangenting the structure. The goal of this project was to increase the area of influence for neurosurgical instruments by expanding the contact area between the instrument and the DRG. A mechanical solution was designed, consisting of a pre-bend nitinol leaf spring and a rigid injection needle as delivery device. Multiple prototypes were made with various pre-bend diameters and tip shapes. The prototypes with a backbend at the tip and a pre-bend diameter of 5-6 mm were the most successful in curving around a phantom of the L5 DRG in an experimental setup. Future research is needed to incorporate treatment modalities such as neurostimulation and radiofrequency ablation into the mechanical design.

Atrial Fibrillation (AF) is a cardiac arrhythmia that occurs due to the initiation of electrical impulses in the heart in an uncoordinated manner, giving rise to a disorganized and rapid heartbeat. The progression of AF over time can lead to complications such as stroke and heart failure. Catheter ablation is an established treatment protocol adopted to eliminate AF triggers and restore normal sinus rhythm in patients. To improve the success rates of ablation procedures, mapping and localizing the regions in the heart that give rise to uncoordinated impulses is crucial. This work focuses on obtaining a visual estimate of the location of irregular electrical activity within a volume domain of cardiac tissue using epicardial electrogram measurements.
A straightforward, effective, and computationally fast backprojection imaging method is proposed that provides an indication of the regions in a given volume of cardiac tissue that exhibit prominent irregular atrial activity. The chosen volume is subdivided into non-overlapping voxels, and the standard electrogram signal model is discretized to a matrix multiplication form. The inverse problem of reconstructing the transmembrane current distribution in the volume is solved using the epicardial electrogram readings obtained from the surface and the inverse distances matrix containing the inverse of the distances from each electrode to each voxel within the volume domain. The generated backprojection images are visualized to estimate the location (x and y coordinate information) and depth of irregular atrial activity in the volume. To improve the accuracy of localization while maintaining the computationally fast nature of the proposed solution, Singular Value decomposition (SVD) of the inverse distances matrix is proposed for the transmembrane current reconstruction. The obtained visual estimates can guide ablation procedures, making them more targeted, and reducing the need for repeat ablation procedures due to AF recurrence.

Atrial fibrillation (AF) is one of the most common heart diseases. Billions of people have suffered from it in the world. Although it can lead to terrible complications such as stroke and heart failure, the underlying mechanisms of it are still under-explored. Besides, there is no so-called optimal therapy for the patients. As the disease is progressive, it is important to detect it in an early stage. To develop methods for understanding and detecting AF, the interpretable parametric model can be an option. This model can provide physiological information at the signal level. In this case, the electrocardiogram, as the most commonly used invasive measurement of cardiac conditions, can be the data to model the heart structure and cardiac activities.
This thesis proposes an interpretable parametric model based on P-waves extracted from the ECG signals. Specifically, the autoregressive (AR) model is implemented, which is also known as linear predicting coding (LPC). The goal is to model the atrium and understand the function of the atrium, which can reflect on the varying parameters in the SR and AF cases. In this context, The formant of P-waves is modeled and estimated, which is a representation of the atrium activities. In addition, the parameters of the model are mapped into 2-dimension by the zero-pole plots in order to interpret the differences between SR and AF situations. Based on the differences between parameters and formants, a parametric classifier of high interpretability is developed to detect AF. An alternating searching algorithm is proposed to determine the parameters of the classifier.

Atrial fibrillation is a common cardiovascular disease, affecting the regular beating of the heart through chaotic contraction of the heart's upper chambers. On its own, the condition—increasingly prevalent among the elderly—is not life threatening, but it leads to an increased risk of stroke and heart failure. As of yet, there is no consensus on the physiological mechanisms responsible for initiating and sustaining atrial fibrillation. A more detailed view of cardiac activity would improve understanding of the disease, making earlier diagnosis possible and improving options for treatment.

The contraction of the cardiac muscles is governed by electrical signals propagating through the tissue. Cardiac activity can be monitored with a high spatial resolution by measuring the electrical potential directly on the epicardium of the heart during open-heart surgery, using an array of closely spaced electrodes. From these electrograms, estimating the time of local activation of the cardiac tissue underneath each electrode provides a quantitative way of mapping the mechanisms of atrial fibrillation. Various methods exist to estimate the activation times, but the complex signals that are typical of atrial fibrillation make it difficult to obtain accurate results. This thesis proposes combining two existing methods for estimating the local activation times. Based on a model of the electrogram as a spatial convolution of local transmembrane currents, an inverse problem is formulated and solved, resulting in a less opaque view of the cardiac activity at the electrode locations by attenuating distant disturbances and emphasizing local activity. The deconvolution output is fed to the second step, where cross-correlating certain pairs of signals gives an estimate for the mutual time delay in local activation. A graph representation of the electrode array is used to define neighbor order and decide which signal pairs are correlated. The set of pairwise time delays this produces is then converted to an estimate for the local activation times, using a least-squares estimator.

The proposed method is evaluated using different simulated cardiac settings. In a setting with one stimulation source, earlier results of the deconvolution and cross-correlation methods are confirmed, and the proposed method is seen to produce a slightly lower mean error than reference methods. In the higher-complexity triple-source setting, the latter effect is again visible. Reinforced by the performance of the different methods in increasingly noisy settings, the main merits of the proposed method for the estimation of local activation times can be said to be found in the form of increased consistency, not significantly improving on the accuracy of existing methods.

An exponential growth in the mobile communications industry over the past decade has raised concerns over it’s energy consumption, especially in light of the current climate crisis. 5G technologies in particular exhibit features that imply high power consumption, such as the densification of 5G base stations. Implementing mitigation measures requires accurate a priori knowledge of the power consumption of 5G array architectures in various scenarios; however, there are large gaps in the literature on this topic, and existing power consumption models for 5G array architectures only address a limited scope of use cases and array topologies. Recent works have focused on the energy efficiency of array architectures, but not on the actual amount of power being consumed.

In this thesis, an integrated system-level power consumption model is devised for 5G base station multi-beam transmitter topologies and beamforming schemes, which accounts for the use cases and architectures missing from the literature. The model is then applied to novel use cases in the enhanced urban Mobile Broadband (eMBB) scenario to obtain the estimated power consumption per component, per array and per user for five different beamforming schemes. A thorough parametric analysis is conducted for the optimality and trade-offs of each use case. Recommendations are made on the optimal topology, beamforming scheme and front-end technology from a power consumption perspective. Initial results show that the choice of technology, architecture and topology can lead to an improvement in the per-user power consumption of 41-80%.