Digital Health Bonn

Modern clinical care generates large amounts of heterogeneous data, including structured records, clinical reports, and longitudinal treatment information. However, these data are often difficult to access, explore, and interpret in a systematic way, especially when analyzing patient trajectories and treatment pathways.

In this thesis, the goal is to design and implement an interactive dashboard for the structured analysis and visualization of clinical data in the context of epileptology. The system should support clinicians and researchers in exploring patient cohorts, understanding treatment pathways, and identifying relevant patterns in the data.

The project builds on existing clinical datasets and focuses on transforming raw and partially structured data into meaningful, queryable representations. A particular emphasis can be placed on integrating modern methods such as large language models (LLMs) for structuring clinical text or supporting data exploration.

Your tasks include:

• Analysis and preprocessing of clinical datasets (structured and unstructured)
• Design of data models representing patient trajectories and treatment pathways
• Development of an interactive dashboard for data exploration and visualization
• Integration of methods for structuring clinical text (e.g., LLM-based approaches)
• Close collaboration with clinicians to define relevant use cases and evaluation criteria

The project is embedded in an interdisciplinary environment at the intersection of digital health, clinical neurology, and data science, with direct relevance for real-world clinical workflows and studies.

Skills and interests:

• Experience in Python and data processing
• Interest in data visualization and dashboard development
• Basic understanding of databases and data modeling
• Interest in medical data and clinical applications

Tremor is a common movement disorder that can severely affect patients’ quality of life. Clinical assessment of tremor typically requires in-person evaluation and lacks standardized, continuous monitoring. In this project, we develop an accelerometry-based smartphone solution that enables at-home tremor assessment, providing a more accessible and standardized quantification of tremor severity.

In this thesis, you will work with data collected from an existing smartphone-based tremor assessment application. The focus is not on app development, but on the analysis of recorded sensor data.

The main objective is to investigate how frequency-domain methods, in particular Fourier-based approaches, can be used in a robust way to extract clinically meaningful tremor characteristics such as frequency and amplitude from real-world, potentially noisy data.

Your tasks include:

• Analysis of accelerometer time series recorded via smartphone-based assessments
• Implementation and comparison of spectral methods (e.g., Fourier analysis)
• Development of robust strategies for frequency estimation under real-world conditions
• Evaluation of extracted features with respect to clinical relevance

The project is embedded in an interdisciplinary collaboration between digital health, clinical neurology, and data science, and is jointly supervised with Prof. Holger Fröhlich.

In this thesis, we investigate whether eye movements can be reliably tracked using EEG electrodes placed close to the eyes, i.e. by means of electrooculography (EOG). To obtain a ground truth for eye movements, recordings are performed in parallel with eye-tracking glasses (Pupil Labs Neon).

The experimental setup as well as the Matlab-based study software (Psychtoolbox) are already in place and are supported by members of Prof. Krüger’s group.

In the first phase of the thesis, the doctoral candidate will conduct the study with patients in the Department of Epileptology. Each eligible patient may participate in the study on up to five consecutive days.

In the second phase, the recorded data will be analyzed, including the application of machine learning methods to assess how well EOG-based signals can capture relevant eye movement patterns.

Your tasks include:

• Conducting the study with patients in the Department of Epileptology
• Working with an existing experimental setup and Matlab/Psychtoolbox codebase
• Processing and analyzing multimodal eye movement data
• Applying and evaluating machine learning methods for signal analysis

Some programming experience, for example in Python, is helpful, but can also be acquired during the thesis.

Clinical EEG cables are subject to significant mechanical and chemical stress due to repeated use, handling, and cleaning procedures. Undetected cable faults can lead to degraded signal quality or even complete signal loss, which may compromise clinical assessments.

Current procedures for testing and repairing EEG cables are often time-consuming and prone to human error. This thesis aims to address these limitations by developing a dedicated cable testing device.

The goal is to design and implement a system that goes beyond simple conductance testing and enables more advanced diagnostics, such as frequency sweeps, to better characterize cable integrity and performance.

Your tasks include:

• Design and development of a hardware-based cable testing device
• Implementation of measurement procedures (e.g., conductance tests, frequency sweeps)
• Integration of microcontroller-based control and data acquisition
• Development of software interfaces for data analysis and visualization
• Evaluation of the system in a clinical EEG context

Skills and interests:

• PCB design
• Microcontroller programming (µC)
• Programming experience in Python and/or C

Recent advances in radar-based sensing have enabled the contactless measurement of vital parameters such as respiratory rate and even heart rate. A growing number of commercially available modules and development kits promise reliable acquisition of these signals without requiring any physical contact with the patient.

This technology holds significant potential for applications in hospitals, home care, and unobtrusive long-term monitoring scenarios. However, the actual performance, robustness, and clinical applicability of these systems often vary considerably, and some solutions may not meet their advertised capabilities.

The goal of this thesis is to systematically evaluate a range of radar-based sensor modules and development kits. The aim is to identify which systems provide reliable and meaningful measurements under realistic conditions and to distinguish promising technologies from less suitable solutions.

Depending on the focus, the project may also include the development of experimental setups and signal processing pipelines to assess measurement quality and robustness.

Your tasks include:

• Review and selection of commercially available radar-based sensing modules
• Design and implementation of experimental evaluation setups
• Acquisition and analysis of radar-based vital parameter data
• Comparison of different systems with respect to accuracy, robustness, and usability
• Optional: Development of signal processing or machine learning approaches for improved estimation

The project can be conducted in collaboration with Fraunhofer FHR (Wachtberg), providing access to additional expertise and infrastructure in radar technology.

Skills and interests:

• Interest in sensor systems and signal processing
• Experience in programming (e.g., Python, MATLAB, or C/C++)
• Basic knowledge of physics, electronics, or data analysis