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Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Building a Data Analysis Platform for the EARDREAM Project |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2023 |
| Abstract Text | With an aging population across the world, dementia and neurological diseases, such as Alzheimer's disease (AD), are on the rise. The disproportionate rise in AD cases in developing countries gives rise to a low-cost, robust way to diagnose early-onset AD. The EARDREAM project takes up the fight against AD in these countries using low-density electroencephalography (EEG) device, with the goal of developing a digital biomarker of early-onset AD. To make the collected health data accessible and enable large-scale analysis, there is a need for accessible, scaleable, secure solutions for EEG data analysis. This thesis presents the design and implementation of the Wondernap platform, a novel, cloud-based system dedicated to enhancing the current process of interacting with the data generated using the EARDREAM EEG device. The evolution of the platform through two iterative prototypes is described, highlighting the transition from an initial prototype to a scaleable, secure, cloud-based solution. The initial prototype laid the groundwork for a scaleable, modular, and transferable architecture capable of accounting for the unique requirements of the EARDREAM project, employing state-of-the-art technologies for EEG data analysis architectures. Deploying a Flask backend and Apache HBase for EEG data storage, with MongoDB for patient data, the first iteration validated its usability through a user evaluation, scoring 84/100 on the System Usability Scale. Building upon user feedback and stakeholder input, the second prototype accentuated the applicability of cloud computing to the current architecture, demonstrating its scalability and portability. Using infrastructure-as-code and incorporating Apache Phoenix, this prototype showcased enhancements in fault tolerance, security, and performance. In summary, the developed platform offers a fault-tolerant, scaleable, secure cloud architecture supporting a user-friendly frontend allowing its users to gain insights into the data generated using the EARDREAM portable EEG device. |
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