Thesis Project Form

Sleep staging in preterm infants provides valuable insights into early brain development and neurological health. However, most available tools are tailored to adult sleep scoring and lack dedicated visualization interfaces for neonatal data. This project aims to create an interactive environment that facilitates exploration and interpretation of neonatal sleep staging results from EEG recordings

The goal of this thesis is to integrate an existing automated sleep staging model for preterm infants into an interactive Python-based visualization framework. The student will design and implement functionalities to display the hypnogram, EEG traces, and stage-specific metrics within a unified interface. The work will involve adapting the current staging model, connecting it with visualization modules, and ensuring full compatibility with standard EEG data formats. Additional activities may include enabling manual inspection or correction of automatically assigned sleep stages and documenting the integrated workflow.

The student will acquire practical experience in Python programming, EEG data analysis, and software integration. They will learn to handle EEG datasets, implement visualization pipelines, and interface machine learning models with interactive graphical tools. The project will strengthen skills in signal processing, data visualization, and reproducible scientific software development.