Developed a Jupyter notebook for the Fernando-JAL/Neurociencias-2026-1 repository focused on processing and analyzing sleep data from EDF files. The work centered on automating data extraction and filtering steps within a reproducible workflow, enabling streamlined analysis of sleep datasets. Leveraging Python and signal processing techniques, the notebook provides a foundation for consistent and efficient handling of raw sleep data. The initial implementation prioritized clarity and reproducibility, making it easier for users to adapt the workflow for future analyses. This contribution demonstrates practical application of data analysis skills in a scientific context, with an emphasis on workflow automation and transparency.