Inspiration

Our journey began with a shared intrigue about the enigmatic nature of sleep. Motivated by the challenges in traditional sleep stage classification, we saw an opportunity to harness technology to unlock deeper insights into sleep patterns. Our collective goal was to blend our fascination with neurotechnology and a commitment to healthcare innovation, aiming to contribute to a field that profoundly affects human health and well-being.

What it does

Our project, dubbed "Nap Navigator," is a sophisticated sleep stage classification system. It leverages multimodal sleep data, including EEG, EOG, and EMG signals, to accurately identify different stages of sleep. The system processes and analyzes sleep data, segmenting it into distinct stages like REM, light sleep, and deep sleep. This provides a detailed map of a person's sleep cycle, offering valuable insights for both healthcare professionals and individuals interested in understanding their sleep patterns.

How we built it

"Nap Navigator" was built on a foundation of meticulous data wrangling and feature extraction. We started by preprocessing the raw sleep data, normalizing it, and segmenting it to ensure consistency and quality. We then extracted key features from the EEG, EOG, and EMG signals, focusing on those most indicative of various sleep stages. The core of our system is a Gradient Boosting Classifier (GBC), chosen for its blend of accuracy and speed..

Challenges we ran into

One of the main challenges we faced was handling the vast volume and variability of the sleep data, which required precise preprocessing to maintain data integrity. Balancing the complexity of our model with computational efficiency was another significant hurdle, necessitating several rounds of optimization and problem-solving. Additionally, interpreting the model's outputs and aligning them with established sleep stage characteristics demanded continuous adjustments and a deep understanding of sleep science.

Accomplishments that we're proud of

We are particularly proud of developing a system that not only meets the technical demands of sleep stage classification but also provides practical, user-centric insights. Our ability to transform complex, multimodal data into a clear, understandable format stands out as a key accomplishment. The collaborative spirit and problem-solving skills we honed during this project are achievements in themselves, reflecting our team's resilience and dedication.

What we learned

Throughout this project, we learned about the intricacies of sleep science and the potential of machine learning in deciphering complex biological data. We gained valuable experience in data preprocessing, feature extraction, and model tuning. The project also enhanced our collaborative skills, teaching us the importance of diverse perspectives and joint problem-solving in tackling challenging tasks.

What's next for Nap Navigator

Looking ahead, we aim to refine "Nap Navigator" further and explore its integration into healthcare settings for diagnosing sleep disorders. We plan to enhance its user interface, making it more accessible for both clinicians and individuals. Additionally, we're interested in expanding its capabilities to include predictive analytics for sleep health and personalized sleep recommendations. Our vision is to see "Nap Navigator" evolve into a tool that not only analyzes sleep but also actively contributes to improving sleep quality and, by extension, overall health.