Deep learning for sleep disorder diagnosis: Enhancing EEG Analysis with Machine Learning Techniques

The rising prevalence of sleep disorders highlights their disastrous effects on health, well-being, cognitive performance, memory, and learning outcomes. Sleep disorders such as insomnia and sleep apnea normally require extensive instrumentation and expertise for diagnosis, which is not always possible or even available in most cases. This work, therefore, proposes a state-of-the-art use of Machine Learning (ML) to develop a model for the classification and detection of sleep patterns from electroencephalogram (EEG) signals. The model is trained on a synthetic dataset, developed using mathematical simulations of real-life EEG data. This is a novelty in this area, which allows this model to be precise yet robust without relying too much on real-world datasets.The application bridges advanced AI techniques with real-world applications and smooths out the experience for healthcare professionals and researchers alike. This research highlights how ML might transform sleep medicine through efficient, extended solutions for EEG data analysis and sleep disorder detection. We provide a realistic EEG simulation combined with an intuitive application, a powerful platform that makes advanced sleep diagnostics more accessible and impactful, which supports the development of educational strategies or technologies.