Recent Trends in EEG-Based Sleep Staging Classification and Analysis: A Comprehensive Review of the Latest Approaches

The report on sleep disorder detection using machine learning (ML) and deep learning (DL) algorithms aims to explore and demonstrate the potential of these advanced technologies in accurately identifying and classifying various sleep disorders. By leveraging ML and DL techniques on sleep data, the report aims to enhance the efficiency and accuracy of diagnosis, enabling timely interventions and personalized treatment strategies. This research offers a comprehensive overview of the current state of sleep disorder detection methodologies, highlighting the limitations of traditional approaches. The report discusses the implementation of ML and DL algorithms, such as convolutional neural networks and recurrent neural networks, to analyze patterns within sleep data, including EEG signals, heart rate, and movement data. It showcases how these algorithms distinguish between standard sleep patterns and aberrations associated with disorders like insomnia, sleep apnea, and narcolepsy. Furthermore, the report emphasizes the potential for continuously monitoring sleep patterns in real-time, facilitating early detection and preventing complications. By providing insights into the challenges and opportunities of using ML and DL in sleep disorder detection, the report aims to contribute to the medical field’s knowledge base. It underscores the importance of interdisciplinary collaboration between medical professionals, data scientists, and technologists to refine these algorithms further. Ultimately, this research seeks to revolutionize sleep medicine by offering more accurate, efficient, and personalized diagnostic tools, thus improving the quality of life for individuals affected by sleep disorders.