Data to Dreams: Enhancing Sleep Research
Sleep disruptions are closely linked to neurodegenerative diseases, underscoring the need for effective sleep stage monitoring. Automatic Sleep Stage Classification (ASSC) has become increasingly important, especially with advancements in deep learning (DL). However, the opaque nature of DL models can hinder their clinical adoption due to trust concerns among medical practitioners. To address this issue, a team of researchers, including Assistant Professor Iman Dehzangi from the Department of Computer Science at Rutgers University–Camden, introduced “SleepBoost,” a transparent multi-level tree-based ensemble model specifically designed for ASSC.
SleepBoost incorporates a feature engineering block that extracts time and frequency domain features, selecting those with high mutual information scores. By integrating three fundamental linear models into a cohesive multi-level tree structure and employing a novel reward-based adaptive weight allocation mechanism, SleepBoost achieves superior performance metrics, including an accuracy of 86.3%, F1-score of 80.9%, and Cohen kappa score of 0.807 on the Sleep-EDF-20 dataset. These results surpass those of leading deep learning models in ASSC.
The open-source implementation of SleepBoost is available at https://github.com/akibzaman/SleepBoost.
This research, co-authored by Akib Zaman, Shiu Kumar, Swakkhar Shatabda, Iman Dehzangi, and Alok Sharma, was published in the journal Medical & Biological Engineering & Computing.
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