SleepBoost: a multi-level tree-based ensemble model for automatic sleep stage classification
Author(s)
Zaman, Akib
Kumar, Shiu
Shatabda, Swakkhar
Dehzangi, Iman
Sharma, Alok
Date Issued
May 3, 2024
Journal
Medical & Biological Engineering & Computing
Publisher
Springer Berlin Heidelberg
Citation
Zaman, A., Kumar, S., Shatabda, S. et al. SleepBoost: a multi-level tree-based ensemble model for automatic sleep stage classification. Med Biol Eng Comput 62, 2769–2783 (2024).
Version
Author's final manuscript
Abstract
Neurodegenerative diseases often exhibit a strong link with sleep disruption, highlighting the importance of effective sleep stage monitoring. In this light, automatic sleep stage classification (ASSC) plays a pivotal role, now more streamlined than ever due to the advancements in deep learning (DL). However, the opaque nature of DL models can be a barrier in their clinical adoption, due to trust concerns among medical practitioners. To bridge this gap, we introduce SleepBoost, a transparent multi-level tree-based ensemble model specifically designed for ASSC. Our approach includes a crafted feature engineering block (FEB) that extracts 41 time and frequency domain features, out of which 23 are selected based on their high mutual information score (> 0.23). Uniquely, SleepBoost integrates three fundamental linear models into a cohesive multi-level tree structure, further enhanced by a novel reward-based adaptive weight allocation mechanism. Tested on the Sleep-EDF-20 dataset, SleepBoost demonstrates superior performance with an accuracy of 86.3%, F1-score of 80.9%, and Cohen kappa score of 0.807, outperforming leading DL models in ASSC. An ablation study underscores the critical role of our selective feature extraction in enhancing model accuracy and interpretability, crucial for clinical settings. This innovative approach not only offers a more transparent alternative to traditional DL models but also extends potential implications for monitoring and understanding sleep patterns in the context of neurodegenerative disorders. The open-source availability of SleepBoost’s implementation at https://github.com/akibzaman/SleepBoost can further facilitate its accessibility and potential for widespread clinical adoption.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s11517-024-03096-x
