SleepBoost: a multi-level tree-based ensemble model for automatic sleep stage classification

Thumbnail Image

Zaman10108066.pdf (320.94 KB)

File version

Accepted Manuscript (AM)

Zaman, Akib
Kumar, Shiu
Shatabda, Swakkhar
Dehzangi, Imam
Sharma, Alok
Griffith University Author(s)
Primary Supervisor
Other Supervisors
File type(s)

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 can further facilitate its accessibility and potential for widespread clinical adoption.

Journal Title

Medical & Biological Engineering & Computing

Conference Title
Book Title
Thesis Type
Degree Program
Publisher link
Patent number
Grant identifier(s)
Rights Statement
Rights Statement

This work is covered by copyright. You must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a specified licence, refer to the licence for details of permitted re-use. If you believe that this work infringes copyright please make a copyright takedown request using the form at

Item Access Status

This publication has been entered in Griffith Research Online as an advance online version.

Access the data
Related item(s)
Persistent link to this record

Zaman, A; Kumar, S; Shatabda, S; Dehzangi, I; Sharma, A, SleepBoost: a multi-level tree-based ensemble model for automatic sleep stage classification, Medical & Biological Engineering & Computing, 2024