ActiTect: A Generalizable Machine Learning Pipeline for REM Sleep Behavior Disorder Screening through Standardized Actigraphy

• URL: http://arxiv.org/abs/2511.05221v2
• Date: Thu, 13 Nov 2025 01:33:25 GMT
• Title: ActiTect: A Generalizable Machine Learning Pipeline for REM Sleep Behavior Disorder Screening through Standardized Actigraphy
• Authors: David Bertram, Anja Ophey, Sinah Röttgen, Konstantin Kufer, Gereon R. Fink, Elke Kalbe, Clint Hansen, Walter Maetzler, Maximilian Kapsecker, Lara M. Reimer, Stephan Jonas, Andreas T. Damgaard, Natasha B. Bertelsen, Casper Skjaerbaek, Per Borghammer, Karolien Groenewald, Pietro-Luca Ratti, Michele T. Hu, Noémie Moreau, Michael Sommerauer, Katarzyna Bozek,
• Abstract summary: Isolated rapid eye movement sleep behavior disorder (iRBD) is a major prodromal marker of $$-synucleinopathies.<n> wrist-worn actimeters hold significant potential for detecting RBD in large-scale screening efforts.<n>This study presents ActiTect, a fully automated, open-source machine learning tool to identify RBD from actigraphy recordings.
• Score: 1.2106870940376342
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Isolated rapid eye movement sleep behavior disorder (iRBD) is a major prodromal marker of $α$-synucleinopathies, often preceding the clinical onset of Parkinson's disease, dementia with Lewy bodies, or multiple system atrophy. While wrist-worn actimeters hold significant potential for detecting RBD in large-scale screening efforts by capturing abnormal nocturnal movements, they become inoperable without a reliable and efficient analysis pipeline. This study presents ActiTect, a fully automated, open-source machine learning tool to identify RBD from actigraphy recordings. To ensure generalizability across heterogeneous acquisition settings, our pipeline includes robust preprocessing and automated sleep-wake detection to harmonize multi-device data and extract physiologically interpretable motion features characterizing activity patterns. Model development was conducted on a cohort of 78 individuals, yielding strong discrimination under nested cross-validation (AUROC = 0.95). Generalization was confirmed on a blinded local test set (n = 31, AUROC = 0.86) and on two independent external cohorts (n = 113, AUROC = 0.84; n = 57, AUROC = 0.94). To assess real-world robustness, leave-one-dataset-out cross-validation across the internal and external cohorts demonstrated consistent performance (AUROC range = 0.84-0.89). A complementary stability analysis showed that key predictive features remained reproducible across datasets, supporting the final pooled multi-center model as a robust pre-trained resource for broader deployment. By being open-source and easy to use, our tool promotes widespread adoption and facilitates independent validation and collaborative improvements, thereby advancing the field toward a unified and generalizable RBD detection model using wearable devices.

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