Journal Article

DZNE-2026-00930

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png ActiTect: a generalizable machine learning pipeline for REM sleep behavior disorder screening through standardized actigraphy.

Bertram, D. ; Ophey, A. ; Röttgen, S. ; Kufer, K.DZNE* ; Merten, N. ; Fink, G. R. ; Kalbe, E. ; Hansen, C. ; Maetzler, W. ; Kapsecker, M. ; Reimer, L. M.Extern* ; Jonas, S. ; Damgaard, A. T. ; Bertelsen, N. B. ; Skjaerbaek, C. ; Borghammer, P. ; Groenewald, K. ; Ratti, P.-L. ; Hu, M. T. ; Moreau, N. ; Sommerauer, M. (Last author)DZNE* ; Bozek, K.

2026
Macmillan Publishers Limited [Basingstoke]

This record in other databases:    

Please use a persistent id in citations: doi:10.1038/s41746-026-02738-8

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 require 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. 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 two independent external cohorts (n = 113, AUROC = 0.84; n = 57, AUROC = 0.94). To assess robustness, leave-one-dataset-out cross-validation across cohorts demonstrated consistent performance (AUROC range = 0.84-0.89). Complementary stability analysis showed that predictive features remained reproducible across datasets, supporting the pooled multi-center pre-trained model for broader deployment. As an open-source, easy-to-use tool, ActiTect promotes adoption, independent validation, and collaborative improvements, thereby advancing generalizable wearable-based RBD detection.

---

Contributing Institute(s):

1. Digital Biomarkers in Parkinson's disease (AG Sommerauer)
2. Biomarker Parkinson's Disease (AG Wüllner)

Research Program(s):

1. 353 - Clinical and Health Care Research (POF4-353) (POF4-353)

---

Database coverage:
; ; Article Processing Charges ; Clarivate Analytics Master Journal List ; Current Contents - Clinical Medicine ; DOAJ Seal ; Essential Science Indicators ; Fees ; IF >= 15 ; JCR ; PubMed Central ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

---