ActiTect: A Generalizable Machine Learning Pipeline for REM Sleep Behavior Disorder Screening through Standardized Actigraphy
arXiv:2511.05221v3 Announce Type: replace Abstract: Isolated rapid eye movement sleep behavior disorder (iRBD) is a major prodromal marker of $\alpha$-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 identi
The increasing availability of wearable health technology and advancements in machine learning are converging to enable more accessible and automated medical diagnostics.
This development represents a significant step towards scalable, early detection of neurodegenerative diseases, potentially transforming public health screening and intervention strategies.
Early and widespread screening for conditions like Parkinson's disease using readily available devices becomes more feasible, shifting diagnosis from clinical symptoms to pre-symptomatic biomarkers.
- · Wearable tech manufacturers
- · Medical AI developers
- · Patients at risk for neurodegenerative diseases
- · Preventative healthcare providers
- · Traditional diagnostic methods requiring specialized clinical visits
- · Companies slow to integrate AI into medical devices
ActiTect offers an automated machine learning pipeline for screening REM sleep behavior disorder using standard actigraphy.
Widespread adoption could lead to earlier diagnosis of alpha-synucleinopathies like Parkinson's, enabling earlier therapeutic interventions.
This could fundamentally alter the trajectory of these diseases, potentially reducing their societal burden and transforming the global pharmaceuticals market for neurodegenerative conditions.
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Read at arXiv cs.LG