ArticleMovement disorders : official journal of the Movement Disorder Society2025
Phenotypical Differentiation of Tremor Using Time Series Feature Extraction and Machine Learning.
Article in Movement disorders : official journal of the Movement Disorder Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Intelligent differentiation between Parkinson's disease and essential tremor using wearable sensors and machine learning: a temporal validation study.BMC medical informatics and decision making · 2026Article
- Essential Tremor and Digital Biomarkers: A Scoping Review Using the TRACE Framework to Map Readiness for Clinical Trials and Routine Practice.Tremor and other hyperkinetic movements (New York, N.Y.) · 2026Article
- Signal-based differentiation of essential and dystonic tremor.Frontiers in neurology · 2026Article
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17 authors.
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Abstract
backgroundThe clinical diagnosis of tremor disorders depends on the interpretation of subtle movement characteristics, signs, and symptoms. Given the absence of a universally accepted biomarker, differentiation between essential tremor (ET) and tremor-dominant Parkinson's disease (PD) frequently proves to be non-trivial.
objectiveTo identify generalizable tremor characteristics to differentiate ET and PD using feature extraction and machine learning (ML).
methodsHand accelerometer recordings from 414 patients, clinically diagnosed at six academic centers, formed an exploratory (158 ET, 172 PD) and a validation dataset (30 ET, 54 PD). Established, standardized tremor characteristics were assessed for their cross-center accuracy and validity. Supervised ML was applied to massive higher-order feature extraction of the same recordings to achieve optimal stratification and mechanistic exploration.
resultsWhile classic tremor characteristics did not consistently differentiate between conditions across centers, the feature combination identified via our ML approach was successfully validated. In comparison with the tremor stability index (TSI), feature-based analysis provided better classification accuracy (81.8% vs. 70.4%), sensitivity (86.4% vs. 70.8%), and specificity (76.6% vs. 70.2%), substantially improving disease stratification. The interpretation of identified features indicates fundamentally different dynamics in tremor-generating circuits: while different discrete but stable signal states in PD indicate several central oscillators, signal characteristics in ET point towards a singular pacemaker.
conclusionThis study establishes the use of feature-based ML as a powerful method to explore accelerometry-derived tremor signals. The combination of hypothesis-free, data-driven analyses and a large, multicenter dataset represents a relevant step towards big data analysis in tremor disorders. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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