Phenotypical differentiation of tremor using time series feature extraction and machine learning
Authors: Häring Verena , Selzam Veronika , Martin-Rodriguez Juan Francisco , Schwingenschuh Petra , Tamás Gertrúd , Raethjen Jan , Paschen Steffen , Goltz Franziska , Mulroy Eoin , Latorre Anna , Mir Pablo , Helmich Rick C. , Bhatia Kailash P. , Volkmann Jens , Peach Robert , Schreglmann Sebastian R. 
Published: March 2024

AbstractThe reliable differentiation of tremor disorders poses a significant challenge, largely depending on the subjective interpretation of subtle signs and symptoms. Given the absence of a universally accepted bio-marker, diagnostic differentiation between the most prevalent tremor disorders, Essential Tremor (ET) and tremor-dominant Parkinson’s Disease (PD), frequently proves to be a non-trivial task. To address this, we employed massive time series feature extraction, a powerful tool to examine the entirety of mathematical descriptors of oscillating biological signals without imposing bias, in combination with machine-learning (ML). We applied this approach to accelerometer recordings from tremor patients to identify the optimal recording conditions, processing, and analysis settings, to differentiate ET and PD.We utilized hand accelerometer recordings from 370 patients (167 ET, 203 PD), clinically diagnosed at five academic centres specialising in movement disorders, comprising an exploratory (158 ET, 172 PD from London, Graz, Budapest, Kiel) and a validation dataset (9 ET, 31 PD from Nijmegen). Using 15 second recording segments from the more affected hand, we first extracted established, standardized tremor characteristics and assessed their cross-centre accuracy and validity. Second, we applied supervised ML to massive higher-order feature extraction of the same recordings to achieve optimal stratification and mechanistic exploration.While classic tremor characteristics were unable to consistently differentiate between conditions across centres, the resulting best classifying feature combination validated successfully. In comparison to tremor-stability index (TSI), the best performing classic tremor characteristic, 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 stratification between ET and PD tremor. Similarly, this approach allowed the differentiation of rest from posture recordings independent of tremor diagnosis, again outperforming TSI (classification accuracy 99.6% vs. 49.2%). The interpretation of identified features indicates fundamentally different dynamics in tremor generating circuits: while there is an interaction between several central oscillators in the generation of PD rest tremor, resulting in several discrete but stable signal states, signal characteristics point towards a singular pacemaker in ET.This study highlights the limitations of current, established tremor metrics and establishes the use of feature-based machine learning as a powerful method to explore accelerometry-derived movement characteristics. More importantly, it showcases the strength of the combination of hypothesis-free, data-driven analyses and a large, multi-centre dataset. The results generated are thus resistant to device-, centre- and clinician-dependent bias and establish a generalizable differentiation method, representing a relevant step towards big data analysis in tremor disorders.