Phenotypical differentiation of tremor using time series feature extraction and machine learning

The 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 Parkinsons 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 data-driven 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. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement VH and VS received support from the Graduate School of Life Sciences at the University Wuerzburg. JFM and PMR are supported by Grants of the Spanish Ministry of Science and Innovation (RTC2019-007150-1), the Instituto de Salud Carlos III-Fondo Europeo de Desarrollo Regional (ISCIII-FEDER) (PI16/01575, PI18/01898, PI19/01576, PI22/01704), PID2021-127034OA-I00 funded by MCIN/AEI/ 10.13039/501100011033 by ERDF A way of making Europe, the Fundacion Progreso y Salud (PI-0055-2014), the Consejeria de Economia, Innovacion, Ciencia y Empleo de la Junta de Andalucia (CVI-02526, CTS-7685), the Consejeria de Salud y Bienestar Social de la Junta de Andalucia (PI-0471-2013, PE-0210-2018, PI-0459-2018, PE-0186-2019), the Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades de la Junta de Andalucia (PY20-00903), and the Fundacion Alicia Koplowitz. PMR is a member of the COST Action IMMUPARKNET (CA21117). RP and SRS acknowledge the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Project-ID 424778381-TRR 295. SRS is a Fellow of the Thiemann Foundation. Open Access funding enabled and organized by Projekt DEAL. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The overall analysis was approved by the local research ethics committee (IRB University of Wuerzburg, Nr. 20210209 03) in accordance with the Declaration of Helsinki. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Raw data is available from individual contributing centres (according to data sharing arrangements governed by patient consent) by reasonable request to the corresponding author.