Head tremor in cervical dystonia: Quantifying severity with computer vision

• Type of Material: Article
• Authors: Hyder Jinnah, Emory University; Jeanne P Vu, University of California San Diego; Elizabeth Cisneros, University of California San Diego; Ha Yeon Lee, University of California San Diego; Lingh Le, University of California San Diego; Qiyu Chen, University of California San Diego; Xiaoyen A Guo, University of California San Diego; Ryin Rouzbehani, University of California San Diego; Joseph Jankovic, Baylor College of Medicine; Stewart Factor, Emory University; Christopher G Goetz, Rush University; Richard L Barbano, University of Rochester; Joel S Perlmutter, Washington University; Sarah Pirio Richardson, University of New Mexico; Glenn T Stebbins, Rush University; Rodger Elble, Southern Illinois University; Cynthia L Comella, Rush University Medical Center; David A Peterson, University of California San Diego
• Language: English
• Date: 2022-01-29
• Publisher: ELSEVIER
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2022 Elsevier B.V. All rights reserved.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.jns.2022.120154
• Title of Journal or Parent Work: JOURNAL OF THE NEUROLOGICAL SCIENCES
• Volume: 434
• Start Page: 120154
• End Page: 120154
• Grant/Funding Information: This research was conducted under the auspices of the Dystonia Coalition, which is part of the Rare Diseases Clinical Research Network, an initiative funded by the Office of Rare Diseases Research at the National Center for Advancing Translational Sciences (U54 TR001456) in collaboration with the National Institute of Neurological Disorders and Stroke (U54 NS065701 and U54 NS116025) at the National Institute of Health (NIH). This work was also supported by the Office of the Assistant Secretary of Defense for Health Affairs, through the Peer-Reviewed Medical Research Program under Awards W81XWH-17-1-0393 and W81XWH-19-1-0146. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the Department of Defense.
• Keywords: Life Sciences & Biomedicine; Computer vision; Neurosciences; Video; TWSTRS; Head tremor; MOTION; ONSET; Clinical Neurology; Severity rating; Science & Technology; Neurosciences & Neurology

Abstract

Background: Head tremor (HT) is a common feature of cervical dystonia (CD), usually quantified by subjective observation. Technological developments offer alternatives for measuring HT severity that are objective and amenable to automation. Objectives: Our objectives were to develop CMOR (Computational Motor Objective Rater; a computer vision-based software system) to quantify oscillatory and directional aspects of HT from video recordings during a clinical examination and to test its convergent validity with clinical rating scales. Methods: For 93 participants with isolated CD and HT enrolled by the Dystonia Coalition, we analyzed video recordings from an examination segment in which participants were instructed to let their head drift to its most comfortable dystonic position. We evaluated peak power, frequency, and directional dominance, and used Spearman's correlation to measure the agreement between CMOR and clinical ratings. Results: Power averaged 0.90 (SD 1.80) deg2/Hz, and peak frequency 1.95 (SD 0.94) Hz. The dominant HT axis was pitch (antero/retrocollis) for 50%, roll (laterocollis) for 6%, and yaw (torticollis) for 44% of participants. One-sided t-tests showed substantial contributions from the secondary (t = 18.17, p < 0.0001) and tertiary (t = 12.89, p < 0.0001) HT axes. CMOR's HT severity measure positively correlated with the HT item on the Toronto Western Spasmodic Torticollis Rating Scale-2 (Spearman's rho = 0.54, p < 0.001). Conclusions: We demonstrate a new objective method to measure HT severity that requires only conventional video recordings, quantifies the complexities of HT in CD, and exhibits convergent validity with clinical severity ratings.