Elsevier Editorial System(tm) for Biomedical Signal Processing and Control Manuscript Draft Manuscript Number: BSPC-D-17-00438R1 Title: Quantitative Assessment of Parkinsonian Tremor Based on a Linear Acceleration Extraction Algorithm Article Type: Research Paper Keywords: Parkinson's disease; Tremor quantification; Wearable device; Linear acceleration; Multiple regression analysis Corresponding Author: Professor Houde Dai, Ph.D. Corresponding Author's Institution: Haixi Institutes, Chinese Academy of Sciences First Author: Guoen Cai, M.D. Order of Authors: Guoen Cai, M.D.; Zhirong Lin, M.D.; Houde Dai, Ph.D.; Xuke Xia, M.D.; Yongsheng Xiong, M.D.; Shi-Jinn Horng, Ph.D.; Tim C. Lueth, Ph.D. Abstract: Tremor detection plays a crucial role in Parkinson's disease (PD) treatment and symptom monitoring. The current gold standard for the clinical assessment of parkinsonian tremor is the evaluation using the standard clinical rating scales, which is performed by the well-trained neurologists. However, this subjective assessment approach shows low consistency between different evaluators. This study, on the basis of the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) criteria, proposed a custom quantitative assessment system for parkinsonian tremors. It adopted an attitude estimation-based gradient descent algorithm to separate the linear acceleration (caused by pure translational motion) from the accelerometer output, which combines gravity component. Signal features extracted from the linear accelerations and angular velocities during the tremor tasks were fitted to the clinicians' ratings with a multiple regression model. Clinical experiments with 34 PD patients and 14 age-matched controls demonstrated that the prediction accuracy was improved by using the decomposed linear acceleration for the extraction of tremor features, which has promoted assessment accuracy compared with the relevant literature (r2 improved from 0.89 to 0.95 for rest tremor, and from 0.90 to 0.93 for postural tremor). In addition, the prediction accuracy was worse when using only the linear accelerations for regression analysis (r2 reduced from 0.95 to 0.87 for rest tremor, and from 0.93 to 0.84 for postural tremor), which means that the effect of rotational motion cannot be ignored in tremor quantification.
Cover Letter 2017-12-01 - 04:35
QIEM-HI-CAS Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences
Page 1/1
Intellectual Property Confidential Author: Houde Dai
Dear editors, On behalf of my co-authors, I am submitting the revised manuscript “Quantitative Assessment of Parkinsonian Tremor Based on A Linear Acceleration Extraction Algorithm” (ID ：BSPC-D-17-00438) for possible publication in the Biomedical Signal Processing and Control. Thank you very much for your letter and advice. We have revised the manuscript, and would like to re-submit it for your consideration. We have addressed the comments raised by the reviewers, and the amendments are highlighted in red in the revised manuscript. Our responses to the reviewers’ comments are attached in another document. In addition, we have added professor Tim C. Lueth in the the author list of the revised manuscript because of his valuable instructions. We hope that the revised version of the manuscript is now acceptable for publication in your journal. I am looking forward to hearing from you soon. Best Regards. Yours Sincerely, Name: Prof. Dr.-Ing. Houde Dai Quanzhou Institute of Equipment of Manufacturing, Haixi Institutes, Chinese Academy of Sciences ADDRESS: Bolan Av., Jinjiang, Fujian, P.R.China, 362200 Tel: (86)-595-36350373; Email:
[email protected]; www.casqiem.ac.cn; www.fjirsm.ac.cn Information for project partners: Guoen Cai, Attending physician Fujian Medical University Union Hospital NO.29, Xinquan Road, Fuzhou City, Fujian Province, China Tel: (86)-591-83357896 FAX: (86)-591-87113828 Email:
[email protected] http://www.fjxiehe.com/english/index.jsp
Haixi Institutes, Chinese Academy of Sciences •Research Center for Intelligent Sensing and Industrial Automation, Quanzhou Institute of Equipment Manufacturing •Bolan Av., Jinjiang, Fujian, P.R.China •P.C:362200 Tel:+86 595 36350373 •Fax:+86 595 82668861 •http://www.casqiem.ac.cn/ •http://english.fjirsm.cas.cn/
Detailed Response to Reviewers
Response Letter Dear Editors and Reviewers: We would like to express our sincere gratitude to you for the constructive comments concerning our manuscript entitled“Quantitative Assessment of Parkinsonian Tremor Based on a Linear Acceleration Extraction Algorithm” (ID：BSPC-D-17-00438). Those comments are all valuable and helpful for revising and improving our manuscript, and are of important guiding significance to our research. We have studied comments carefully and have made a major revision in accordance with those comments. Revised portions were marked in red in the manuscript. We hope that the revised manuscript will meet the requirements for publication on your journal. The main corrections in the manuscript and the responses to your comments are listed as following: Reviewer 1: Comment 1：Limb motion due to tremor is primarily rotational motion of a body segment about its joint. An unanswered question is why bother with the accelerometer? Does the gravity-free accelerometer signal correlate better with the UPDRS than the gyroscopic signal? I doubt it. Response： Many thanks! According to the previous report [30], motion of a body part typically consists of translational motion and rotation in three-dimensional space, which can be detected through a triaxial accelerometer (linear acceleration and gravity vectors) and a triaxial gyroscope (angular velocity vector) respectively. As most tremor motion is a mixture of translation and rotation, accelerometers can be used in combination with gyroscopic transducers to completely record the translational and rotational motion of a body part. Moreover, the MDS-UPDRS rates tremor severity according to the linear displacement of its motion. Therefore, it is obvious that tracking the linear movement of tremor is of significant concern for the quantitative tremor assessment. Hence, we used an IMU (which consists of a triaxial accelerometer and a triaxial gyroscope), instead of only accelerometers or only gyroscopes, to capture tremor features in the proposed tremor quantification method. According to Table 3 in the manuscript, the time-domain feature ampaL (computed by the gravity-free accelerometer signal) correlated better with the UPDRS than the feature ampω (computed by the gyroscopic signal). In addition, the frequency-domain features Spω (computed by the gyroscopic signal) had an even greater correlation with the UPDRS scores. Therefore, we used these four tremor futures which were correlated well with the clinical tremor scores for the regression modeling to obtain the satisfactory prediction performance. Table 3 Quantitative features correlations to clinical UPDRS Quantitative Features ampaL ampω sdaL Spω ln(ampaL)
Rest tremor r 0.72 0.70 0.69 0.74 0.95
p
