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Volume 24
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10.3390/s24154960
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Article

Development of a Tremor Detection Algorithm for Use in an Academic Movement Disorders Center

by
Mark Saad
1,
Sofia Hefner
2,
Suzann Donovan
3,
Doug Bernhard
1,
Richa Tripathi
1,
Stewart A. Factor
1,
Jeanne M. Powell
4,
Hyeokhyen Kwon
5,
Reza Sameni
5,6,
Christine D. Esper
1 and
J. Lucas McKay
1,5,*
1
Jean and Paul Amos Parkinson’s Disease and Movement Disorders Program, Department of Neurology, School of Medicine, Emory University, Atlanta, GA 30322, USA
2
Department of Neuroscience, Georgia Institute of Technology, Atlanta, GA 30322, USA
3
Department of Neuroscience and Behavioral Biology, College of Arts and Sciences, Emory University, Atlanta, GA 30322, USA
4
Department of Psychology, Laney Graduate School, Emory University, Atlanta, GA 30322, USA
5
Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA
6
Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30322, USA
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(15), 4960; https://doi.org/10.3390/s24154960
Submission received: 25 May 2024 / Revised: 24 July 2024 / Accepted: 28 July 2024 / Published: 31 July 2024
(This article belongs to the Special Issue 3D Sensing and Imaging for Biomedical Investigations)
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Abstract

Tremor, defined as an “involuntary, rhythmic, oscillatory movement of a body part”, is a key feature of many neurological conditions including Parkinson’s disease and essential tremor. Clinical assessment continues to be performed by visual observation with quantification on clinical scales. Methodologies for objectively quantifying tremor are promising but remain non-standardized across centers. Our center performs full-body behavioral testing with 3D motion capture for clinical and research purposes in patients with Parkinson’s disease, essential tremor, and other conditions. The objective of this study was to assess the ability of several candidate processing pipelines to identify the presence or absence of tremor in kinematic data from patients with confirmed movement disorders and compare them to expert ratings from movement disorders specialists. We curated a database of 2272 separate kinematic data recordings from our center, each of which was contemporaneously annotated as tremor present or absent by a movement physician. We compared the ability of six separate processing pipelines to recreate clinician ratings based on F1 score, in addition to accuracy, precision, and recall. The performance across algorithms was generally comparable. The average F1 score was 0.84±0.02 (mean ± SD; range 0.81–0.87). The second highest performing algorithm (cross-validated F1=0.87) was a hybrid that used engineered features adapted from an algorithm in longstanding clinical use with a modern Support Vector Machine classifier. Taken together, our results suggest the potential to update legacy clinical decision support systems to incorporate modern machine learning classifiers to create better-performing tools.
Keywords: motion capture; Parkinson’s disease; essential tremor; machine learning; support vector machines; XGBoost motion capture; Parkinson’s disease; essential tremor; machine learning; support vector machines; XGBoost

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MDPI and ACS Style

Saad, M.; Hefner, S.; Donovan, S.; Bernhard, D.; Tripathi, R.; Factor, S.A.; Powell, J.M.; Kwon, H.; Sameni, R.; Esper, C.D.; et al. Development of a Tremor Detection Algorithm for Use in an Academic Movement Disorders Center. Sensors 2024, 24, 4960. https://doi.org/10.3390/s24154960

AMA Style

Saad M, Hefner S, Donovan S, Bernhard D, Tripathi R, Factor SA, Powell JM, Kwon H, Sameni R, Esper CD, et al. Development of a Tremor Detection Algorithm for Use in an Academic Movement Disorders Center. Sensors. 2024; 24(15):4960. https://doi.org/10.3390/s24154960

Chicago/Turabian Style

Saad, Mark, Sofia Hefner, Suzann Donovan, Doug Bernhard, Richa Tripathi, Stewart A. Factor, Jeanne M. Powell, Hyeokhyen Kwon, Reza Sameni, Christine D. Esper, and et al. 2024. "Development of a Tremor Detection Algorithm for Use in an Academic Movement Disorders Center" Sensors 24, no. 15: 4960. https://doi.org/10.3390/s24154960

APA Style

Saad, M., Hefner, S., Donovan, S., Bernhard, D., Tripathi, R., Factor, S. A., Powell, J. M., Kwon, H., Sameni, R., Esper, C. D., & McKay, J. L. (2024). Development of a Tremor Detection Algorithm for Use in an Academic Movement Disorders Center. Sensors, 24(15), 4960. https://doi.org/10.3390/s24154960

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