Abstract
Background & purpose
Tremor is a common movement disorder diagnosed employing electrophysiological methods. Today, machine learning (ML) algorithms can efficiently analyze image-based data. Thus, we subjected the dynamics of essential tremor (ET) to video-based analysis.
Methods
We enrolled 59 ET patients and 48 age-matched normal controls. The Clinical Rating Scale for Tremor was used to score tremors. All subjects used a smartphone to record an image designed especially for this study while both stationary and in motion. The trajectories were divided into lower bandpass-filtered and bandpass-filtered (BPF) groups based on the frequency. We extracted seven trajectory features, including the angle, velocity, homogeneity, pitch, power, entropy, and cosine. We used Student’s t-test to compare the features of the ET patients and normal controls. A Random Forest model was employed to rank feature importance. Five ML models (random forest, k-nearest neighbors, support vector machine, decision tree, and multi-layer perceptron) were applied to estimate diagnostic accuracy.
Results
Significant differences in most of the features of the BPF signals were evident between the two groups. The velocity and homogeneity of the BPF trajectory were highest in the stationary and motion phases, respectively. The highest accuracy levels in the stationary, motion, and combined phases for predicting ET were 0.901, 0.757, and 0.892, respectively.
Conclusions
Features of ET tremor were evident in image-based data, enabling analysis of the tremor dynamics. ML analyses distinguished ET subjects from normal controls; however, more research is needed.
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Data Availability
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
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Acknowledgements
This study was supported by a 2021 research grant from Kangwon National University Hospital and Kangwon National University. The authors would like to thank Jeong Yun Song for performing data management.
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Contributions
Conceptualization: Seung-Hwan Lee, Baeksop Kim.
Data curation: Seung-Hwan Lee.
Formal analysis: Seung-Hwan Lee, Baeksop Kim.
Investigation: Seung-Hwan Lee.
Methodology: Baeksop Kim.
Project administration: Seung-Hwan Lee, Jae-Min Shim, Baeksop Kim.
Resources: Seung-Hwan Lee, Baeksop Kim.
Software: Dongseop Lee, Jihoon Park, Jae-Min Shim, Baeksop Kim.
Supervision: Seung-Hwan Lee, Baeksop Kim.
Validation: Dongseop Lee, Jihoon Park, Jae-Min Shim, Baeksop Kim.
Writing – original draft: Seung-Hwan Lee.
Writing – review & editing: Seung-Hwan Lee, Baeksop Kim.
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This study was approved by the Institutional Review Board of Kangwon National University Hospital (KNUH-B-2021–02-005).
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Lee, SH., Lee, D., Park, J. et al. Quantification of tremor dynamics via video-based analysis. Multimed Tools Appl 83, 82963–82981 (2024). https://doi.org/10.1007/s11042-024-18438-y
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DOI: https://doi.org/10.1007/s11042-024-18438-y