Characteristics of hand tremor time series
Abstract
Tremor is classified into physiological, essential, and parkinsonian tremor by means of clinical criteria. The aim of our work was to extract quantitative features from the measurements of the acceleration of human postural hand tremor. Different mathematical methods were adopted and modified in order to separate these three types of tremor. Best discrimination between physiological and pathological tremors has been achieved by methods distinguishing nonlinear from linear behavior. On the other hand, methods separating different forms of nonlinear behavior have been found to be superior in discriminating parkinsonian and essential tremor. By these methods physiological and pathological tremors can be separated with an error rate below 20% and essential and parkinsonian tremor with an error rate below 10%. This may help to classify tremor time series by objective mathematical criteria and may increase the understanding of the pathophysiological differences underlying these kinds of tremor.
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The active test features were selected based on previous literature and their relevancy to PD.
“The following active test features were selected, based on previous literature and their relevancy to PD: (1) Sustained phonation: mel‐frequency cepstral coefficient 2 (MFCC2) 34, 35; (2) Rest tremor: skewness36, 37, 38, 39; (3) Postural tremor: total power40, 41; (4) Finger tapping: intratap variability18, 42; (5) Balance: mean velocity43, 44, 45, 46, 47; and (6) Gait: turn speed46, 48, 49, 50”
From Evaluation of smartphone‐based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial (2018) · cites this paper as [36]
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Singh, S. K., & Chaturvedi, A. (2024) · ACM Transactions on Computing for Healthcare · doi:10.1145/3700143 · Cited by 4 · see citing works
Analysis of slope based heart rate asymmetry using poincaré plots
Karmakar, C. K., Khandoker, A. H., & Palaniswami, M. S. (2012) · Computing in cardiology · Cited by 4 · see citing works
Multimodal classification of Parkinson’s disease using delay differential analysis
Weyhenmeyer, J., Hernandez, M. E., Lainscsek, C. S. M., Howard Poizner, A., & B, T. J. S. A. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313394 · Cited by 4 · see citing works
Decoupling tremor and volitional force control factors in analyses of motor task performance
Eakin, T., Francis, K., & Spirduso, W. W. (2001) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(01)00019-6 · Cited by 4 · see citing works
Porta, A., D’Addio, G., Corbi, G., Maestri, R., & Pinna, G. D. (2008) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2008.4649611 · Cited by 3 · see citing works
Frequency, Amplitude, and Waveform Characteristics of Physiologic and Pathologic Tremors
Deuschl, G., Timmer, J., Genger, H., Gantert, C., Lücking, C. H., & Honerkamp, J. (1995) · Springer eBooks · doi:10.1007/978-3-642-78914-4_9 · Cited by 3 · see citing works
Self-citationSorting ECGs by lag irreversibility
Merino-Negrete, N., Maldonado, C., & Salgado-García, R. (2023) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2023.134022 · Cited by 2 · see citing works
Identifying statistical indicators of temporal asymmetry using a data-driven approach
Nogare, T. D., & Fulcher, B. (2026) · Physical Review Research · doi:10.1103/4nb2-398s · Cited by 2 · see citing works
Raethjen, J., & Volkmann, J. (2004) · Steinkopff eBooks · doi:10.1007/978-3-7985-1956-5_9 · Cited by 2 · see citing works
Timmer, J., Lauk, M., Pfleger, W., & Deuschl, G. (1998) · arXiv (Cornell University) · doi:10.48550/arxiv.chao-dyn/9805011 · Cited by 2 · see citing works
Self-citation
- Authors
- Timmer, J., Gantert, C., Deuschl, G., & Honerkamp, J.
- Affiliations
- University of Freiburg (DE)
- Venue
- Biological Cybernetics
- Published
- 1993-11
- Volume
- 70
- Issue
- 1
- Pages
- 75-80
- ISSN
- 0340-1200 (ISSN-L)
- 1432-0770
- OpenAlex
- W2052510837
- Cited by
- 112
- References
- 13
- Citations by year
- 2026: 2 · 2024: 2 · 2023: 3 · 2022: 5 · 2021: 6 · 2020: 5 · 2019: 3 · 2018: 10 · 2017: 3 · 2016: 3 · 2015: 4 · 2014: 2 · 2013: 2 · 2012: 3
- FWCI
- 1.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Biological Cybernetics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Health Sciences › Medicine › Neurology › Neurological disorders and treatments
- Keywords
- hand tremor, tremor classification, parkinsonian tremor, essential tremor, physiological tremor, nonlinear dynamics, acceleration time series, quantitative features, tremor pathophysiology
- MeSH terms
- Cybernetics, Hand, Humans, Models, Biological, Parkinson Disease, Time Factors, Tremor
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