Bioinformatic Approaches Used in Modelling Human Tremor
Abstract
Bioinformatics is a field of information technology concerning the storage, retrieval, analysis, visualization, prediction and analysis of sets of data with biological or clinical significance. Tremor is a common movement disorder, for which pharmacological and neurophysiological models have been developed these last 3 decades, and which is at the frontier of biology, health sciences and computer technologies. Recently, new biomechanical modelling approaches of tremor have been proposed, based upon ambulatory systems and body area networks (BAN). Use of digital signal processing (DSP) techniques taking into account the non-linearity and non stationarity features of tremor time-series is reviewed in the present article. In particular, algorithms for instantaneous assessments of oscillations and direct online cancellations have been suggested. We discuss the advantages and drawbacks of the tremor detection algorithms, as well as prediction tools. In addition, promising models based upon neural networks, conductance studies and brain neurotransmitters are under development. These models will allow the accurate simulation of the behaviour of limbs. Their impact is outlined. The field of tremor research represents an excellent application of bioinformatics in medicine and rehabilitation. Keywords: Tremor, bioinformatics, brain, animal models, time-series, Fast Fourier Transform, tremor detection algorithms, computational models
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Editing remains a critical step in digital processing of tremor series.
“Editing remains a critical step in digital processing of tremor series [8,67,68].”
From Neurological Tremor: Sensors, Signal Processing and Emerging Applications (2010) · cites this paper as [67]
Pure clinical evaluation of tremor is often considered as a good clinical correlate of tremor severity, despite variability of tremor in a given patient and amongst different patients.
“However, the pure clinical evaluation of tremor is often considered as a good clinical correlate of tremor severity, despite the variability of tremor in a given patient and amongst different patients [8,67].”
From Neurological Tremor: Sensors, Signal Processing and Emerging Applications (2010) · cites this paper as [67]
Cites
Measuring normal and pathological anxiety-like behaviour in mice: a review
Belzung, C., & Griebel, G. (2001) · Behavioural Brain Research · doi:10.1016/s0166-4328(01)00291-1 · Cited by 912 · see citing works
Deuschl, G., Raethjen, J., Lindemann, M., & Krack, P. (2001) · Muscle & Nerve · doi:10.1002/mus.1063 · Cited by 508 · see citing works
Adaptive cancelling of physiological tremor for improved precision in microsurgery
Riviere, C. N., Rader, R. S., & Thakor, N. V. (1998) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.686791 · Cited by 260 · cited 2× · see citing works
“The WFLC is an adaptive algorithm that estimates tremor using a sinusoidal model, estimating its time-varying frequency, amplitude, and phase [150].”
BackgroundMethodPhysiologic and essential tremor
Elble, R. J. (1986) · Neurology · doi:10.1212/wnl.36.2.225 · Cited by 231 · cited 3× · see citing works
“The PSD is usually averaged over epochs or smoothed over frequencies for reducing the variance of estimation [39-42] and the spectral content of the signal is presented in either linear or logarithmic scale (e.”
BackgroundMechanical factors in human tremor frequency
Stiles, R. N., & Randall, J. E. (1967) · Journal of Applied Physiology · doi:10.1152/jappl.1967.23.3.324 · Cited by 191 · see citing works
The effects of load and force on tremor at the normal human elbow joint
Joyce, G. C., & Rack, P. M. H. (1974) · The Journal of Physiology · doi:10.1113/jphysiol.1974.sp010615 · Cited by 167 · see citing works
Homberg, V, Hefter, H., Reiners, K., & Freund, H. J. (1987) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp.50.5.568 · Cited by 147 · cited 1× · see citing works
“Another approach for quantifying the signal intensity consists in calculating the power for a fixed frequency range, usually –1 Hz to +1 Hz, around the peak frequency [39, 44].”
BackgroundPathological tremors: deterministic chaos or nonlinear stochastic oscillators?
Timmer, J., Häussler, S., Lauk, M., & Lücking, C.-H. (2000) · Chaos: An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.166494 · Cited by 130 · see citing works
Analyzing the dynamics of hand tremor time series
Gantert, C., Honerkamp, J., & Timmer, J. (1992) · Biological Cybernetics · doi:10.1007/bf00204112 · Cited by 74 · see citing works
Comparison of electromyogram spectra with force spectra during human elbow tremor.
Matthews, P. B., & Muir, R. B. (1980) · The Journal of Physiology · doi:10.1113/jphysiol.1980.sp013254 · Cited by 70 · see citing works
Optimal digital filtering for tremor suppression
Gonzalez, J. G., Heredia, E. A., Rahman, T., Barner, K. E., & Arce, G. R. (2000) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.841338 · Cited by 56 · see citing works
A software for recording and analysis of human tremor
Lauk, M., Timmer, J., Lücking, C. H., Honerkamp, J., & Deuschl, G. (1999) · Computer Methods and Programs in Biomedicine · doi:10.1016/s0169-2607(99)00012-7 · Cited by 56 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Neurological Tremor: Sensors, Signal Processing and Emerging Applications
Grimaldi, G., & Manto, M. (2010) · Sensors · doi:10.3390/s100201399 · Cited by 138 · see citing works
“Editing remains a critical step in digital processing of tremor series [8,67,68].”
BackgroundGait and tremor assessment for patients with Parkinson’s disease using wearable sensors
Perumal, S. V., & Sankar, R. T. (2016) · ICT Express · doi:10.1016/j.icte.2016.10.005 · Cited by 118 · see citing works
The Role of β-Carboline Alkaloids in the Pathogenesis of Essential Tremor
Laviță, S. L., Aro, R., Kiss, B., Manto, M., & Duez, P. (2015) · Cerebellum · doi:10.1007/s12311-015-0751-z · Cited by 32 · see citing works
Motion characteristics of subclinical tremors in Parkinson’s disease and normal subjects
Chan, P. Y., Ripin, Z. M., Halim, S. A., Arifin, W. N., Yahya, A. S., Eow, G. B., Tan, K., Hor, J. Y., & Wong, C. K. (2022) · Scientific Reports · doi:10.1038/s41598-022-07957-z · Cited by 33 · see citing works
Monitoring Bodily Oscillation With RFID Tags
Zhang, Y., Chen, S.-G., Zhou, Y., Fang, Y., & Qian, C. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2892000 · Cited by 19 · see citing works
Marchis, C. D., Schmid, M., & Conforto, S. (2012) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2011.12.017 · Cited by 21 · see citing works
Resonant ultrasonic wireless power transmission for bio-implants
Lee, S. Q., Youm, W., Hwang, G., Moon, K., & Ozturk, Y. (2014) · Smart Structures · doi:10.1117/12.2046600 · Cited by 16 · see citing works
Properties of different types of dry electrodes for wearable smart monitoring devices
Popović-Maneski, L., Ivanovic, M. D., Atanasoski, V. A., Miletić, M., Zdolšek, S., Bojović, B., & Hadžievski, L. (2020) · Biomedizinische Technik. Biomedical engineering · doi:10.1515/bmt-2019-0167 · Cited by 11 · see citing works
“Physiological tremor is expected oscillatory motion in fingers of all healthy individuals in the frequency range of 6–13 Hz with different amplitudes depending on the current physiological and psychological conditions [12, 13].”
BackgroundConforto, S., Bernabucci, I., Severini, G., Schmid, M., & D'Alessio, T. (2009) · Front. Neurorobot. · doi:10.3389/neuro.12.003.2009 · Cited by 11
Pinheiro, W. C., Ferraz, H. B., Castro, M. C. F., & Menegaldo, L. (2024) · IEEE transactions on neural systems and rehabilitation engineering · doi:10.1109/tnsre.2024.3373433 · Cited by 8 · see citing works
“Healthy subjects may manifest tremors with frequencies of 13-15Hz, for example, when maintaining the arm outstretched parallel to the floor [2], which can be modeled as a second-order stochastic process driven by white noise [3], [4].”
BackgroundAssessment of a hand tremor based on analysis of the accelerometer signal
Wilk, B., & Olbrycht, S. (2016) · doi:10.15199/48.2016.11.36 · Cited by 8
Tremor quantification through event-based movement trajectory modeling
Dimitrakopoulos, K., Ellmer, C., Lindstrcöm, M., & Medvedev, A. (2017) · Conference on Control Technology and Applications · doi:10.1109/ccta.2017.8062518 · Cited by 5 · see citing works
Tremor quantification through data-driven nonlinear system modeling
Medvedev, A., Olsson, F., & Wigren, T. (2017) · IEEE Conference on Decision and Control · doi:10.1109/cdc.2017.8264559 · Cited by 4 · see citing works
“Manto et al pointed out the limitations of applying spectral analysis to tremor time-series in [10]: “neither amplitude nor peak frequency provides reliable discrimination of the type of pathological tremor”.”
BackgroundEstimation of pathological tremor by using adaptive shifting BMFLC base d on RLS algorithm
Gao, Y., Wang, S., Zhao, J., & Cai, H. (2013) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma.2013.6617979 · Cited by 4 · see citing works
Quantitative Assessment of Deep Brain Stimulation on Tremor in Multiple Sclerosis Disease
Esmailpour, H., Esteki, A., & Seddighi, A. (2015) · doi:10.22037/icnj.v2i3.10152 · Cited by 2
Tremor severity rating by Markov chains
Olsson, F., & Medvedev, A. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.09.154 · Cited by 2 · see citing works
“As the authors of Mario et al. (2009) point out, neither amplitude nor peak frequency provides reliable discrimination of the type of pathological tremor.”
BackgroundInfluentialMachine Learning Techniques for Automated Tremor Detection in the Presence of External Stressors
M, V. K., & Talasila, V. (2022) · International Journal of Circuits Systems and Signal Processing · doi:10.46300/9106.2022.16.69 · Cited by 1 · see citing works
Gavas, R., Mazumder, O., & Sinha, A. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc44109.2020.9176062 · Cited by 1
“and surgical therapies for treating tremor, however, the treatment is still symptomatic and comes with inherent risks and side effects [7].”
BackgroundMotion Characteristics of Subclinical Tremors in Parkinson’s Disease and Healthy Subjects
Chan, P.-Y., Ripin, Z., Halim, S., Arifin, W. N., Yahya, A. S., Eow, G., & Tan, K. (2021) · doi:10.21203/rs.3.rs-145673/v1
- Authors
- Mario, M., Giuliana, G., Thomas, L., Dario, F., Lana, P., Silvia, C., Tommaso, D., Juan-Manuel, B.-L., Jose-Luis, P., & Eduardo, R.
- Affiliations
- Roma Tre University (IT)
- Venue
- Current Bioinformatics
- Publisher
- Bentham Science Publishers Ltd.
- Published
- 2009-05-01
- Volume
- 4
- Issue
- 2
- Pages
- 154-172
- ISSN
- 1574-8936 (ISSN-L)
- OpenAlex
- W2104196865
- Cited by
- 17
- References
- 0
- Citations by year
- 2024: 1 · 2022: 2 · 2020: 2 · 2019: 1 · 2018: 1 · 2017: 2 · 2016: 1 · 2015: 2 · 2014: 1 · 2013: 1 · 2012: 1
- FWCI
- 0.48
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Current Bioinformatics (publishedVersion)
- Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) (submittedVersion)
- Iris (Roma Tre University) (submittedVersion)
- VBN Forskningsportal (Aalborg Universitet) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Health Sciences › Medicine › Neurology › Neurological disorders and treatments
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Keywords
- tremor, movement disorders, digital signal processing, non-stationary time series, body area networks, artificial neural networks, neurotransmitters, rehabilitation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
