Physiological tremor, pathological tremor and clonus. (Progress in clinical neurophysiology, vol 5).edited by J. E. Desmedt, S. Karger, Basel, 1978 218 pp, illustrated, 7$59.00

BACKGROUND AND OBJECTIVE: Although careful clinical examination and medical history are the most important steps towards a diagnostic separation between different tremors, the electro-physiological analysis of the tremor using accelerometry and electromyography (EMG) of the affected limbs are promising tools. METHODS: A soft-decision wavelet-based decomposition technique is applied with 8 decomposition stages to estimate the power spectral density of accelerometer and surface EMG signals (sEMG) sampled at 800 Hz. A discrimination factor between physiological tremor (PH) and pathological tremor, namely, essential tremor (ET) and the tremor caused by Parkinson’s disease (PD), is obtained by summing the power entropy in band 6 (B6: 7.8125–9.375 Hz) and band 11 (B11: 15.625–17.1875 Hz). RESULTS: A discrimination accuracy of 93.87% is obtained between the PH group and the ET & PD group using a voting between three results obtained from the accelerometer signal and two sEMG signals. CONCLUSION: Biomedical signal processing techniques based on high resolution wavelet spectral analysis of accelerometer and sEMG signals are implemented to efficiently perform classification between physiological tremor and pathological tremor.

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Tremor

Involuntary Movements ◽

10.1093/med/9780190865047.003.0002 ◽

2020 ◽

pp. 7-44

Author(s):

Hiroshi Shibasaki ◽

Mark Hallett ◽

Kailash P. Bhatia ◽

Stephen G. Reich ◽

Bettina Balint

Keyword(s):

Physiological Tremor ◽

Postural Tremor ◽

Isotonic Contraction ◽

Resting Tremor ◽

Efferent Pathway ◽

Kinetic Tremor ◽

Antagonist Muscles ◽

Agonist And Antagonist ◽

Agonist And Antagonist Muscles ◽

Pathological Tremor

Tremor is broadly classified into physiological tremor and pathological tremor. Depending on the clinical features and the predominant pattern of production, tremor is classified into resting tremor, postural tremor, and kinetic tremor. Tremor is associated with rhythmic contraction of agonist and antagonist muscles, either alternately or simultaneously. Tremor involving muscles in the resting condition is called resting tremor and is seen most commonly in Parkinson disease. Tremor involving muscles during isometric contraction is called postural tremor, and it is most commonly seen in essential tremor. Tremor involving muscles during intended movements (isotonic contraction) is called kinetic tremor, and it is most commonly seen in a lesion of the cerebellar efferent pathway.

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CROSS-SPECTRAL ANALYSIS OF TREMOR TIME SERIES

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127400001663 ◽

2000 ◽

Vol 10 (11) ◽

pp. 2595-2610 ◽

Cited By ~ 31

Author(s):

J. TIMMER ◽

M. LAUK ◽

S. HÄUßLER ◽

V. RADT ◽

B. KÖSTER ◽

...

Keyword(s):

Spectral Analysis ◽

Graphical Models ◽

Normal Subjects ◽

The Body ◽

Neural Pathways ◽

Physiological Tremor ◽

Specific Difference ◽

The Right ◽

Pathological Tremor ◽

Resonant Contribution

We discuss cross-spectral analysis and report applications for the investigation of human tremors. For the physiological tremor in healthy subjects, the analysis enables to determine the resonant contribution to the oscillation and allows to test for a contribution of reflexes to this tremor. Comparing the analysis of the relation between the tremor of both hands in normal subjects and subjects with a rare abnormal organization of certain neural pathways proves the involvement of central structures in enhanced physiological tremor. The relation between the left and the right side of the body in pathological tremor shows a specific difference between orthostatic and all other forms of tremor. An investigation of EEG and tremor in patients suffering from Parkinson's disease reveals the tremor-correlated cortical activity. Finally, the general issue of interpreting the results of methods designed for the analysis of bivariate processes when applied to multivariate processes is considered. We discuss and apply partial cross-spectral analysis in the frame of graphical models as an extention of bivariate cross-spectral analysis for the multivariate case.

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