Microcomputer Analysis of Finger Tapping as a Measure of Cerebellar Dysfunction

Shimoyama, Ichiro; Hinokuma, Kaoru; Ninchoji, Toshiaki; Uemura, Kenichi

doi:10.2176/nmc.23.437

Cited by 11 publications

(7 citation statements)

References 2 publications

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“…Several studies evaluated finger taps (Task 1) [51–55], finger to nose task (Task 2) [51], pronation and supination (Task 3) [56], and postural tremor of the hands (Task 4) [57]. Thus, results from our study came from real procedures widely used in neurological assessments.…”

Section: Discussionmentioning

confidence: 99%

Feature visualization and classification for the discrimination between individuals with Parkinson’s disease under levodopa and DBS treatments

et al. 2016

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BackgroundOver the years, a number of distinct treatments have been adopted for the management of the motor symptoms of Parkinson’s disease (PD), including pharmacologic therapies and deep brain stimulation (DBS). Efficacy is most often evaluated by subjective assessments, which are prone to error and dependent on the experience of the examiner. Our goal was to identify an objective means of assessing response to therapy.MethodsIn this study, we employed objective analyses in order to visualize and identify differences between three groups: healthy control (N = 10), subjects with PD treated with DBS (N = 12), and subjects with PD treated with levodopa (N = 16). Subjects were assessed during execution of three dynamic tasks (finger taps, finger to nose, supination and pronation) and a static task (extended arm with no active movement). Measurements were acquired with two pairs of inertial and electromyographic sensors. Feature extraction was applied to estimate the relevant information from the data after which the high-dimensional feature space was reduced to a two-dimensional space using the nonlinear Sammon’s map. Non-parametric analysis of variance was employed for the verification of relevant statistical differences among the groups (p < 0.05). In addition, K-fold cross-validation for discriminant analysis based on Gaussian Finite Mixture Modeling was employed for data classification.ResultsThe results showed visual and statistical differences for all groups and conditions (i.e., static and dynamic tasks). The employed methods were successful for the discrimination of the groups. Classification accuracy was 81 ± 6% (mean ± standard deviation) and 71 ± 8%, for training and test groups respectively.ConclusionsThis research showed the discrimination between healthy and diseased groups conditions. The methods were also able to discriminate individuals with PD treated with DBS and levodopa. These methods enable objective characterization and visualization of features extracted from inertial and electromyographic sensors for different groups.

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Section: Discussionmentioning

confidence: 99%

Feature visualization and classification for the discrimination between individuals with Parkinson’s disease under levodopa and DBS treatments

et al. 2016

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“…= not significant.) Parkinson disease, 12,13 and spastic hemiparesis, 16 while no previous reports have demonstrated it in patients with cervical myelopathy. It is reported that there are neural networks in the spinal cord of vertebrates, which can generate rhythmic patterns in activities such as walking, scratching, and swimming.…”

Section: Discussionmentioning

confidence: 99%

“…[11][12][13] More recently, the test is being used to evaluate symptoms in patients after brain stroke, 14,15 spastic hemiparesis, 16 Alzheimer disease, 17 and essential tremor. 18 Thus, the finger-tapping test is one of the most common performance tests for evaluating neurological disorders.…”

Section: Discussionmentioning

confidence: 99%

“…We first used this test to evaluate patients with cervical compression myelopathy that involves pyramidal but not extrapyramidal tract lesions. In previous studies, tapping motion has been evaluated using a keyboard, 16 electrical board, 11,12 or 3-dimensional optoelectronic camera tracking. 19 However, the methods with keyboard or electrical board can monitor only a certain point of movement (that is "on" and "off").…”

Section: Discussionmentioning

confidence: 99%

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Finger-tapping Motion Analysis in Cervical Myelopathy by Magnetic-Sensor Tapping Device

Miwa

Hosono

Mukai

et al. 2013

Journal of Spinal Disorders &Amp; Techniques

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“…There were several reports for quantitative evaluation of the finger tapping movement. By using a keyboard of PC and a mouse, rhythm of the finger tapping was evaluated [2]- [7]. In the some studies, the amplitude and velocity during the finger tapping were measured by using a 3D position measurement system [8] or high speed cameras [9,10].…”

Section: Introductionmentioning

confidence: 99%

Estimation of UPDRS Finger Tapping Score by using Artificial Neural Network for Quantitative Diagnosis of Parkinson's disease

Fukawa

Okuno

Yokoe

et al. 2007

2007 6th International Special Topic Conference on Information Technology Applications in Biomedicine

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The purpose of this study was to estimate UPDRS finger tapping score with Parkinson's disease patients by using an artificial neural network. The measurement system was composed of a pair of 3-axis accelerometers, a pair of touch sensors, an analog to digital converter and a personal computer. Firstly, the accelerations during the finger tapping were measured with 44 normal subjects and 17 Parkinson's diseases subjects by using this system. Secondly, the four features were extracted from the obtained accelerations. Finally, the UPDRS finger tapping score was estimated by using a three-layer artificial neural network model.

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Microcomputer Analysis of Finger Tapping as a Measure of Cerebellar Dysfunction

Cited by 11 publications

References 2 publications

Feature visualization and classification for the discrimination between individuals with Parkinson’s disease under levodopa and DBS treatments

Feature visualization and classification for the discrimination between individuals with Parkinson’s disease under levodopa and DBS treatments

Finger-tapping Motion Analysis in Cervical Myelopathy by Magnetic-Sensor Tapping Device

Estimation of UPDRS Finger Tapping Score by using Artificial Neural Network for Quantitative Diagnosis of Parkinson's disease

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