Automatic Analysis of Archimedes’ Spiral for Characterization of Genetic Essential Tremor Based on Shannon’s Entropy and Fractal Dimension

López-de-Ipiña, Karmele; Solé-Casals, Jordi; Faundez-Zanuy, Marcos; Calvo, Pilar M.; Sesa, Enric; Roure, Josep; Martinez-de-Lizarduy, U.; Beitia, B.; Fernández, Elsa; Iradi, Jon; Garcia-Melero, J.; Bergareche, Alberto

doi:10.3390/e20070531

Cited by 25 publications

(28 citation statements)

References 44 publications

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“…), were used in the previous works, now only the x and y coordinate points are used. For example, comparing our results with those presented in our recently publiched work (López-de-Ipiña et al, 2018), we can see that we propose a new set of extremely reduced features derived directly from the x and y coordinate points, which allows us to obtain better results (97.96% against 91%) than those in (López-de-Ipiña et al, 2018). We combined x and y coordinate values in two ways: (i) calculating the radius and (ii) calculating the residue after reconstructing the coordinate points using the cosine and inverse cosine transforms.…”

Section: Resultssupporting

confidence: 53%

“…In particular, the SVM classifier was the best choice for both methods, and the results obtained with the residue method clearly outperforms the results obtained with the radius method. The best results, using only one of the methods is close to 96% of accuracy, clearly exceeding the best results obtained in (Lopez De Ipina et al, 2015) and (López-de-Ipiña et al, 2018) and similar to those obtained in (López-de-Ipiña et al, 2016), in all the cases using the same database. But the combination of both methods allowed to increase up to almost 98% of accuracy.…”

Section: Resultssupporting

confidence: 50%

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Discrete Cosine Transform for the Analysis of Essential Tremor

et al. 2019

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Essential tremor (ET) is the most common movement disorder. In fact, its prevalence is about 20 times higher than that of Parkinson's disease. In addition, studies have shown that a high percentage of cases, between 50 and 70%, are estimated to be of genetic origin. The gold standard test for diagnosis, monitoring and to differentiate between both pathologies is based on the drawing of the Archimedes' spiral. Our major challenge is to develop the simplest system able to correctly classify Archimedes' spirals, therefore we will exclusively use the information of the x and y coordinates. This is the minimum information provided by any digitizing device. We explore the use of features from drawings related to the Discrete Cosine Transform as part of a wider cross-study for the diagnosis of essential tremor held at Biodonostia. We compare the performance of these features against other classic and already analyzed ones. We outperform previous results using a very simple system and a reduced set of features. Because the system is simple, it will be possible to implement it in a portable device (microcontroller), which will receive the x and y coordinates and will issue the classification result. This can be done in real time, and therefore without needing any extra job from the medical team. In future works these new drawing-biomarkers will be integrated with the ones obtained in the previous Biodonostia study. Undoubtedly, the use of this technology and user-friendly tools based on indirect measures could provide remarkable social and economic benefits.

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Section: Resultssupporting

confidence: 53%

Section: Resultssupporting

confidence: 50%

Discrete Cosine Transform for the Analysis of Essential Tremor

et al. 2019

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“…Recently, many works have focused on providing digital cognitive and motoric function self-assessment tests on electronic consumer devices, such as smartphones [8], tablets [9], and dedicated graphical tablets [10]. Commonly implemented tests include Archimedes spiral drawing tasks [11][12][13][14][15][16][17], finger tapping [9], freehand drawing tasks [17], and tracing tasks [12,17]. The methods used to analyze the collected spatiotemporal finger tapping, finger drawing, or pen path data include statistical analysis [11,12,15], discrete cosine transform (DCT) features [10], entropy, and fractal dimension analysis [13].…”

Section: Related Workmentioning

confidence: 99%

A Mobile Application for Smart Computer-Aided Self-Administered Testing of Cognition, Speech, and Motor Impairment

Lauraitis

Maskeliūnas

Damaševičius

et al. 2020

Sensors

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We present a model for digital neural impairment screening and self-assessment, which can evaluate cognitive and motor deficits for patients with symptoms of central nervous system (CNS) disorders, such as mild cognitive impairment (MCI), Parkinson’s disease (PD), Huntington’s disease (HD), or dementia. The data was collected with an Android mobile application that can track cognitive, hand tremor, energy expenditure, and speech features of subjects. We extracted 238 features as the model inputs using 16 tasks, 12 of them were based on a self-administered cognitive testing (SAGE) methodology and others used finger tapping and voice features acquired from the sensors of a smart mobile device (smartphone or tablet). Fifteen subjects were involved in the investigation: 7 patients with neurological disorders (1 with Parkinson’s disease, 3 with Huntington’s disease, 1 with early dementia, 1 with cerebral palsy, 1 post-stroke) and 8 healthy subjects. The finger tapping, SAGE, energy expenditure, and speech analysis features were used for neural impairment evaluations. The best results were achieved using a fusion of 13 classifiers for combined finger tapping and SAGE features (96.12% accuracy), and using bidirectional long short-term memory (BiLSTM) (94.29% accuracy) for speech analysis features.

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“…Teaching predictive models based on both of the aforementioned groups of parameters, being a multimodal approach, is a novelty in the field of the kinetic tremor diagnostics. The solutions found in literature are based exclusively on one of two types of parameters mentioned above, commonly with a disease diagnosis or a disease stage classification as an output [8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…An interesting example of parametrizing data from a digitizing tablet by features based on field-specific knowledge can be found in the study by Lopez-de-Ipna et al [13] that was targeted on early ET diagnosis. The authors used linear and nonlinear features (the latter being complexity metrics, namely a Shannon entropy and a fractal dimension) of the drawings of spirals as an input for the classifiers such as support vector machine, multilayer perceptron neural network (MLP), and k-nearest neighbors algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Multimodal Approach to the Quantification of Kinetic Tremor in Parkinson’s Disease

Szumilas

Lewenstein

Ślubowska

et al. 2019

Sensors

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Parkinson's disease results in motor impairment that deteriorates patients' quality of life. One of the symptoms negatively interfering with daily activities is kinetic tremor which should be measured to monitor the outcome of therapy. A new instrumented method of quantification of the kinetic tremor is proposed, based on the analysis of circles drawn on a digitizing tablet by a patient. The aim of this approach is to obtain a tremor scoring equivalent to that performed by trained clinicians. Models are trained with the least absolute shrinkage and selection operator (LASSO) method to predict the tremor scores on the basis of the parameters computed from the patients' drawings. Signal parametrization is derived from both expert knowledge and the response of an artificial neural network to the raw data, thus the approach was named multimodal. The fitted models are eventually combined into model ensembles that provide aggregated scores of the kinetic tremor captured in the drawings. The method was verified with a set of clinical data acquired from 64 Parkinson's disease patients. Automated and objective quantification of the kinetic tremor with the presented approach yielded promising results, as the Pearson's correlations between the visual ratings of tremor and the model predictions ranged from 0.839 to 0.890 in the best-performing models.

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Automatic Analysis of Archimedes’ Spiral for Characterization of Genetic Essential Tremor Based on Shannon’s Entropy and Fractal Dimension

Cited by 25 publications

References 44 publications

Discrete Cosine Transform for the Analysis of Essential Tremor

Discrete Cosine Transform for the Analysis of Essential Tremor

A Mobile Application for Smart Computer-Aided Self-Administered Testing of Cognition, Speech, and Motor Impairment

A Multimodal Approach to the Quantification of Kinetic Tremor in Parkinson’s Disease

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