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On the use of histograms of oriented gradients for tremor detection from sinusoidal and spiral handwritten drawings of people with Parkinson’s disease

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Abstract

Parkinson’s disease (PD), whose cardinal signs are tremor, rigidity, bradykinesia, and postural instability, gradually reduces the quality of life of the patient, making early diagnosis and follow-up of the disorder essential. This study aims to contribute to the objective evaluation of tremor in PD by introducing and assessing histograms of oriented gradients (HOG) to the analysis of handwriting sinusoidal and spiral patterns. These patterns were digitized and collected from handwritten drawings of people with PD (n = 20) and control healthy individuals (n = 20). The HOG descriptor was employed to represent relevant information from the data classified by three distinct machine-learning methods (random forest, k-nearest neighbor, support vector machine) and a deep learning method (convolutional neural network) to identify tremor in participants with PD automatically. The HOG descriptor allowed for the highest discriminating rates (accuracy 83.1%, sensitivity 85.4%, specificity 80.8%, area under the curve 91%) on the test set of sinusoidal patterns by using the one-dimensional convolutional neural network. In addition, ANOVA and Tukey analysis showed that the sinusoidal drawing is more appropriate than the spiral pattern, which is the most common drawing used for tremor detection. This research introduces a novel and alternative way of quantifying and evaluating tremor by means of handwritten drawings.

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Acknowledgments

The present work was carried out with the support of the National Council for Scientific and Technological Development (CNPq), Coordination for the Improvement of Higher Education Personnel (CAPES–Program CAPES/DFATD-88887.159028/2017-00, Program CAPES/COFECUB-88881.370894/2019-01) and the Foundation for Research Support of the State of Minas Gerais (FAPEMIG-APQ-00942-17). A. O. Andrade, A. A. Pereira, and M. F. Vieira are fellows of CNPq, Brazil (304818/2018-6, 310911/2017-6, and 306205/2017-3).

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Authors and Affiliations

  1. Centre for Innovation and Technology Assessment in Health, Postgraduate Program in Electrical and Biomedical Engineering, Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia, Brazil

    João Paulo Folador, Maria Cecilia Souza Santos, Luiza Maire David Luiz, Adriano Alves Pereira & Adriano de Oliveira Andrade

  2. Applied Physiotherapy Department, Federal University of Triângulo Mineiro, Uberaba, Brazil

    Luciane Aparecida Pascucci Sande de Souza

  3. Bioengineering and Biomechanics Laboratory, Federal University of Goiás, Goiânia, Brazil

    Marcus Fraga Vieira

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  1. João Paulo Folador
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Contributions

João Paulo Folador was responsible for the coding of programs for data analysis and storage, aiding in data collection, and writing and revising the paper. This work is one of the primary outcomes of his Ph.D. thesis.

Maria Cecilia Souza Santos, Luiza Maire David Luiz, and Luciane Aparecida Pascucci Sande de Souza are specialists in health sciences and Parkinson’s disease. They were responsible for designing the experimental protocols, assessing patients through the application of the UPDRS, and data collection and paper revision.

Adriano Alves Pereira and Marcus Fraga Vieira contributed with the writing and revision of the paper.

Adriano de Oliveira Andrade was the principal supervisor of the study, contributing to all the steps from its conception to its conclusion, in addition to the paper writing and revision.

Corresponding author

Correspondence to João Paulo Folador.

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The study was approved by the Research Ethics Committee of the Federal University of Uberlândia (CAAE: 07075413.6.0000.5152).

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Informed consent was obtained from all individual participants included in the study.

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Folador, J.P., Santos, M.C.S., Luiz, L.M.D. et al. On the use of histograms of oriented gradients for tremor detection from sinusoidal and spiral handwritten drawings of people with Parkinson’s disease. Med Biol Eng Comput 59, 195–214 (2021). https://doi.org/10.1007/s11517-020-02303-9

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