On the use of histograms of oriented gradients for tremor detection from sinusoidal and spiral handwritten drawings of people with Parkinson’s disease,Medical & Biological Engineering & Computing

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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
Medical & Biological Engineering & Computing ( IF 2.6 ) Pub Date : 2021-01-07 , DOI: 10.1007/s11517-020-02303-9
João Paulo Folador ₁ , Maria Cecilia Souza Santos ₁ , Luiza Maire David Luiz ₁ , Luciane Aparecida Pascucci Sande de Souza ₂ , Marcus Fraga Vieira ₃ , Adriano Alves Pereira ₁ , Adriano de Oliveira Andrade ₁

Affiliation

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.

Graphical abstract

中文翻译：

使用定向梯度直方图检测帕金森病患者的正弦曲线和螺旋手写图的震颤

帕金森病 (PD) 的主要体征是震颤、强直、运动迟缓和姿势不稳定，逐渐降低了患者的生活质量，因此对该疾病的早期诊断和随访至关重要。本研究旨在通过将定向梯度 (HOG) 直方图引入和评估笔迹正弦和螺旋图案的分析，为 PD 震颤的客观评估做出贡献。这些模式是从 PD 患者 ( n = 20) 和对照健康个体 ( n= 20）。使用 HOG 描述符来表示由三种不同的机器学习方法（随机森林、k-最近邻、支持向量机）和深度学习方法（卷积神经网络）分类的数据中的相关信息，以识别 PD 参与者的震颤自动地。通过使用一维卷积神经网络，HOG 描述符允许对正弦模式测试集的最高判别率（准确度 83.1%，灵敏度 85.4%，特异性 80.8%，曲线下面积 91%）。此外，ANOVA 和 Tukey 分析表明，正弦图比螺旋图更合适，螺旋图是最常用的用于震颤检测的图。

图形概要

更新日期：2021-01-07

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