Laboratório de Processamento de Imagens, Sinais e Computação Aplicada (LAPISCO), Instituto Federal de Educação, Ciência e Tecnologia do Ceará, Fortaleza, CE, Brazil.
Comput Intell Neurosci. 2018 Apr 24;2018:7613282. doi: 10.1155/2018/7613282. eCollection 2018.
Parkinson's disease affects millions of people around the world and consequently various approaches have emerged to help diagnose this disease, among which we can highlight handwriting exams. Extracting features from handwriting exams is an important contribution of the computational field for the diagnosis of this disease. In this paper, we propose an approach that measures the similarity between the exam template and the handwritten trace of the patient following the exam template. This similarity was measured using the Structural Cooccurrence Matrix to calculate how close the handwritten trace of the patient is to the exam template. The proposed approach was evaluated using various exam templates and the handwritten traces of the patient. Each of these variations was used together with the Naïve Bayes, OPF, and SVM classifiers. In conclusion the proposed approach was proven to be better than the existing methods found in the literature and is therefore a promising tool for the diagnosis of Parkinson's disease.
帕金森病影响着全球数百万人，因此出现了各种方法来帮助诊断这种疾病，其中我们可以突出手写考试。从手写考试中提取特征是计算领域为诊断这种疾病做出的重要贡献。在本文中，我们提出了一种方法，该方法根据考试模板来测量考试模板和患者手写轨迹之间的相似性。这种相似性是使用结构共现矩阵来衡量的，以计算患者的手写轨迹与考试模板的接近程度。使用各种考试模板和患者的手写轨迹评估了所提出的方法。这些变体中的每一个都与朴素贝叶斯、OPF 和 SVM 分类器一起使用。总之，所提出的方法被证明优于文献中发现的现有方法，因此是帕金森病诊断的有前途的工具。
