Abstract

Parkinson has remained as one of the most difficult diseases to diagnose, as there are no biomarkers to be measured, and this requires one patient to do neurological and physical examinations. As Parkinson is a progressive disease, accurate detection of its symptoms is a crucial factor for therapeutic reasons. In this study, we perform Multifractal Detrended Fluctuation Analysis (MFDFA) on measured keystroke time series for three different categories of subjects: healthy, early-PD, and De-Novo patients. We have observed different scaling behavior in terms of multifractality of the measured time series, which can be used as a practical tool for diagnosis purposes. Additionally, the source of the multifractality has been studied which shows that in healthy and early-PD subjects, multifractality due to the long-range correlations is stronger than the influence of its probability distribution function (PDF) fatness, while in De-Novo patients, both shape of PDF and long-range correlations are contributing to observed multifractality.

Graphical abstract

中文翻译：

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帕金森氏病患者在测量的击键时间序列中的缩放行为

摘要

帕金森病仍然是最难诊断的疾病之一，因为没有可测量的生物标志物，这需要一名患者进行神经和身体检查。由于帕金森病是一种进行性疾病，因此出于治疗原因，对其症状的准确检测是至关重要的因素。在这项研究中，我们对三种不同类别的受试者（健康，PD早期和De-Novo患者）的测得的击键时间序列进行了多重分形趋势波动分析（MFDFA）。我们在测量时间序列的多重分数方面观察到了不同的缩放行为，可以将其用作诊断目的的实用工具。此外，还对多重分形的来源进行了研究，结果表明，在健康的早期PD患者中，

图形概要