4 research outputs found
Exploration of Various Fractional Order Derivatives in Parkinson's Disease Dysgraphia Analysis
Parkinson's disease (PD) is a common neurodegenerative disorder with a
prevalence rate estimated to 2.0% for people aged over 65 years. Cardinal motor
symptoms of PD such as rigidity and bradykinesia affect the muscles involved in
the handwriting process resulting in handwriting abnormalities called PD
dysgraphia. Nowadays, online handwritten signal (signal with temporal
information) acquired by the digitizing tablets is the most advanced approach
of graphomotor difficulties analysis. Although the basic kinematic features
were proved to effectively quantify the symptoms of PD dysgraphia, a recent
research identified that the theory of fractional calculus can be used to
improve the graphomotor difficulties analysis. Therefore, in this study, we
follow up on our previous research, and we aim to explore the utilization of
various approaches of fractional order derivative (FD) in the analysis of PD
dysgraphia. For this purpose, we used the repetitive loops task from the
Parkinson's disease handwriting database (PaHaW). Handwritten signals were
parametrized by the kinematic features employing three FD approximations:
Gr\"unwald-Letnikov's, Riemann-Liouville's, and Caputo's. Results of the
correlation analysis revealed a significant relationship between the clinical
state and the handwriting features based on the velocity. The extracted
features by Caputo's FD approximation outperformed the rest of the analyzed FD
approaches. This was also confirmed by the results of the classification
analysis, where the best model trained by Caputo's handwriting features
resulted in a balanced accuracy of 79.73% with a sensitivity of 83.78% and a
specificity of 75.68%.Comment: Print ISBN 978-3-031-19744-
Prodromal Diagnosis of Lewy Body Diseases Based on the Assessment of Graphomotor and Handwriting Difficulties
To this date, studies focusing on the prodromal diagnosis of Lewy body
diseases (LBDs) based on quantitative analysis of graphomotor and handwriting
difficulties are missing. In this work, we enrolled 18 subjects diagnosed with
possible or probable mild cognitive impairment with Lewy bodies (MCI-LB), 7
subjects having more than 50% probability of developing Parkinson's disease
(PD), 21 subjects with both possible/probable MCI-LB and probability of PD >
50%, and 37 age- and gender-matched healthy controls (HC). Each participant
performed three tasks: Archimedean spiral drawing (to quantify graphomotor
difficulties), sentence writing task (to quantify handwriting difficulties),
and pentagon copying test (to quantify cognitive decline). Next, we
parameterized the acquired data by various temporal, kinematic, dynamic,
spatial, and task-specific features. And finally, we trained classification
models for each task separately as well as a model for their combination to
estimate the predictive power of the features for the identification of LBDs.
Using this approach we were able to identify prodromal LBDs with 74% accuracy
and showed the promising potential of computerized objective and non-invasive
diagnosis of LBDs based on the assessment of graphomotor and handwriting
difficulties.Comment: Print ISBN 978-3-031-19744-
Comparison of CNN-Learned vs. Handcrafted Features for Detection of Parkinson's Disease Dysgraphia in a Multilingual Dataset
Parkinson's disease dysgraphia (PDYS), one of the earliest signs of Parkinson's disease (PD), has been researched as a promising biomarker of PD and as the target of a noninvasive and inexpensive approach to monitoring the progress of the disease. However, although several approaches to supportive PDYS diagnosis have been proposed (mainly based on handcrafted features (HF) extracted from online handwriting or the utilization of deep neural networks), it remains unclear which approach provides the highest discrimination power and how these approaches can be transferred between different datasets and languages. This study aims to compare classification performance based on two types of features: features automatically extracted by a pretrained convolutional neural network (CNN) and HF designed by human experts. Both approaches are evaluated on a multilingual dataset collected from 143 PD patients and 151 healthy controls in the Czech Republic, United States, Colombia, and Hungary. The subjects performed the spiral drawing task (SDT; a language-independent task) and the sentence writing task (SWT; a language-dependent task). Models based on logistic regression and gradient boosting were trained in several scenarios, specifically single language (SL), leave one language out (LOLO), and all languages combined (ALC). We found that the HF slightly outperformed the CNN-extracted features in all considered evaluation scenarios for the SWT. In detail, the following balanced accuracy (BACC) scores were achieved: SL—0.65 (HF), 0.58 (CNN); LOLO—0.65 (HF), 0.57 (CNN); and ALC—0.69 (HF), 0.66 (CNN). However, in the case of the SDT, features extracted by a CNN provided competitive results: SL—0.66 (HF), 0.62 (CNN); LOLO—0.56 (HF), 0.54 (CNN); and ALC—0.60 (HF), 0.60 (CNN). In summary, regarding the SWT, the HF outperformed the CNN-extracted features over 6% (mean BACC of 0.66 for HF, and 0.60 for CNN). In the case of the SDT, both feature sets provided almost identical classification performance (mean BACC of 0.60 for HF, and 0.58 for CNN). Copyright © 2022 Galaz, Drotar, Mekyska, Gazda, Mucha, Zvoncak, Smekal, Faundez-Zanuy, Castrillon, Orozco-Arroyave, Rapcsak, Kincses, Brabenec and Rektorova
Changes in phonation and their relations with Progress of Parkinson's disease
Hypokinetic dysarthria, which is associated with Parkinson’s disease (PD), affects several speech dimensions, including phonation. Although the scientific community has dealt with a quantitative analysis of phonation in PD patients, a complex research revealing probable relations between phonatory features and progress of PD is missing. Therefore, the aim of this study is to explore these relations and model them mathematically to be able to estimate progress of PD during a two-year follow-up. We enrolled 51 PD patients who were assessed by three commonly used clinical scales. In addition, we quantified eight possible phonatory disorders in five vowels. To identify the relationship between baseline phonatory features and changes in clinical scores, we performed a partial correlation analysis. Finally, we trained XGBoost models to predict the changes in clinical scores during a two-year follow-up. For two years, the patients’ voices became more aperiodic with increased microperturbations of frequency and amplitude. Next, the XGBoost models were able to predict changes in clinical scores with an error in range 11–26%. Although we identified some significant correlations between changes in phonatory features and clinical scores, they are less interpretable. This study suggests that it is possible to predict the progress of PD based on the acoustic analysis of phonation. Moreover, it recommends utilizing the sustained vowel /i/ instead of /a/