Distinguishing Different Stages of Parkinson’s Disease Using Composite Index of Speed and Pen-Pressure of Sketching a Spiral

Zham, Poonam; Kumar, Deepak; Dabnichki, Peter; Arjunan, Sridhar P.; Raghav, Sanjay

doi:10.3389/fneur.2017.00435

Cited by 106 publications

(42 citation statements)

References 28 publications

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“…In other words, the Parkinsonian group is typically considered as a single cluster in which all subjects share the same degree of disease severity. In Reference [92], Zham et al addressed this issue by investigating the correlation between the speed and pen pressure while sketching a spiral and the severity of disease symptoms. The strongest correlation was found with a combination of these two parameters, which turned out to be useful for the automatic differentiation between the low and high degree of severity.…”

Section: Disease Diagnosismentioning

confidence: 99%

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Vessio

2019

Applied Sciences

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Studying the effects of neurodegeneration on handwriting has emerged as an interdisciplinary research topic and has attracted considerable interest from psychologists to neuroscientists and from physicians to computer scientists. The complexity of handwriting, in fact, appears to be sensitive to age-related impairments in cognitive functioning; thus, analyzing handwriting in elderly people may facilitate the diagnosis and monitoring of these impairments. A large body of knowledge has been collected in the last thirty years thanks to the advent of new technologies which allow researchers to investigate not only the static characteristics of handwriting but also especially the dynamic aspects of the handwriting process. The present paper aims at providing an overview of the most relevant literature investigating the application of dynamic handwriting analysis in neurodegenerative disease assessment. The focus, in particular, is on Parkinon’s disease (PD) and Alzheimer’s disease (AD), as the two most widespread neurodegenerative disorders. More specifically, the studies taken into account are grouped in accordance with three main research questions: disease insight, disease monitoring, and disease diagnosis. The net result is that dynamic handwriting analysis is a powerful, noninvasive, and low-cost tool for real-time diagnosis and follow-up of PD and AD. In conclusion of the paper, open issues still demanding further research are highlighted.

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Section: Disease Diagnosismentioning

confidence: 99%

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Vessio

2019

Applied Sciences

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“…monitoring services, PD has been a focus of early large-scale data gathering studies, such as mPower (16). Many of these approaches, like the one presented in this report, are task-based and require a patient to generate data electively (17)(18)(19)(20)(21)(22). Other approaches use background-running software to collect data from a patient's quotidian interactions with their devices (23)(24)(25)(26).…”

Section: Introductionmentioning

confidence: 99%

Multi-Dimensional, Short-Timescale Quantification of Parkinson's Disease and Essential Tremor Motor Dysfunction

Sanderson

Liu

et al. 2020

Front. Neurol.

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Introduction: Parkinson's disease (PD) is a progressive movement disorder characterized by heterogenous motor dysfunction with fluctuations in severity. Objective, short-timescale characterization of this dysfunction is necessary as therapies become increasingly adaptive. Objectives: This study aims to characterize a novel, naturalistic, and goal-directed tablet-based task and complementary analysis protocol designed to characterize the motor features of PD. Methods: A total of 26 patients with PD and without deep brain stimulation (DBS), 20 control subjects, and eight patients with PD and with DBS completed the task. Eight metrics, each designed to capture an aspect of motor dysfunction in PD, were calculated from 1-second, non-overlapping epochs of the raw positional and pressure data captured during task completion. These metrics were used to generate a classifier using a support vector machine (SVM) model to produce a unifying, scalar "motor error score" (MES). The data generated from these patients with PD were compared to same-day standard clinical assessments. Additionally, these data were compared to analogous data generated from a separate group of 12 patients with essential tremor (ET) to assess the task's specificity for different movement disorders. Finally, an SVM model was generated for each of the eight patients with PD and with DBS to differentiate between their motor dysfunction in the "DBS On" and "DBS Off" stimulation states. Results: The eight metrics calculated from the raw positional and force data captured during task completion were non-redundant. MES generated by the SVM analysis protocol showed a strong correlation with MDS-UPDRS-III scores assigned by movement disorder specialists. Analysis of the relative contributions of each of the eight metrics showed a significant difference between the motor dysfunction of PD and ET. Much of this difference was attributable to the homogenous, tremor-dominant phenotype Sanderson et al. Quantifying Movement Disorders on a Tablet of ET motor dysfunction. Finally, in individual patients with PD with DBS, task performance and subsequent SVM classification effectively differentiated between the "DBS On" and "DBS Off" stimulation states. Conclusion: This tablet-based task and analysis protocol correlated strongly with expert clinical assessments of PD motor dysfunction. Additionally, the task showed specificity for PD when compared to ET, another common movement disorder. This specificity was driven by the relative heterogeneity of motor dysfunction of PD compared to ET. Finally, the task was able to distinguish between the "DBS On" and "DBS Off" states within single patients with PD. This task provides temporally-precise and specific information about motor dysfunction in at least two movement disorders that could feasibly correlate to neural activity.

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“…The distances between the two points from the SST and DST plots of the subjects were compared and compared, and a tablet-based remote monitoring system was designed to measure the cortical and motor performances of the subjects [10]. In the studies performed, the success of the spiral analysis test in detecting early PD was demonstrated statistically [11,12].…”

Section: Introductionmentioning

confidence: 99%

Parkinson Hastalığının Teşhisinde YSA Destekli Karar Sistemi Başarımı

Fidan

Ozkan

2020

European Journal of Science and Technology

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Parkinson's disease (PD) is a neurodegenerative disease that results in the loss of function of dopamine-producing brain cells. Primer designation of PD; is seen as tremor in the upper and lower limbs in 70% of the patients, and as in slowing and stiffness in the movement in 30% of them. Archimedes spiral technique is a clinical test method developed for examining PD motor disorders. The reliability and validity of the spiral test drawing technique was statistically proven by comparing it with the Unified Predictive Rating Scale (UPDRS). In this study, it was aimed to construct a static spiral test and a dynamic spiral test drawings, to extract the characteristics using the signal processing techniques and to identify the Parkinson's disease using the artificial neural network model. In the classification of the disease, only SST and ANN using only DST and f score ratio in the classification were found to be 0.95 and 0.92, respectively. When SST and DST methods were evaluated together, ANN classification success was found to be 0.99. For this reason, it was found that SST and DST methods were more successful in the classification of the disease than the classification using SST and DST alone. Using the combination of SST and DST data as a result of the study, PD was classified with artificial intelligence techniques with an accuracy of 98.6% and a score of 0.99 f.

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Distinguishing Different Stages of Parkinson’s Disease Using Composite Index of Speed and Pen-Pressure of Sketching a Spiral

Cited by 106 publications

References 28 publications

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Multi-Dimensional, Short-Timescale Quantification of Parkinson's Disease and Essential Tremor Motor Dysfunction

Parkinson Hastalığının Teşhisinde YSA Destekli Karar Sistemi Başarımı

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