Online Drawings for Dementia Diagnose: In-Air and Pressure Information Analysis

Faundez-Zanuy, Marcos; Sesa-Nogueras, Enric; Roure-Alcobe, Josep; Garré-Olmo, Josep; López-de-Ipiña, Karmele; Solé-Casals, Jordi

doi:10.1007/978-3-319-00846-2_140

Cited by 15 publications

(13 citation statements)

References 13 publications

(10 reference statements)

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“…For instance, handwriting skill degradation and Alzheimer's disease (AD) appear to be significantly correlated [17,18], and some aspects of handwriting can be good indicators for its diagnosis [6,17,18] or help to differentiate between mild Alzheimer's disease and mild cognitive impairment [17,18]. Additionally, the analysis of handwriting has proved useful to assess the effects of substances such as alcohol [19], marijuana [20] or caffeine [21].…”

Section: Online Drawing Applied To Health Analysismentioning

confidence: 99%

Selection of Entropy Based Features for Automatic Analysis of Essential Tremor

Ipiña

Solé-Casals

Faundez-Zanuy

et al. 2016

Entropy

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Biomedical systems produce biosignals that arise from interaction mechanisms. In a general form, those mechanisms occur across multiple scales, both spatial and temporal, and contain linear and non-linear information. In this framework, entropy measures are good candidates in order provide useful evidence about disorder in the system, lack of information in time-series and/or irregularity of the signals. The most common movement disorder is essential tremor (ET), which occurs 20 times more than Parkinson's disease. Interestingly, about 50%-70% of the cases of ET have a genetic origin. One of the most used standard tests for clinical diagnosis of ET is Archimedes' spiral drawing. This work focuses on the selection of non-linear biomarkers from such drawings and handwriting, and it is part of a wider cross study on the diagnosis of essential tremor, where our piece of research presents the selection of entropy features for early ET diagnosis. Classic entropy features are compared with features based on permutation entropy. Automatic analysis system settled on several Machine Learning paradigms is performed, while automatic features selection is implemented by means of ANOVA (analysis of variance) test. The obtained results for early detection are promising and appear applicable to real environments.

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Section: Online Drawing Applied To Health Analysismentioning

confidence: 99%

Selection of Entropy Based Features for Automatic Analysis of Essential Tremor

Ipiña

Solé-Casals

Faundez-Zanuy

et al. 2016

Entropy

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“…For example, some authors have examined information recovered by a graphical tablet, which provides up to 5 different parameters: azimuth angle, elevation angle, pressure, X-coordinates and Ycoordinates. Using these parameters, it is possible to design a classification system wherein the values of each parameter show the probability that an individual suffers from a specific cognitive impairment [49]. Online drawings performed by elderly people (control group) and AD patients were used in order to design a non-invasive system for dementia diagnosis.…”

Section: Cognitive Impairments As An Examplementioning

confidence: 99%

Population Aging in the European Information Societies: Towards a Comprehensive Research Agenda in eHealth Innovations for Elderly

Vancea¹,

Solé-Casals²

2016

Aging and disease

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Population ageing is one of the major social and economic challenges of our contemporary societies. With the advent of the information society, new research and technological developments have been promoted in the field of assistive technologies and information and communication technologies of benefit to elderly people. This article examines the potentialities of new informatics developments in generating solutions to better address elderly people's daily-life, especially those with chronic illness and/or low autonomy. The authours attempt to propose a research agenda, by exposing various strengts and weaknesses of eHealth innovations for elderly, mainly grounded in secondary sources analysis.

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“…Recent experimental results based on pen pressure and in-air movements appear to be significantly different for patient with early AD compared to healthy control (HC) individuals (Faundez-Zanuy et al, 2014). More recently, the diagnostic value of a tablet-based drawing task (i.e., drawing three-dimensional house) for the discrimination of patients with amnestic MCI (aMCI) or early dementia of Alzheimer-type (eDAT) from HC could be demonstrated (Müller et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Increased Diagnostic Accuracy of Digital vs. Conventional Clock Drawing Test for Discrimination of Patients in the Early Course of Alzheimer’s Disease from Cognitively Healthy Individuals

Müller

Preische

Heymann

et al. 2017

Front. Aging Neurosci.

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The conventional Clock Drawing Test (cCDT) is a rapid and inexpensive screening tool for detection of moderate and severe dementia. However, its usage is limited due to poor diagnostic accuracy especially in patients with mild cognitive impairment (MCI). The diagnostic value of a newly developed digital Clock Drawing Test (dCDT) was evaluated and compared with the cCDT in 20 patients with early dementia due to AD (eDAT), 30 patients with amnestic MCI (aMCI) and 20 cognitively healthy controls (HCs). Parameters assessed by dCDT were time while transitioning the stylus from one stroke to the next above the surface (i.e., time-in-air), time the stylus produced a visible stroke (i.e., time-on-surface) and total-time during clock drawing. Receiver-operating characteristic (ROC) curves were calculated and logistic regression analyses have been conducted for statistical analysis. Using dCDT, time-in-air was significantly increased in eDAT (70965.8 ms) compared to aMCI (54073.7 ms; p = 0.027) and HC (32315.6 ms; p < 0.001). In addition, time-in-air was significantly longer in patients with aMCI compared to HC (p = 0.003), even in the aMCI group with normal cCDT score (54141.8 ms; p < 0.001). Time-in-air using dCDT allowed discrimination of patients with aMCI from HCs with a sensitivity of 81.3% and a specificity of 72.2% while cCDT scoring revealed a sensitivity of 62.5% and a specificity of 83.3%. Most interestingly, time-in-air allowed even discrimination of aMCI patients with normal cCDT scores (80% from all aMCI patients) from HCs with a clinically relevant sensitivity of 80.8% and a specificity of 77.8%. A combination of dCDT variables and cCDT scores did not improve the discrimination of patients with aMCI from HC. In conclusion, assessment of time-in-air using dCDT yielded a higher diagnostic accuracy for discrimination of aMCI patients from HCs than the use of cCDT even in those aMCI patients with normal cCDT scores. Modern digitizing devices offer the opportunity to measure subtle changes of visuo-constructive demands and executive functions that may be used as a fast and easy to perform screening instrument for the early detection of cognitive impairment in primary care.

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Online Drawings for Dementia Diagnose: In-Air and Pressure Information Analysis

Cited by 15 publications

References 13 publications

Selection of Entropy Based Features for Automatic Analysis of Essential Tremor

Selection of Entropy Based Features for Automatic Analysis of Essential Tremor

Population Aging in the European Information Societies: Towards a Comprehensive Research Agenda in eHealth Innovations for Elderly

Increased Diagnostic Accuracy of Digital vs. Conventional Clock Drawing Test for Discrimination of Patients in the Early Course of Alzheimer’s Disease from Cognitively Healthy Individuals

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