Machine learning-based classification of simple drawing movements in Parkinson's disease

Kotsavasiloglou, C.; Kostikis, Nikolaos; Hristu-Varsakelis, Dimitrios; Arnaoutoglou, Marianthi

doi:10.1016/j.bspc.2016.08.003

Cited by 142 publications

(67 citation statements)

References 20 publications

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“…Using repetitive cursive loops and kinematic features for evaluation, Haremans et al found a correlation of r = −0.40 between the handwriting measurements and the medical scales considering a corpus with 30 PD/15 HC [9]. From a population of 24 PD/20 HC, Kotsavasiloglou et al report classification results of 91% using kinematic features and entropy analysis from drawings of horizontal lines [10]. Finally, Taleb et al report results of 96.87% when classifying between PD patients and HC subjects.…”

Section: Introductionmentioning

confidence: 99%

Characterization of the Handwriting Skills as a Biomarker for Parkinson’s Disease

Castrillon

Acien

Orozco-Arroyave

et al. 2019

2019 14th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2019)

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In this paper we evaluate the suitability of handwriting patterns as potential biomarkers to model Parkinson's disease (PD). Although the study of PD is attracting the interest of many researchers around the world, databases to evaluate handwriting patterns are scarce and knowledge about patterns associated to PD is limited and biased to the existing datasets. This paper introduces a database with a total of 935 handwriting tasks collected from 55 PD patients and 94 healthy controls (45 young and 49 old). Three feature sets are extracted from the signals: neuromotor, kinematic, and nonlinear dynamic. Different classifiers are used to discriminate between PD and healthy subjects: support vector machines, knearest neighbors, and a multilayer perceptron. The proposed features and classifiers enable to detect PD with accuracies between 81% and 97%. Additionally, new insights are presented on the utility of the studied features for monitoring and detecting PD.

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Section: Introductionmentioning

confidence: 99%

Characterization of the Handwriting Skills as a Biomarker for Parkinson’s Disease

Castrillon

Acien

Orozco-Arroyave

et al. 2019

2019 14th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2019)

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“…To address this, we have modified the way we feed the training examples to the network. In fact, instead of normally feeding PHTNet with m t t ( : ) 1 and calculating the MSE value between ŷ t t ( : ) 1 and m t t…”

Section: Data Preparationmentioning

confidence: 99%

PHTNet: Characterization and Deep Mining of Involuntary Pathological Hand Tremor using Recurrent Neural Network Models

Shahtalebi

Atashzar

Samotus

et al. 2020

Sci Rep

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the global aging phenomenon has increased the number of individuals with age-related neurological movement disorders including parkinson's Disease (pD) and essential tremor (et). pathological Hand tremor (pHt), which is considered among the most common motor symptoms of such disorders, can severely affect patients' independence and quality of life. To develop advanced rehabilitation and assistive technologies, accurate estimation/prediction of nonstationary pHt is critical, however, the required level of accuracy has not yet been achieved. The lack of sizable datasets and generalizable modeling techniques that can fully represent the spectrotemporal characteristics of PHT have been a critical bottleneck in attaining this goal. this paper addresses this unmet need through establishing a deep recurrent model to predict and eliminate the PHT component of hand motion. More specifically, we propose a machine learning-based, assumption-free, and real-time pHt elimination framework, the pHtnet, by incorporating deep bidirectional recurrent neural networks. the pHtnet is developed over a hand motion dataset of 81 ET and PD patients collected systematically in a movement disorders clinic over 3 years. The PHTNet is the first intelligent systems model developed on this scale for pHt elimination that maximizes the resolution of estimation and allows for prediction of future and upcoming sub-movements.Age-related neurological movement disorders such as Parkinson's Disease (PD) and Essential tremor (ET) 1-4 are expected to become more prevalent as the population of seniors over the age of sixty is expected to increase from 962 million in 2017 to 2.1 billion by 2050, and to 3.1 billion in 2100 5 . Pathological Hand Tremor (PHT) is a common upper-limb motor symptom of several age-related neurological movement disorders and is described as involuntary and pseudo-rhythmic movements 6 affecting coordination, targeting, and speed of intended motions 7 .Unlike physiological tremor, which is identified with low amplitude vibrations occurring within the spectral range of 6 to 14 Hz 8 and affects the performance of individuals in high precision tasks such as robotic surgery 9 , PHT represents higher amplitude motion occurring in the broader frequency range of 3-14 Hz 10 . The repetitive and oscillating nature of PHT differentiates itself from other involuntary movement disorders such as chorea, athetosis, ballism, tics, and myoclonus 11 . Upper-limb tremor significantly limits individuals in performing Activities of Daily Livings (ADLs) 12 . Thus, during the last decade, several techniques and technologies have been proposed in both rehabilitation and assistive domains 13,14 to compensate for the involuntary movement while promoting the voluntary component of motion. The accuracy of a tremor compensation technology (such as sophisticated wearable exosuits) relies significantly on the efficacy and spectrotemporal resolution of the algorithm, as inaccurate or slow extraction techniques do not allow for proper compensation. PHT consists o...

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“…For each of the five scales, machine learning models were built and tested by utilizing the principal components of the features as predictors and mean ratings of the three specialists as target variables. In [12], the authors demonstrated that handwriting markers could be used for PD symptoms profiling successfully. They defined a novel metric as the normalized velocity variability.…”

Section: Related Workmentioning

confidence: 99%

A Study on the Diagnosis of Parkinson’s Disease using Digitized Wacom Graphics Tablet Dataset

Akyol¹

2017

IJITCS

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Abstract-ParkinsonDisease is a neurological disorder, which is one of the most painful, dangerous and noncurable diseases, which occurs at older ages. The Static Spiral Test, Dynamic Spiral Test and Stability Test on Certain Point records were used in the application which was developed for the diagnosis of this disease. These datasets were divided into 80-20% training and testing data respectively within the framework of 10-fold cross validation technique. Training data as the input data were sent to the Random Forest, Logistic Regression and Artificial Neural Networks classifier algorithms. After this step, performances of these classifier algorithms were evaluated on testing data. Also, new data analysis was carried out. According to the results obtained, Artificial Neural Networks is more successful than Random Forest and Logistic Regression algorithms in analysis of new data.

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Machine learning-based classification of simple drawing movements in Parkinson's disease

Cited by 142 publications

References 20 publications

Characterization of the Handwriting Skills as a Biomarker for Parkinson’s Disease

Characterization of the Handwriting Skills as a Biomarker for Parkinson’s Disease

PHTNet: Characterization and Deep Mining of Involuntary Pathological Hand Tremor using Recurrent Neural Network Models

A Study on the Diagnosis of Parkinson’s Disease using Digitized Wacom Graphics Tablet Dataset

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