Handwritten dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification

Pereira, Cassiano Costa Silva; Pereira, Danilo Florentino; Rosa, Guilherme Jordão de Magalhães; Albuquerque, Victor Hugo C. de; Weber, Silke Anna Theresa; Hook, Christian; Papa, João Paulo

doi:10.1016/j.artmed.2018.04.001

Cited by 154 publications

(70 citation statements)

References 31 publications

(42 reference statements)

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“…In both studies, time series data was used. In comparison to Pereira et al [35][36][37], our system showed consider gain in performance i.e. 80.19% [35], 90.38% [36], and 93.50 [37] to 98.28%.…”

Section: Discussioncontrasting

confidence: 65%

“…Handwriting dataset has been used for detection of Parkinson's disease and provided effective results on BA approach with over all accuracy of 90.38% for spiral data. In 2018, Pereira et al [37] combined six representations ( spirals, drawing of circles on the page and in air, meanders, left-wrist and right-wrist movements) and extracted features by deploying CNN using pen based features (time series) from online images and reported about 93.50% accuracy. In both studies, time series data was used.…”

Section: Discussionmentioning

confidence: 99%

“…CNN gave the effective results on BA approach with over all accuracy of 90.38% for spiral data. Pereira et al map signals extracted from handwriting dynamics into images.These time series images passed to Convolutional neural network and achieved 93.50% accuracy using feature learned from CCN [37]. Zhang et al [38] applied stacked autoencoders and KNN classifier and employed the speech records and extract the time frequency features such as jitter , shimmer , voice pitch etc and got an accuracy 90.53 % .…”

Section: Related Workmentioning

confidence: 99%

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RETRACTED ARTICLE: Refining Parkinson’s neurological disorder identification through deep transfer learning

Naseer¹,

Rani²,

Naz³

et al. 2019

Neural Comput & Applic

131

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Parkinson's disease (PD), a multi-system neurodegenerative disorder which affects the brain slowly, is characterized by symptoms such as muscle stiffness, tremor in the limbs and impaired balance, all of which tend to worsen with the passage of time. Available treatments target its symptoms, aiming to improve the quality of life, however, automatic diagnosis at early stages is still a challenging medicine-related task to date, since a patient may have an identical behavior to that of a healthy individual at the very early stage of the disease. Parkinson's disease detection through handwriting data is a significant classification problem for identification of PD at the infancy stage. In this paper, a PD identification is realized with help of handwriting images that helps as one of the earliest indicators for PD. For this purpose, we proposed a deep convolutional neural network classifier with transfer learning and data augmentation techniques to improve the identification. Two approaches like freeze and fine-tuning of transfer learning are investigated using ImageNet and MNIST dataset as source task independently. A trained network achieved 98.28% accuracy using fine-tuning based approach using ImageNet and PaHaW dataset. Experimental results on benchmark dataset reveal that proposed approach provides better detection of Parkinson's disease as compared to state of the art work.

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Section: Discussioncontrasting

confidence: 65%

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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RETRACTED ARTICLE: Refining Parkinson’s neurological disorder identification through deep transfer learning

Naseer¹,

Rani²,

Naz³

et al. 2019

Neural Comput & Applic

131

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“…To improve the diagnosis of Parkinson's disease, [17] introduced an improved and optimized version of the Crow Search Algorithm. Convolutional Neural Networks (CNN) were applied to hand-written exams to investigate their performance of PD detection, achieving 95% of accuracy [18].…”

Section: Related Workmentioning

confidence: 99%

Detecting Parkinson’s disease with sustained phonation and speech signals using machine learning techniques

Almeida

Filho

Carneiro

et al. 2019

Pattern Recognition Letters

190

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This study investigates the processing of voice signals for detecting Parkinson's disease. This disease is one of the neurological disorders that affect people in the world most. The approach evaluates the use of eighteen feature extraction techniques and four machine learning methods to classify data obtained from sustained phonation and speech tasks. Phonation relates to the vowel /a/ voicing task and speech to the pronunciation of a short sentence in Lithuanian language. The audio tasks were recorded using two microphone channels from acoustic cardioid (AC) and a smartphone (SP), thus allowing to evaluate the performance for different types of microphones. Five metrics were employed to analyze the classification performance: Equal Error Rate (EER) and Area Under Curve (AUC) measures from Detection Error Tradeoff (DET) and Receiver Operating Characteristic curves, Accuracy, Specificity, and Sensitivity. We compare this approach with other approaches that use the same data set. We show that the task of phonation was more efficient than speech tasks in the detection of disease. The best performance for the AC channel achieved an accuracy of 94.55%, AUC 0.87, and EER 19.01%. When using the SP channel, we have achieved an accuracy of 92.94%, AUC 0.92, and EER 14.15%.

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“…The abovementioned work was extended by the authors in References [52,90]. In Reference [90], CNNs were used to learn features directly from time-series-based images. The main hypothesis was that texture-oriented features are able to encode the tremors during handwriting.…”

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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Handwritten dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification

Abstract: The analysis of handwritten dynamics using deep learning techniques showed to be useful for automatic Parkinson's disease identification, as well as it can outperform handcrafted features.

Cited by 154 publications

References 31 publications

RETRACTED ARTICLE: Refining Parkinson’s neurological disorder identification through deep transfer learning

RETRACTED ARTICLE: Refining Parkinson’s neurological disorder identification through deep transfer learning

Detecting Parkinson’s disease with sustained phonation and speech signals using machine learning techniques

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

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