Visual Representation of Online Handwriting Time Series for Deep Learning Parkinson's Disease Detection

Taleb, Catherine; Khachab, Maha; Mokbel, Chafic; Likforman-Sulem, Laurence

doi:10.1109/icdarw.2019.50111

Cited by 12 publications

(12 citation statements)

References 8 publications

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“…In 93 out of 209 studies (43.1%), original data were collected from human participants. In 108 studies (51.7%), data used were from public repositories and databases, including University of California at Irvine (UCI) Machine Learning Repository (Dua and Graff, 2018 ) ( n = 44), Parkinson's Progression Markers Initiative (Marek et al, 2011 ) (PPMI; n = 33), PhysioNet (Goldberger et al, 2000 ) ( n = 15), HandPD dataset (Pereira et al, 2015 ) ( n = 6), mPower database (Bot et al, 2016 ) ( n = 4), and 6 other databases (Mucha et al, 2018 ; Vlachostergiou et al, 2018 ; Bhati et al, 2019 ; Hsu et al, 2019 ; Taleb et al, 2019 ; Wodzinski et al, 2019 ; Table 2 ).…”

Section: Resultsmentioning

confidence: 99%

Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature

Mei

Desrosiers

Frasnelli

2021

Front. Aging Neurosci.

216

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Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and assessment of clinical signs, including the characterization of a variety of motor symptoms. However, traditional diagnostic approaches may suffer from subjectivity as they rely on the evaluation of movements that are sometimes subtle to human eyes and therefore difficult to classify, leading to possible misclassification. In the meantime, early non-motor symptoms of PD may be mild and can be caused by many other conditions. Therefore, these symptoms are often overlooked, making diagnosis of PD at an early stage challenging. To address these difficulties and to refine the diagnosis and assessment procedures of PD, machine learning methods have been implemented for the classification of PD and healthy controls or patients with similar clinical presentations (e.g., movement disorders or other Parkinsonian syndromes). To provide a comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of PD, in this study, we conducted a literature review of studies published until February 14, 2020, using the PubMed and IEEE Xplore databases. A total of 209 studies were included, extracted for relevant information and presented in this review, with an investigation of their aims, sources of data, types of data, machine learning methods and associated outcomes. These studies demonstrate a high potential for adaptation of machine learning methods and novel biomarkers in clinical decision making, leading to increasingly systematic, informed diagnosis of PD.

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Section: Resultsmentioning

confidence: 99%

Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature

Mei

Desrosiers

Frasnelli

2021

Front. Aging Neurosci.

216

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“…Recently, with the developments of new techniques such as convolutional neural network [ 101 ] and transfer learning [ 63 ], deep learning gained significant advances in the computer vision tasks, e.g., ImageNet [ 77 ]. Therefore, most of the studies used different imaging data to diagnose PD, such as MRI ( n = 12) [ 41 , 47 , 54 , 56 , 58 , 66 , 72 , 78 , 82 , 86 , 90 , 95 ] and handwritten images ( n = 9) [ 3 , 19 , 25 , 30 , 69 , 75 , 101 , 102 ], as well as PET and CT imaging ( n = 6) [ 28 , 59 , 67 , 71 , 88 , 90 ] and DaTscan imaging ( n = 4) [ 54 , 76 , 99 , 103 ]. However, CNN and transfer learning techniques were not limited to imaging data; they also learn complex features from voices and signal data [ 29 ].…”

Section: Resultsmentioning

confidence: 99%

“…In [ 38 , 83 ], we found that long short-term memory (LSTM) achieved outstanding results, indicating the best option to deal with EEG data. On the other hand, seven studies [ 3 , 19 , 25 , 27 , 40 , 69 , 101 , 102 ] focused on the classification of handwriting image to identify PD in the early stage, and we found that outstanding results were achieved in ANN + SVM in [ 3 ], dual-path RNN (DPRNN) in [ 40 ], and CNN + Optimum-Path Forest (OPF) in [ 102 ], respectively.…”

Section: Discussionmentioning

confidence: 99%

The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

et al. 2021

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Background: Parkinson’s Disease (PD) is a chronic neurodegenerative disorder that has been ranked second after Alzheimer’s disease worldwide. Early diagnosis of PD is crucial to combat against PD to allow patients to deal with it properly. However, there is no medical test(s) available to diagnose PD conclusively. Therefore, computer-aided diagnosis (CAD) systems offered a better solution to make the necessary data-driven decisions and assist the physician. Numerous studies were conducted to propose CAD to diagnose PD in the early stages. No comprehensive reviews have been conducted to summarize the role of AI tools to combat PD. Objective: The study aimed to explore and summarize the applications of neural networks to diagnose PD. Methods: PRISMA Extension for Scoping Reviews (PRISMA-ScR) was followed to conduct this scoping review. To identify the relevant studies, both medical databases (e.g., PubMed) and technical databases (IEEE) were searched. Three reviewers carried out the study selection and extracted the data from the included studies independently. Then, the narrative approach was adopted to synthesis the extracted data. Results: Out of 1061 studies, 91 studies satisfied the eligibility criteria in this review. About half of the included studies have implemented artificial neural networks to diagnose PD. Numerous studies included focused on the freezing of gait (FoG). Biomedical voice and signal datasets were the most commonly used data types to develop and validate these models. However, MRI- and CT-scan images were also utilized in the included studies. Conclusion: Neural networks play an integral and substantial role in combating PD. Many possible applications of neural networks were identified in this review, however, most of them are limited up to research purposes.

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“…As per Taleb [21], the creator uses deep learning procedure for programmed classification of PD. Proposed 2 Profound based learning models for end to-end time plan grouping: the CNN and the CNN-BLSTM.…”

Section: Zhang Et Al (2020)mentioning

confidence: 99%

Design and Development of Efficient Model to Predict Neurodegenerative Disorders Using Advanced LSTM: A Review of Literature

Devi

2022

IJCSBE

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Purpose: A continuous Nervous system illness that influences development is known as neurodegenerative sickness. Side effects show up continuously, and may start with a scarcely distinguishable quake in just a single hand. Quakes are normal, but they are frequently joined by firmness or eased back versatility. The emphasis is on Parkinson's infection specifically (PD). The signs and results of Parkinson's contamination shift starting with one individual then onto the next. Early reprimand markers could be subtle and go unnoticed. Regardless, when aftereffects start to influence the different sides of your body, secondary effects normally jump on single side of your body and decay on that side. Parkinson's illness is brought about by the demise of nerve cells in the substantia nigra, a portion of the cerebrum. The exactness of a few fake brain network approaches, for example, Convolutional Neural Network, Recurrent Neural Network, Long-Short term Memory network have been concentrated on to analyze Parkinson's illnesses. This record inspects a synopsis of a portion of the examination and concentrates on that have been directed in the field of clinical diagnostics. In light of the audit, research holes are featured, as well as examination needs for future review. Approach: A thorough study on the algorithms used in analysis of handwritten and vocal to distinguish and anticipate Parkinson's illness. Findings: The review showed that the majority of the AI and deep learning strategy can order neurodegenerative illness in view of vocal, transcribed and walk investigation in light of the clinical datasets. The new crossover philosophy proposed will be more exact as the model will actually want to foresee and distinguish neurodegenerative sickness in view of eye development. Originality: The sort of information expected for forecast and discovery framework are considered and the design and portrayal outline of a proposed model are incorporated. Paper Type: Literature Review.

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Visual Representation of Online Handwriting Time Series for Deep Learning Parkinson's Disease Detection

Cited by 12 publications

References 8 publications

Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature

Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature

The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

Design and Development of Efficient Model to Predict Neurodegenerative Disorders Using Advanced LSTM: A Review of Literature

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