Handwritten pattern recognition for early Parkinson’s disease diagnosis

Bernardo, Lucas Salvador; Quezada, Ángeles; Castro-Muñoz, Roberto; Maia, F. F. S.; Pereira, Clayton Reginaldo; Wu, Wanqing; Albuquerque, Victor Hugo C. de

doi:10.1016/j.patrec.2019.04.003

Cited by 47 publications

(22 citation statements)

References 12 publications

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“…( 2020 ); Bernardo et al. ( 2019 )). However, images-based diagnostics are considered very costly considering that routine blood tests are relatively easy to run and widely used at admission in several clinical centers (Ferrari et al.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Simple hemogram to support the decision-making of COVID-19 diagnosis using clusters analysis with self-organizing maps neural network

et al. 2021

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The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which is related to new coronavirus disease (COVID-19) has mobilized several scientifics to explore clinical data using soft-computing approaches. In the context of machine learning, previous studies have explored supervised algorithms to predict and support diagnosis based on several clinical parameters from patients diagnosed with and without COVID-19. However, in most of them the decision is based on a "black-box" method, making it impossible to discover the variable relevance in decision making. Hence, in this study, we introduce a non-supervised clustering analysis with neural network self-organizing maps (SOM) as a strategy of decision-making. We propose to identify potential variables in routine blood tests that can support clinician decision-making during COVID-19 diagnosis at hospital admission, facilitating rapid medical intervention. Based on SOM features (visual relationships between clusters and identification of patterns and behaviors), and using linear discriminant analysis , it was possible to detect a group of units of the map with a discrimination power around 83% to SARS-CoV-2-positive patients. In addition, we identified some variables in admission blood tests (Leukocytes, Basophils, Eosinophils, and Red cell Distribution Width) that, in combination had strong influence in the clustering performance, which could assist a possible clinical decision. Thus, although with limitations, we believe that SOM can be used as a soft-computing approach to support clinician decision-making in the context of COVID-19. Keywords Covid-19 diagnostic • SARS-CoV-2 • Self-organizing maps Communicated by Victor Hugo C. de Albuquerque.

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Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Simple hemogram to support the decision-making of COVID-19 diagnosis using clusters analysis with self-organizing maps neural network

et al. 2021

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“…The Archimedean spiral contour was experimentally examined to be an indicator of detecting early signs in PD [33]. In this context, the PD and HD tremors are related such that T4 is adaptable as a significant measurement method of a tremor impairment state for the HD patients.…”

Section: T4: Archimedean Spiralmentioning

confidence: 99%

A Mobile Application for Smart Computer-Aided Self-Administered Testing of Cognition, Speech, and Motor Impairment

Lauraitis

Maskeliūnas

Damaševičius

et al. 2020

Sensors

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We present a model for digital neural impairment screening and self-assessment, which can evaluate cognitive and motor deficits for patients with symptoms of central nervous system (CNS) disorders, such as mild cognitive impairment (MCI), Parkinson’s disease (PD), Huntington’s disease (HD), or dementia. The data was collected with an Android mobile application that can track cognitive, hand tremor, energy expenditure, and speech features of subjects. We extracted 238 features as the model inputs using 16 tasks, 12 of them were based on a self-administered cognitive testing (SAGE) methodology and others used finger tapping and voice features acquired from the sensors of a smart mobile device (smartphone or tablet). Fifteen subjects were involved in the investigation: 7 patients with neurological disorders (1 with Parkinson’s disease, 3 with Huntington’s disease, 1 with early dementia, 1 with cerebral palsy, 1 post-stroke) and 8 healthy subjects. The finger tapping, SAGE, energy expenditure, and speech analysis features were used for neural impairment evaluations. The best results were achieved using a fusion of 13 classifiers for combined finger tapping and SAGE features (96.12% accuracy), and using bidirectional long short-term memory (BiLSTM) (94.29% accuracy) for speech analysis features.

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“…More recently in 2019, a software was developed for analyzing images based on drawings assessed by PD and healthy individuals acquired from similar datasets like the one used in this work. It correctly predicted over 96% of the total subjects using ML techniques [ 20 ]. Another case of successful use of ML and signal analysis for the detection of PD was done by Jefferson S. Almeida using sustained phonation obtained from speech tasks.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Sensory Pen for Aiding in the Diagnosis of Parkinson’s Disease from Dynamic Handwriting Analysis

Júnior

Delmiro

Magaia

et al. 2020

Sensors

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In this paper, we propose a pen device capable of detecting specific features from dynamic handwriting tests for aiding on automatic Parkinson’s disease identification. The method used in this work uses machine learning to compare the raw signals from different sensors in the device coupled to a pen and extract relevant information such as tremors and hand acceleration to diagnose the patient clinically. Additionally, the datasets composed of raw signals from healthy and Parkinson’s disease patients acquired here are made available to further contribute to research related to this topic.

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Handwritten pattern recognition for early Parkinson’s disease diagnosis

Cited by 47 publications

References 12 publications

RETRACTED ARTICLE: Simple hemogram to support the decision-making of COVID-19 diagnosis using clusters analysis with self-organizing maps neural network

RETRACTED ARTICLE: Simple hemogram to support the decision-making of COVID-19 diagnosis using clusters analysis with self-organizing maps neural network

A Mobile Application for Smart Computer-Aided Self-Administered Testing of Cognition, Speech, and Motor Impairment

Intelligent Sensory Pen for Aiding in the Diagnosis of Parkinson’s Disease from Dynamic Handwriting Analysis

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