scholarly journals GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson’s Disease Detection Using EEG Signals

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1740
Author(s):  
Hui Wen Loh ◽  
Chui Ping Ooi ◽  
Elizabeth Palmer ◽  
Prabal Datta Barua ◽  
Sengul Dogan ◽  
...  
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Clinical Decision ◽  
Healthy Controls ◽  
Noninvasive Methods ◽  
Proposed Model ◽  
Tenfold Cross Validation ◽  
Eeg Recordings ◽  
Medication Status

Parkinson’s disease (PD) is globally the most common neurodegenerative movement disorder. It is characterized by a loss of dopaminergic neurons in the substantia nigra of the brain. However, current methods to diagnose PD on the basis of clinical features of Parkinsonism may lead to misdiagnoses. Hence, noninvasive methods such as electroencephalographic (EEG) recordings of PD patients can be an alternative biomarker. In this study, a deep-learning model is proposed for automated PD diagnosis. EEG recordings of 16 healthy controls and 15 PD patients were used for analysis. Using Gabor transform, EEG recordings were converted into spectrograms, which were used to train the proposed two-dimensional convolutional neural network (2D-CNN) model. As a result, the proposed model achieved high classification accuracy of 99.46% (±0.73) for 3-class classification (healthy controls, and PD patients with and without medication) using tenfold cross-validation. This indicates the potential of proposed model to simultaneously automatically detect PD patients and their medication status. The proposed model is ready to be validated with a larger database before implementation as a computer-aided diagnostic (CAD) tool for clinical-decision support.

scholarly journals Quantitative Electroencephalography as a Biomarker for Cognitive Dysfunction in Parkinson’s Disease

2022 ◽  
Vol 13 ◽  
Author(s):  
Kevin Novak ◽  
Bruce A. Chase ◽  
Jaishree Narayanan ◽  
Premananda Indic ◽  
Katerina Markopoulou
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Principal Component ◽  
Disease Stage ◽  
Positive Contribution ◽  
Healthy Controls ◽  
Quantitative Electroencephalography ◽  
Motor Score ◽  
Eeg Recordings

Background: Quantitative electroencephalography (qEEG) has been suggested as a biomarker for cognitive decline in Parkinson’s disease (PD).Objective: Determine if applying a wavelet-based qEEG algorithm to 21-electrode, resting-state EEG recordings obtained in a routine clinical setting has utility for predicting cognitive impairment in PD.Methods: PD subjects, evaluated by disease stage and motor score, were compared to healthy controls (N = 20 each). PD subjects with normal (PDN, MoCA 26–30, N = 6) and impaired (PDD, MoCA ≤ 25, N = 14) cognition were compared. The wavelet-transform based time-frequency algorithm assessed the instantaneous predominant frequency (IPF) at 60 ms intervals throughout entire recordings. We then determined the relative time spent by the IPF in the four standard EEG frequency bands (RTF) at each scalp location. The resting occipital rhythm (ROR) was assessed using standard power spectral analysis.Results: Comparing PD subjects to healthy controls, mean values are decreased for ROR and RTF-Beta, greater for RTF-Theta and similar for RTF-Delta and RTF-Alpha. In logistic regression models, arithmetic combinations of RTF values [e.g., (RTF-Alpha) + (RTF-Beta)/(RTF-Delta + RTF-Theta)] and RTF-Alpha values at occipital or parietal locations are most able to discriminate between PD and controls. A principal component (PC) from principal component analysis (PCA) using RTF-band values in all subjects is associated with PD status (p = 0.004, β = 0.31, AUC = 0.780). Its loadings show positive contribution from RTF-Theta at all scalp locations, and negative contributions from RTF-Beta at occipital, parietal, central, and temporal locations. Compared to cognitively normal PD subjects, cognitively impaired PD subjects have lower median RTF-Alpha and RTF-Beta values, greater RTF-Theta values and similar RTF-Delta values. A PC from PCA using RTF-band values in PD subjects is associated with cognitive status (p = 0.002, β = 0.922, AUC = 0.89). Its loadings show positive contributions from RTF-Theta at all scalp locations, negative contributions from RTF-Beta at central locations, and negative contributions from RTF-Delta at central, frontal and temporal locations. Age, disease duration and/or sex are not significant covariates. No PC was associated with motor score or disease stage.Significance: Analyzing standard EEG recordings obtained in a community practice setting using a wavelet-based qEEG algorithm shows promise as a PD biomarker and for predicting cognitive impairment in PD.

scholarly journals Parkinson’s disease diagnosis using convolutional neural networks and figure-copying tasks

2021 ◽  
Author(s):  
Mohamad Alissa ◽  
Michael A. Lones ◽  
Jeremy Cosgrove ◽  
Jane E. Alty ◽  
Stuart Jamieson ◽  
...  
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Network Architecture ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Disease Diagnosis ◽  
Healthy Controls ◽  
Abnormal Movements ◽  
Drawing Tasks

AbstractParkinson’s disease (PD) is a progressive neurodegenerative disorder that causes abnormal movements and an array of other symptoms. An accurate PD diagnosis can be a challenging task as the signs and symptoms, particularly at an early stage, can be similar to other medical conditions or the physiological changes of normal ageing. This work aims to contribute to the PD diagnosis process by using a convolutional neural network, a type of deep neural network architecture, to differentiate between healthy controls and PD patients. Our approach focuses on discovering deviations in patient’s movements with the use of drawing tasks. In addition, this work explores which of two drawing tasks, wire cube or spiral pentagon, are more effective in the discrimination process. With $$93.5\%$$ 93.5 % accuracy, our convolutional classifier, trained with images of the pentagon drawing task and augmentation techniques, can be used as an objective method to discriminate PD from healthy controls. Our compact model has the potential to be developed into an offline real-time automated single-task diagnostic tool, which can be easily deployed within a clinical setting.

scholarly journals Global efficiency of the motor network is decreased in Parkinson's disease in comparison with essential tremor and healthy controls

2021 ◽  
Author(s):  
Natalia Pelizari Novaes ◽  
Joana Bisol Balardin ◽  
Fabiana Campos Hirata ◽  
Luciano Melo ◽  
Edson Amaro ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Essential Tremor ◽  
Healthy Controls ◽  
Global Efficiency ◽  
Motor Network

Vestibular semicircular canal function as detected by video Head Impulse Test (vHIT) is essentially unchanged in people with Parkinson’s disease compared to healthy controls

2021 ◽  
pp. 1-9
Author(s):  
Kim E. Hawkins ◽  
Elodie Chiarovano ◽  
Serene S. Paul ◽  
Ann M Burgess ◽  
Hamish G. MacDougall ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Semicircular Canal ◽  
Neurodegenerative Disorder ◽  
Vestibular Function ◽  
Head Impulse Test ◽  
Healthy Controls ◽  
Video Head Impulse Test ◽  
Head Impulse ◽  
Vestibulo Ocular Reflex

BACKGROUND: Parkinson’s disease (PD) is a common multi-system neurodegenerative disorder with possible vestibular system dysfunction, but prior vestibular function test findings are equivocal. OBJECTIVE: To report and compare vestibulo-ocular reflex (VOR) gain as measured by the video head impulse test (vHIT) in participants with PD, including tremor dominant and postural instability/gait dysfunction phenotypes, with healthy controls (HC). METHODS: Forty participants with PD and 40 age- and gender-matched HC had their vestibular function assessed. Lateral and vertical semicircular canal VOR gains were measured with vHIT. VOR canal gains between PD participants and HC were compared with independent samples t-tests. Two distinct PD phenotypes were compared to HC using Tukey’s ANOVA. The relationship of VOR gain with PD duration, phenotype, severity and age were investigated using logistic regression. RESULTS: There were no significant differences between groups in vHIT VOR gain for lateral or vertical canals. There was no evidence of an effect of PD severity, phenotype or age on VOR gains in the PD group. CONCLUSION: The impulsive angular VOR pathways are not significantly affected by the pathophysiological changes associated with mild to moderate PD.

scholarly journals Oral Factors That Impact the Oral Microbiota in Parkinson’s Disease

2021 ◽  
Vol 9 (8) ◽  
pp. 1616
Author(s):  
Natalia S. Rozas ◽  
Gena D. Tribble ◽  
Cameron B. Jeter
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Oral Health ◽  
Soft Tissue ◽  
Oral Hygiene ◽  
Beta Diversity ◽  
Oral Microbiota ◽  
Healthy Controls ◽  
Microbiota Composition ◽  
Salivary Ph

Patients with Parkinson’s disease (PD) are at increased risk of aspiration pneumonia, their primary cause of death. Their oral microbiota differs from healthy controls, exacerbating this risk. Our goal was to explore if poor oral health, poor oral hygiene, and dysphagia status affect the oral microbiota composition of these patients. In this cross-sectional case-control study, the oral microbiota from hard and soft tissues of patients with PD (n = 30) and age-, gender-, and education-matched healthy controls (n = 30) was compared using 16S rRNA gene sequencing for bacterial identification. Study participants completed dietary, oral hygiene, drooling, and dysphagia questionnaires, and an oral health screening. Significant differences in soft tissue beta-diversity (p < 0.005) were found, and a higher abundance of opportunistic oral pathogens was detected in patients with PD. Factors that significantly influenced soft tissue beta-diversity and microbiota composition include dysphagia, drooling (both p < 0.05), and salivary pH (p < 0.005). Thus, patients with PD show significant differences in their oral microbiota compared to the controls, which may be due, in part, to dysphagia, drooling, and salivary pH. Understanding factors that alter their oral microbiota could lead to the development of diagnostic and treatment strategies that improve the quality of life and survivability of these patients.

Development of standardized regression-based formulas to assess meaningful cognitive change in early Parkinson’s disease

2020 ◽  
Author(s):  
Hannah L Combs ◽  
Kate A Wyman-Chick ◽  
Lauren O Erickson ◽  
Michele K York
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Test Scores ◽  
Prediction Models ◽  
Early Stage ◽  
Cognitive Change ◽  
Cognitive Test ◽  
Healthy Controls ◽  
Change Over Time ◽  
Over Time

Abstract Objective Longitudinal assessment of cognitive and emotional functioning in patients with Parkinson’s disease (PD) is helpful in tracking progression of the disease, developing treatment plans, evaluating outcomes, and educating patients and families. Determining whether change over time is meaningful in neurodegenerative conditions, such as PD, can be difficult as repeat assessment of neuropsychological functioning is impacted by factors outside of cognitive change. Regression-based prediction formulas are one method by which clinicians and researchers can determine whether an observed change is meaningful. The purpose of the current study was to develop and validate regression-based prediction models of cognitive and emotional test scores for participants with early-stage idiopathic PD and healthy controls (HC) enrolled in the Parkinson’s Progression Markers Initiative (PPMI). Methods Participants with de novo PD and HC were identified retrospectively from the PPMI archival database. Data from baseline testing and 12-month follow-up were utilized in this study. In total, 688 total participants were included in the present study (NPD = 508; NHC = 185). Subjects from both groups were randomly divided into development (70%) and validation (30%) subsets. Results Early-stage idiopathic PD patients and healthy controls were similar at baseline. Regression-based models were developed for all cognitive and self-report mood measures within both populations. Within the validation subset, the predicted and observed cognitive test scores did not significantly differ, except for semantic fluency. Conclusions The prediction models can serve as useful tools for researchers and clinicians to study clinically meaningful cognitive and mood change over time in PD.

scholarly journals High-throughput behavioral screen in C. elegans reveals Parkinson’s disease drug candidates

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Salman Sohrabi ◽  
Danielle E. Mor ◽  
Rachel Kaletsky ◽  
William Keyes ◽  
Coleen T. Murphy
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
High Throughput ◽  
Potential Candidate ◽  
Automated Scoring ◽  
C Elegans ◽  
Drug Candidates ◽  
Branched Chain ◽  
Approved Drugs

AbstractWe recently linked branched-chain amino acid transferase 1 (BCAT1) dysfunction with the movement disorder Parkinson’s disease (PD), and found that RNAi-mediated knockdown of neuronal bcat-1 in C. elegans causes abnormal spasm-like ‘curling’ behavior with age. Here we report the development of a machine learning-based workflow and its application to the discovery of potentially new therapeutics for PD. In addition to simplifying quantification and maintaining a low data overhead, our simple segment-train-quantify platform enables fully automated scoring of image stills upon training of a convolutional neural network. We have trained a highly reliable neural network for the detection and classification of worm postures in order to carry out high-throughput curling analysis without the need for user intervention or post-inspection. In a proof-of-concept screen of 50 FDA-approved drugs, enasidenib, ethosuximide, metformin, and nitisinone were identified as candidates for potential late-in-life intervention in PD. These findings point to the utility of our high-throughput platform for automated scoring of worm postures and in particular, the discovery of potential candidate treatments for PD.

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