scholarly journals Assessing Parkinson’s Disease at Scale Using Telephone-Recorded Speech: Insights from the Parkinson’s Voice Initiative

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1892
Author(s):  
Siddharth Arora ◽  
Athanasios Tsanas
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Screening Tool ◽  
Population Based ◽  
Remote Detection ◽  
Support Vector ◽  
Support Tool ◽  
Specialized Equipment ◽  
Fold Cross Validation ◽  
Diagnostic Support

Numerous studies have reported on the high accuracy of using voice tasks for the remote detection and monitoring of Parkinson’s Disease (PD). Most of these studies, however, report findings on a small number of voice recordings, often collected under acoustically controlled conditions, and therefore cannot scale at large without specialized equipment. In this study, we aimed to evaluate the potential of using voice as a population-based PD screening tool in resource-constrained settings. Using the standard telephone network, we processed 11,942 sustained vowel /a/ phonations from a US-English cohort comprising 1078 PD and 5453 control participants. We characterized each phonation using 304 dysphonia measures to quantify a range of vocal impairments. Given that this is a highly unbalanced problem, we used the following strategy: we selected a balanced subset (n = 3000 samples) for training and testing using 10-fold cross-validation (CV), and the remaining (unbalanced held-out dataset, n = 8942) samples for further model validation. Using robust feature selection methods we selected 27 dysphonia measures to present into a radial-basis-function support vector machine and demonstrated differentiation of PD participants from controls with 67.43% sensitivity and 67.25% specificity. These findings could help pave the way forward toward the development of an inexpensive, remote, and reliable diagnostic support tool for PD using voice as a digital biomarker.

scholarly journals A Wearable System to Objectify Assessment of Motor Tasks for Supporting Parkinson’s Disease Diagnosis

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2630 ◽  
Author(s):  
Erika Rovini ◽  
Carlo Maremmani ◽  
Filippo Cavallo
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Objective Assessment ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Motor Tasks ◽  
Wearable System ◽  
Support Tool ◽  
Average Accuracy ◽  
Open Issue

Objective assessment of the motor evaluation test for Parkinson’s disease (PD) diagnosis is an open issue both for clinical and technical experts since it could improve current clinical practice with benefits both for patients and healthcare systems. In this work, a wearable system composed of four inertial devices (two SensHand and two SensFoot), and related processing algorithms for extracting parameters from limbs motion was tested on 40 healthy subjects and 40 PD patients. Seventy-eight and 96 kinematic parameters were measured from lower and upper limbs, respectively. Statistical and correlation analysis allowed to define four datasets that were used to train and test five supervised learning classifiers. Excellent discrimination between the two groups was obtained with all the classifiers (average accuracy ranging from 0.936 to 0.960) and all the datasets (average accuracy ranging from 0.953 to 0.966), over three conditions that included parameters derived from lower, upper or all limbs. The best performances (accuracy = 1.00) were obtained when classifying all the limbs with linear support vector machine (SVM) or gaussian SVM. Even if further studies should be done, the current results are strongly promising to improve this system as a support tool for clinicians in objectifying PD diagnosis and monitoring.

scholarly journals Using Artificial Neural Network to Discriminate Parkinson’s Disease from Other Parkinsonisms by Focusing on Putamen of Dopamine Transporter SPECT Images

Biomedicines ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 12
Author(s):  
Chung-Yao Chien ◽  
Szu-Wei Hsu ◽  
Tsung-Lin Lee ◽  
Pi-Shan Sung ◽  
Chou-Ching Lin
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Parkinson's Disease ◽  
Dopamine Transporter ◽  
Support Vector ◽  
Dat Spect ◽  
Ann Classifier ◽  
Spect Images

Background: The challenge of differentiating, at an early stage, Parkinson’s disease from parkinsonism caused by other disorders remains unsolved. We proposed using an artificial neural network (ANN) to process images of dopamine transporter single-photon emission computed tomography (DAT-SPECT). Methods: Abnormal DAT-SPECT images of subjects with Parkinson’s disease and parkinsonism caused by other disorders were divided into training and test sets. Striatal regions of the images were segmented by using an active contour model and were used as the data to perform transfer learning on a pre-trained ANN to discriminate Parkinson’s disease from parkinsonism caused by other disorders. A support vector machine trained using parameters of semi-quantitative measurements including specific binding ratio and asymmetry index was used for comparison. Results: The predictive accuracy of the ANN classifier (86%) was higher than that of the support vector machine classifier (68%). The sensitivity and specificity of the ANN classifier in predicting Parkinson’s disease were 81.8% and 88.6%, respectively. Conclusions: The ANN classifier outperformed classical biomarkers in differentiating Parkinson’s disease from parkinsonism caused by other disorders. This classifier can be readily included into standalone computer software for clinical application.

Migraine is related to an increased risk of Parkinson’s disease: A population-based, propensity score-matched, longitudinal follow-up study

Cephalalgia ◽  
2016 ◽  
Vol 36 (14) ◽  
pp. 1316-1323 ◽  
Author(s):  
Hsin-I Wang ◽  
Yu-Chun Ho ◽  
Ya-Ping Huang ◽  
Shin-Liang Pan
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Survival Rate ◽  
Propensity Score ◽  
Population Based ◽  
Free Survival ◽  
Follow Up Study ◽  
Increased Risk ◽  
Migraine Group

Background The association between migraine and Parkinson’s disease (PD) remains controversial. The purpose of the present population-based, propensity score-matched follow-up study was to investigate whether migraineurs are at a higher risk of developing PD. Methods A total of 41,019 subjects aged between 40 and 90 years with at least two ambulatory visits with a diagnosis of migraine in 2001 were enrolled in the migraine group. A logistic regression model that included age, sex, pre-existing comorbidities and socioeconomic status as covariates was used to compute the propensity score. The non-migraine group consisted of 41,019 propensity score-matched, randomly sampled subjects without migraine. The PD-free survival rate were estimated using the Kaplan–Meier method. Stratified Cox proportional hazard regression was used to estimate the effect of migraine on the risk of developing PD. Results During follow-up, 148 subjects in the migraine group and 101 in the non-migraine group developed PD. Compared to the non-migraine group, the hazard ratio of PD for the migraine group was 1.64 (95% confidence interval: 1.25–2.14, p = 0.0004). The PD-free survival rate for the migraine group was significantly lower than that for the non-migraine group ( p = 0.0041). Conclusions This study showed an increased risk of developing PD in patients with migraine.

scholarly journals Trend of recognizing depression symptoms and antidepressants use in newly diagnosed Parkinson's disease: Population‐based study

2021 ◽  
Author(s):  
Khalid Orayj ◽  
Tahani Almeleebia ◽  
Easwaran Vigneshwaran ◽  
Sultan Alshahrani ◽  
Sirajudeen. S. Alavudeen ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Population Based ◽  
Depression Symptoms ◽  
Newly Diagnosed ◽  
Population Based Study

scholarly journals Individual Magnetoencephalography Response Profiles to Short-Duration L-Dopa in Parkinson’s Disease

2021 ◽  
Vol 15 ◽  
Author(s):  
Edgar Peña ◽  
Tareq M. Mohammad ◽  
Fedaa Almohammed ◽  
Tahani AlOtaibi ◽  
Shahpar Nahrir ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Short Duration ◽  
Spectral Power ◽  
Clinical State ◽  
Support Vector ◽  
Learning Approaches ◽  
Factor Scores ◽  
Subject Specific ◽  
Motor Cortical

Clinical responses to dopamine replacement therapy for individuals with Parkinson’s disease (PD) are often difficult to predict. We characterized changes in MDS-UPDRS motor factor scores resulting from a short-duration L-Dopa response (SDR), and investigated how the inter-subject clinical differences could be predicted from motor cortical magnetoencephalography (MEG). MDS-UPDRS motor factor scores and resting-state MEG recordings were collected during SDR from twenty individuals with a PD diagnosis. We used a novel subject-specific strategy based on linear support vector machines to quantify motor cortical oscillatory frequency profiles that best predicted medication state. Motor cortical profiles differed substantially across individuals and showed consistency across multiple data folds. There was a linear relationship between classification accuracy and SDR of lower limb bradykinesia, although this relationship did not persist after multiple comparison correction, suggesting that combinations of spectral power features alone are insufficient to predict clinical state. Factor score analysis of therapeutic response and novel subject-specific machine learning approaches based on subject-specific neuroimaging provide tools to predict outcomes of therapies for PD.

Complications and operative spine fusion construct length in Parkinson’s disease: A nationwide population-based analysis

2017 ◽  
Vol 43 ◽  
pp. 220-223 ◽  
Author(s):  
Shearwood McClelland ◽  
Joseph F. Baker ◽  
Justin S. Smith ◽  
Breton G. Line ◽  
Robert A. Hart ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Population Based ◽  
Spine Fusion ◽  
Fusion Construct

Chemical exposures and Parkinson's disease: A population-based case–control study

2006 ◽  
Vol 21 (10) ◽  
pp. 1688-1692 ◽  
Author(s):  
Roberta Frigerio ◽  
Kevin R. Sanft ◽  
Brandon R. Grossardt ◽  
Brett J. Peterson ◽  
Alexis Elbaz ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Case Control Study ◽  
Population Based ◽  
Case Control ◽  
Chemical Exposures ◽  
Control Study
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