scholarly journals Enhancing Parkinson’s Disease Diagnosis Accuracy Through Speech Signal Algorithm Modeling

2022 ◽  
Vol 70 (2) ◽  
pp. 2953-2969
Author(s):  
Omar M. El-Habbak ◽  
Abdelrahman M. Abdelalim ◽  
Nour H. Mohamed ◽  
Habiba M. Abd-Elaty ◽  
Mostafa A. Hammouda ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Speech Signal ◽  
Disease Diagnosis ◽  
Diagnosis Accuracy

Parkinson's Disease Diagnosis Based on Multivariate Deep Features of Speech Signal

2018 ◽  
Author(s):  
P. Khojasteh ◽  
R. Viswanathan ◽  
B. Aliahmad ◽  
S. Ragnav ◽  
P. Zham ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Speech Signal ◽  
Disease Diagnosis

A Belief Rule Base Approach to Support Comparison of Digital Speech Signal Features for Parkinson’s Disease Diagnosis

2021 ◽  
pp. 388-400
Author(s):  
Shafkat Raihan ◽  
Sharif Noor Zisad ◽  
Raihan Ul Islam ◽  
Mohammad Shahadat Hossain ◽  
Karl Andersson
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Speech Signal ◽  
Disease Diagnosis ◽  
Rule Base ◽  
Base Approach ◽  
Signal Features ◽  
Belief Rule Base

Pursuing Multiple Biomarkers for Early Idiopathic Parkinson’s Disease Diagnosis

2021 ◽  
Author(s):  
Yareth Gopar-Cuevas ◽  
Ana P. Duarte-Jurado ◽  
Rosa N. Diaz-Perez ◽  
Odila Saucedo-Cardenas ◽  
Maria J. Loera-Arias ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Diagnosis ◽  
Idiopathic Parkinson’S Disease ◽  
Idiopathic Parkinson's Disease ◽  
Multiple Biomarkers

scholarly journals Holistic Metabolomic Laboratory-Developed Test (LDT): Development and Use for the Diagnosis of Early-Stage Parkinson’s Disease

Metabolites ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 14
Author(s):  
Petr G. Lokhov ◽  
Dmitry L. Maslov ◽  
Steven Lichtenberg ◽  
Oxana P. Trifonova ◽  
Elena E. Balashova
Keyword(s):  
Parkinson’S Disease ◽  
Molecular Weight ◽  
Parkinson's Disease ◽  
High Resolution Mass Spectrometry ◽  
Early Stage ◽  
Disease Diagnosis ◽  
The Body ◽  
Low Molecular Weight ◽  
Low Molecular Weight Compounds

A laboratory-developed test (LDT) is a type of in vitro diagnostic test that is developed and used within a single laboratory. The holistic metabolomic LDT integrating the currently available data on human metabolic pathways, changes in the concentrations of low-molecular-weight compounds in the human blood during diseases and other conditions, and their prevalent location in the body was developed. That is, the LDT uses all of the accumulated metabolic data relevant for disease diagnosis and high-resolution mass spectrometry with data processing by in-house software. In this study, the LDT was applied to diagnose early-stage Parkinson’s disease (PD), which currently lacks available laboratory tests. The use of the LDT for blood plasma samples confirmed its ability for such diagnostics with 73% accuracy. The diagnosis was based on relevant data, such as the detection of overrepresented metabolite sets associated with PD and other neurodegenerative diseases. Additionally, the ability of the LDT to detect normal composition of low-molecular-weight compounds in blood was demonstrated, thus providing a definition of healthy at the molecular level. This LDT approach as a screening tool can be used for the further widespread testing for other diseases, since ‘omics’ tests, to which the metabolomic LDT belongs, cover a variety of them.

Changes in Body Composition Before and After Parkinson's Disease Diagnosis

2021 ◽  
Author(s):  
Shengfang Song ◽  
Zhehui Luo ◽  
Chenxi Li ◽  
Xuemei Huang ◽  
Eric J. Shiroma ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Body Composition ◽  
Disease Diagnosis ◽  
Before And After

Color-convertible fluorescent nanoprobe for Parkinson’s disease diagnosis

2021 ◽  
pp. 132368
Author(s):  
Jiapei Yang ◽  
Lei Wang ◽  
Yue Su ◽  
Lingyue Shen ◽  
Xihui Gao ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Diagnosis ◽  
Fluorescent Nanoprobe

Blood Cholesterol Decreases as Parkinson’s Disease Develops and Progresses

2021 ◽  
pp. 1-10
Author(s):  
Keran Wang ◽  
Zhehui Luo ◽  
Chenxi Li ◽  
Xuemei Huang ◽  
Eric J. Shiroma ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Confidence Interval ◽  
Total Cholesterol ◽  
Cholesterol Level ◽  
Disease Diagnosis ◽  
Inverse Association ◽  
Prodromal Stage ◽  
Before And After ◽  
Blood Total Cholesterol

Background: Literature shows an inverse association of circulating cholesterol level with the risk of Parkinson’s disease (PD); this finding has important ramifications, but its interpretation has been debated. Objective: To longitudinally examine how blood total cholesterol changes during the development of PD. Methods: In the Health, Aging and Body Composition study (n = 3,075, 73.6±2.9 years), blood total cholesterol was measured at clinic visit years 1, 2, 4, 6, 8, 10, and 11. We first examined baseline cholesterol in relation to PD risk, adjusting for potential confounders and competing risk of death. Then, by contrasting the observed with expected cholesterol levels, we examined the trajectory of changes in total cholesterol before and after disease diagnosis. Results: Compared to the lowest tertile of baseline total cholesterol, the cumulative incident ratio of PD and 95%confidence interval was 0.41 (0.20, 0.86) for the second tertile, and 0.69 (0.35, 1.35) for the third tertile. In the analysis that examined change of total cholesterol level before and after PD diagnosis, we found that its level began to decrease in the prodromal stage of PD and became statistically lower than the expected values∼4 years before disease diagnosis (observed-expected difference, –6.68 mg/dL (95%confidence interval: –13.14, –0.22)). The decreasing trend persisted thereafter; by year-6 post-diagnosis, the difference increased to –13.59 mg/dL (95%confidence interval: –22.12, –5.06), although the linear trend did not reach statistical significance (p = 0.10). Conclusion: Circulating total cholesterol began to decrease in the prodromal stage of PD, which may in part explain its reported inverse association with PD.

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.

Sign in / Sign up

Export Citation Format

Share Document