scholarly journals Invasive and Non-Invasive Stimulation in Parkinson’s Disease

10.5772/21202 ◽  
2011 ◽  
Author(s):  
Caspar Stephani
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Non Invasive

IS 28. Non-invasive brain stimulation in Parkinson’s disease

2013 ◽  
Vol 124 (10) ◽  
pp. e48
Author(s):  
R. Chen
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Brain Stimulation ◽  
Non Invasive

Long-term effect of non-invasive Vagus Nerve in patients with Parkinson's disease: A randomized double blind controlled study

2020 ◽  
Vol 79 ◽  
pp. e61
Author(s):  
B. Mondal ◽  
S. Choudhury ◽  
R. Banerjee ◽  
A. Roy ◽  
K. Chatterjee ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Vagus Nerve ◽  
Term Effect ◽  
Controlled Study ◽  
Double Blind ◽  
Non Invasive ◽  
Long Term Effect

10-Effects of non-invasive brain stimulation on motor speech disorder in Parkinson’s disease

2018 ◽  
Vol 129 (4) ◽  
pp. e9
Author(s):  
L. Brabenec ◽  
J. Mekyska ◽  
Z. Galáž ◽  
P. Klobušiakova ◽  
M. Koštálová ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Brain Stimulation ◽  
Speech Disorder ◽  
Motor Speech ◽  
Non Invasive ◽  
Motor Speech Disorder

T002 Non-invasive brain stimulation for Parkinson’s disease

2017 ◽  
Vol 128 (3) ◽  
pp. e1
Author(s):  
D. Benninger
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Brain Stimulation ◽  
Non Invasive

scholarly journals Eye movement evaluation in Multiple Sclerosis and Parkinson’s Disease using a Standardized Oculomotor and Neuro-ophthalmic Disorder Assessment (SONDA)

2020 ◽  
Author(s):  
Alessandro Grillini ◽  
Remco J. Renken ◽  
Anne C. L. Vrijling ◽  
Joost Heutink ◽  
Frans W. Cornelissen
Keyword(s):  
Multiple Sclerosis ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Standardized Tests ◽  
Eye Movement ◽  
Oculomotor System ◽  
Oculomotor Behavior ◽  
Non Invasive ◽  
Spatio Temporal ◽  
Pursuit Gain

AbstractEvaluating the state of the oculomotor system of a patient is one of the fundamental tests done in neuro-ophthalmology. However, up to date, very few quantitative standardized tests of eye movements quality exist, limiting this assessment to confrontational tests reliant on subjective interpretation. Furthermore, quantitative tests relying on eye movement properties such as pursuit gain and saccade dynamics are often insufficient to capture the complexity of the underlying disorders and are often (too) long and tiring. In this study, we present SONDA (Standardised Oculomotor and Neurological Disorder Assessment): this test is based on analyzing eye tracking recorded during a short and intuitive continuous tracking task. We tested patients affected by Multiple Sclerosis (MS) and Parkinson’s Disease (PD) and find that: (1) the saccadic dynamics of the main sequence alone are not sufficient to separate patients from healthy controls; (2) the combination of spatio-temporal and statistical properties of saccades and saccadic dynamics enables an identification of oculomotor abnormalities in both MS and PD patients. We conclude that SONDA constitutes a powerful screening tool that allows an in-depth evaluation of (deviant) oculomotor behavior in a few minutes of non-invasive testing.

scholarly journals Gut Microbiota Dysfunction as Reliable Non-invasive Early Diagnostic Biomarkers in the Pathophysiology of Parkinson's Disease: A Critical Review

2018 ◽  
Vol 24 (1) ◽  
pp. 30-42 ◽  
Author(s):  
Arun T Nair ◽  
Vadivelan Ramachandran ◽  
Nanjan M Joghee ◽  
Shanish Antony ◽  
Gopalakrishnan Ramalingam
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Gut Microbiota ◽  
Critical Review ◽  
Diagnostic Biomarkers ◽  
Non Invasive ◽  
Early Diagnostic

Classification of Parkinson's Disease from Smartphone Recording Data using Time-Frequency Analysis and Convolutional Neural Network (Preprint)

2021 ◽  
Author(s):  
Denchai Worasawate ◽  
Warisara Asawaponwiput ◽  
Natsue Yoshimura ◽  
Apichart Intarapanich ◽  
Decho Surangsrirat
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Fourier Transform ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Non Invasive ◽  
The Central Nervous System ◽  
Short Time ◽  
The Voice

BACKGROUND Parkinson’s disease (PD) is a long-term neurodegenerative disease of the central nervous system. The current diagnosis is dependent on clinical observation and the abilities and experience of a trained specialist. One of the symptoms that affect most patients over the course of their illness is voice impairment. OBJECTIVE Voice is one of the non-invasive data that can be collected remotely for diagnosis and disease progression monitoring. In this study, we analyzed voice recording data from a smartphone as a possible disease biomarker. The dataset is from one of the largest mobile PD studies, the mPower study. METHODS A total of 29,798 audio clips from 4,051 participants were used for the analysis. The voice recordings were from sustained phonation by the participant saying /aa/ for ten seconds into the iPhone microphone. The audio samples were converted to a spectrogram using a short-time Fourier transform. CNN models were then applied to classify the samples. RESULTS A total of 29,798 audio clips from 4,051 participants were used for the analysis. The voice recordings were from sustained phonation by the participant saying /aa/ for ten seconds into the iPhone microphone. The audio samples were converted to a spectrogram using a short-time Fourier transform. CNN models were then applied to classify the samples. CONCLUSIONS Classification accuracies of the proposed method with LeNet-5, ResNet-50, and VGGNet-16 are 97.7 ± 0.1%, 98.6 ± 0.2%, and 99.3 ± 0.1%, respectively. CLINICALTRIAL ClinicalTrials.gov NCT02696603; https://www.clinicaltrials.gov/ct2/show/NCT02696603

Soft Computing-Based Early Detection of Parkinson's Disease Using Non-Invasive Method Based on Speech Analysis

2021 ◽  
pp. 329-340
Author(s):  
Chandrasekar Ravi
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Speech Analysis ◽  
Machine Learning Techniques ◽  
Speech Signals ◽  
Fuzzy Classifier ◽  
Non Invasive ◽  
Specificity And Sensitivity ◽  
Learning Techniques ◽  
Invasive Method

This chapter aims to use the speech signals that are a behavioral bio-marker for Parkinson's disease. The victim's vocabulary is mostly lost, or big gaps are observed when they are talking or the conversation is abruptly stopped. Therefore, speech analysis could help to identify the complications in conversation from the inception of the symptoms of Parkinson's disease in initial phases itself. Speech can be regularly logged in an unobstructed approach and machine learning techniques can be applied and analyzed. Fuzzy logic-based classifier is proposed for learning from the training speech signals and classifying the test speech signals. Brainstorm optimization algorithm is proposed for extracting the fuzzy rules from the speech data, which is used by fuzzy classifier for learning and classification. The performance of the proposed classifier is evaluated using metrics like accuracy, specificity, and sensitivity, and compared with benchmark classifiers like SVM, naïve Bayes, k-means, and decision tree. It is observed that the proposed classifier outperforms the benchmark classifiers.

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