scholarly journals A comparative and comprehensive study of prediction of Parkinson’s disease

2021 ◽  
Vol 23 (3) ◽  
pp. 1748
Author(s):  
N. Prasath ◽  
Vigneshwaran Pandi ◽  
Sindhuja Manickavasagam ◽  
Prabu Ramadoss
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Subtractive Clustering ◽  
Monitoring Methods ◽  
Paralysis Agitans ◽  
Replacement System ◽  
Future Work ◽  
Comprehensive Study

Objectives: Parkinson's Disease (PD) is a form of neurodegenerative disease that is caused the progressive weakening of dopaminergic nerve cells that affects a large number of people around the world.  The event of recent treatment methods principally depends upon the experimental data resulting from assessment balances and patients’ journals that take varied boundaries with reference to legitimacy, inter-rater inconsistency, and incessant monitoring. Methods: Nowadays various techniques and algorithms are utilized in predicting the accuracy in PD. A range of those techniques, including SVM, Artificial Neural Network, Naive Bayes, Kernel based extreme learning through subtractive clustering landscapes, Random Forest, The Multi-Layer Perceptron with Back-Propagation Learning Algorithm are widely applied to form the acceptable decision accurately. During this work, and in-depth review was administered on various techniques proposed by numerous researchers. a replacement system must be proposed which uses DL techniques and considers other attributes of paralysis agitans which can improve the prediction and be an advancement within the medical field. Result: It has been observed that many researches have been done in identifying the PD yet there is a need of suitable method or algorithm to improve the prediction of PD which will help in the clinical management. Conclusion and Future work: Most of the methods have used speech as a major attribute for their research and have produced substantial accuracy. In order to increase the precision approaches involving movements, facial expression and other attributes also be considered for evaluation

scholarly journals Historical and cross-cultural perspectives on Parkinson’s disease

2018 ◽  
Vol 15 (3) ◽  
Author(s):  
Lee Xenakis Blonder
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Neurodegenerative Disorder ◽  
Ancient Greek ◽  
Genetic Studies ◽  
Cultural Perspectives ◽  
Paralysis Agitans ◽  
Ancient Times ◽  
Jean Martin Charcot ◽  
British Physician

Abstract Parkinson’s disease (PD) is a common neurodegenerative disorder, affecting up to 10 million people worldwide according to the Parkinson’s Disease Foundation. Epidemiological and genetic studies show a preponderance of idiopathic cases and a subset linked to genetic polymorphisms of a familial nature. Traditional Chinese medicine and Ayurveda recognized and treated the illness that Western Medicine terms PD millennia ago, and descriptions of Parkinson’s symptomatology by Europeans date back 2000 years to the ancient Greek physician Galen. However, the Western nosological classification now referred to in English as “Parkinson’s disease” and the description of symptoms that define it, are accredited to British physician James Parkinson, who in 1817 authored The Shaking Palsy. Later in the nineteenth century, French neurologist Jean-Martin Charcot re-labeled paralysis agitans “Parkinson’s disease” and over a century of scientific research ensued. This review discusses European, North American, and Asian contributions to the understanding and treatment of PD from ancient times through the twentieth century.

Comprehensive analysis of resting tremor based on acceleration signals of patients with Parkinson’s disease

2021 ◽  
pp. 1-13
Author(s):  
Sen Liu ◽  
Han Yuan ◽  
Jiali Liu ◽  
Hai Lin ◽  
Cuiwei Yang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rating Scale ◽  
Statistical Tests ◽  
Real Life ◽  
Feature Analysis ◽  
Support Vector ◽  
Monitoring Methods ◽  
Resting Tremor ◽  
Acceleration Signals

BACKGROUND: Resting tremor is an essential characteristic in patients suffering from Parkinson’s disease (PD). OBJECTIVE: Quantification and monitoring of tremor severity is clinically important to help achieve medication or rehabilitation guidance in daily monitoring. METHODS: Wrist-worn tri-axial accelerometers were utilized to record the long-term acceleration signals of PD patients with different tremor severities rated by Unified Parkinson’s Disease Rating Scale (UPDRS). Based on the extracted features, three kinds of classifiers were used to identify different tremor severities. Statistical tests were further designed for the feature analysis. RESULTS: The support vector machine (SVM) achieved the best performance with an overall accuracy of 94.84%. Additional feature analysis indicated the validity of the proposed feature combination and revealed the importance of different features in differentiating tremor severities. CONCLUSION: The present work obtains a high-accuracy classification in tremor severity, which is expected to play a crucial role in PD treatment and symptom monitoring in real life.

Leopold Ordenstein: on paralysis agitans and multiple sclerosis

2007 ◽  
Vol 13 (9) ◽  
pp. 1195-1199 ◽  
Author(s):  
H.C. Lehmann ◽  
H.-P. Hartung ◽  
B.C. Kieseier
Keyword(s):  
Multiple Sclerosis ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
French Language ◽  
Doctoral Thesis ◽  
Paralysis Agitans ◽  
History Of ◽  
First Time ◽  
Jean Martin Charcot

In 1868 the German Leopold Ordenstein (1835—1902) published in Paris a doctoral thesis in French language under the patronage of Jean-Martin Charcot (1825—1893). For the first time, multiple sclerosis and Parkinson's disease were clearly recognized as different clinical entities, based on clinical and pathological data. Ordenstein's work represents today a fundamental and often credited, yet still widely unknown, contribution to the history of these two diseases. The present paper delivers a synopsis of this key document. In addition, the life and work of Leopold Ordenstein will be reviewed. Multiple Sclerosis 2007; 13: 1195—1199. http://msj.sagepub.com

scholarly journals Overconfidence in visual perception in Parkinson’s disease

2020 ◽  
Author(s):  
Orly Halperin ◽  
Roie Karni ◽  
Simon Israeli-Korn ◽  
Sharon Hassin-Baer ◽  
Adam Zaidel
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Young Adult ◽  
Motion Perception ◽  
Multisensory Integration ◽  
Visual Cues ◽  
Visual Motion ◽  
Visual Motion Perception ◽  
The Right ◽  
Future Work

AbstractBackgroundIncreased dependence on visual cues in Parkinson’s disease (PD) can unbalance the perception-action loop, impair multisensory integration, and affect everyday function of PD patients. It is currently unknown why PD patients seem to be more reliant on their visual cues.ObjectivesWe hypothesized that PD patients may be overconfident in the reliability (precision) of their visual cues. In this study we tested coherent visual motion perception in PD, and probed subjective (self-reported) confidence in their visual motion perception.Methods20 patients with idiopathic PD, 21 healthy aged-matched controls and 20 healthy young adult participants were presented with visual stimuli of moving dots (random dot kinematograms). They were asked to report: (1) whether the aggregate motion of dots was to the left or to the right, and (2) how confident they were that their perceptual discrimination was correct.ResultsVisual motion discrimination thresholds were similar (unimpaired) in PD compared to the other groups. By contrast, PD patients were significantly overconfident in their visual perceptual decisions (p=0.002 and p<0.001 vs. the age-matched and young adult groups, respectively).ConclusionsThese results suggest intact visual motion perception, but overestimation of visual cue reliability, in PD. Overconfidence in visual (vs. other, e.g., somatosensory) cues could underlie accounts of increased visual dependence and impaired multisensory integration in PD, and could contribute to gait and balance impairments. Future work should investigate PD confidence in somatosensory function. A better understanding of altered sensory reliance in PD might open up new avenues to treat debilitating symptoms.

A Comprehensive Study of miRNAs in Parkinson’s Disease: Diagnostics and Therapeutic Approaches

2022 ◽  
Vol 21 ◽  
Author(s):  
Saima Owais ◽  
Yasir Hasan Siddique
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Lewy Bodies ◽  
Alpha Synuclein ◽  
Therapeutic Approaches ◽  
Molecular Events ◽  
Disease Diagnostics ◽  
Therapeutic Molecules ◽  
Comprehensive Study

Abstract: Parkinson’s disease (PD) is the second most debilitating neurodegenerative movement disorder. It is characterized by the presence of fibrillar alpha-synuclein amassed in the neurons, known as Lewy bodies. Certain cellular and molecular events are involved leading to the degeneration of dopaminergic neurons. However, the origin and implication of such events are still uncertain. Nevertheless, the role of microRNAs (miRNAs) as important biomarkers and therapeutic molecules is unquestionable. The most challenging task by far in PD treatment has been its late diagnosis followed by therapeutics. miRNAs are an emerging hope to meet the need of early diagnosis, thereby promising an improved movement symptom and prolonged life of the patients. The continuous efforts in discovering the role of miRNAs could be made possible by the utilisation of various animal models of PD. These models help us to understand insights into the mechanism of the disease. Moreover, miRNAs have been surfaced as therapeutically important molecules with distinct delivery systems enhancing their success rate. This review aims at providing an outline of different miRNAs implicated in either PD-associated gene regulation or involved in therapeutics.

An ANFIS Based Model for Predicting Frost Heaving in Seasonal Frozen Regions

2014 ◽  
Vol 501-504 ◽  
pp. 391-394
Author(s):  
Yi Ming Xiang ◽  
Xue Yan Liu ◽  
Gui Xiang Ling ◽  
Bin Du
Keyword(s):  
Clustering Algorithm ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Subtractive Clustering ◽  
Forecast Errors ◽  
Frost Heaving ◽  
Inference System ◽  
Neuro Fuzzy

An adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict frost heaving in seasonal frozen regions. The structure of ANFIS is initialized by the subtractive clustering algorithm. The hybrid learning algorithm consisting of back-propagation and least-squares estimation is used to adjust parameters of ANFIS and automatically produce fuzzy rules. The data of frost heaving test obtained from a literature are used to train and check the system. The predicted results show that the proposed model outperforms the back propagation neural network (BPNN) in terms of computational speed, forecast errors, and efficiency. The ANFIS based model proves to be an effective approach to achieve both high accuracy and less computational complexity for predicting frost heaving.

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