A Hybrid Extreme Learning Machine Approach for Early Diagnosis of Parkinson’s Disease

2014 ◽  
pp. 342-349
Author(s):  
Yao-Wei Fu ◽  
Hui-Ling Chen ◽  
Su-Jie Chen ◽  
Li-Juan Li ◽  
Shan-Shan Huang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Diagnosis ◽  
Extreme Learning Machine ◽  
Learning Machine

scholarly journals An Optimized Brain-Based Algorithm for Classifying Parkinson’s Disease

2020 ◽  
Vol 10 (5) ◽  
pp. 1827 ◽  
Author(s):  
Rodrigo Olivares ◽  
Roberto Munoz ◽  
Ricardo Soto ◽  
Broderick Crawford ◽  
Diego Cárdenas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Extreme Learning Machine ◽  
Neurodegenerative Disorder ◽  
Fine Tuning ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Classification Problems ◽  
Learning Machine

During the last years, highly-recognized computational intelligence techniques have been proposed to treat classification problems. These automatic learning approaches lead to the most recent researches because they exhibit outstanding results. Nevertheless, to achieve this performance, artificial learning methods firstly require fine tuning of their parameters and then they need to work with the best-generated model. This process usually needs an expert user for supervising the algorithm’s performance. In this paper, we propose an optimized Extreme Learning Machine by using the Bat Algorithm, which boosts the training phase of the machine learning method to increase the accuracy, and decreasing or keeping the loss in the learning phase. To evaluate our proposal, we use the Parkinson’s Disease audio dataset taken from UCI Machine Learning Repository. Parkinson’s disease is a neurodegenerative disorder that affects over 10 million people. Although its diagnosis is through motor symptoms, it is possible to evidence the disorder through variations in the speech using machine learning techniques. Results suggest that using the bio-inspired optimization algorithm for adjusting the parameters of the Extreme Learning Machine is a real alternative for improving its performance. During the validation phase, the classification process for Parkinson’s Disease achieves a maximum accuracy of 96.74% and a minimum loss of 3.27%.

scholarly journals Classification of Parkinson’s disease patients based on spectrogram using local binary pattern descriptors

2022 ◽  
Vol 2153 (1) ◽  
pp. 012014
Author(s):  
E Gelvez-Almeida ◽  
A Váasquez-Coronel ◽  
R Guatelli ◽  
V Aubin ◽  
M Mora
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Extreme Learning Machine ◽  
Local Binary Pattern ◽  
Audio Signal ◽  
Training Time ◽  
Network Training ◽  
Learning Machine ◽  
Hidden Layer ◽  
Rate Of Success

Abstract Extreme learning machine is an algorithm that has shown a good performance facing classification and regression problems. It has gained great acceptance by the scientific community due to the simplicity of the model and its sola great generalization capacity. This work proposes the use of extreme learning machine neural networks to carry out the classification between Parkinson’s disease patients and healthy individuals. The descriptor used corresponds to the feature vector generated applying the local binary Pattern algorithm to the grayscale spectrograms. The spectrograms are obtained from the audio signal samples from the considered repository. Experiments are conducted with single hidden layer and multilayer extreme learning machine networks comparing the results of each structure. Results show that hierarchical extreme learning machine with three hidden layers has a better general performance over multilayer extreme learning machine networks and a single hidden layer extreme learning machine. The rate of success obtained is within the ranges presented in the literature. However, the hierarchical network training time is considerably faster compared to multilayer networks of three or two hidden layers.

Current Therapeutic Strategies and Perspectives for Neuroprotection in Parkinson’s Disease

2020 ◽  
Vol 26 (37) ◽  
pp. 4738-4746
Author(s):  
Mohan K. Ghanta ◽  
P. Elango ◽  
Bhaskar L. V. K. S.
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Diagnosis ◽  
Neurodegenerative Disorder ◽  
Therapeutic Strategies ◽  
Neuroprotective Agents ◽  
Striatal Neurons ◽  
The Status ◽  
Advanced Stages ◽  
Dopaminergic Pathways

Parkinson’s disease is a progressive neurodegenerative disorder of dopaminergic striatal neurons in basal ganglia. Treatment of Parkinson’s disease (PD) through dopamine replacement strategies may provide improvement in early stages and this treatment response is related to dopaminergic neuronal mass which decreases in advanced stages. This treatment failure was revealed by many studies and levodopa treatment became ineffective or toxic in chronic stages of PD. Early diagnosis and neuroprotective agents may be a suitable approach for the treatment of PD. The essentials required for early diagnosis are biomarkers. Characterising the striatal neurons, understanding the status of dopaminergic pathways in different PD stages may reveal the effects of the drugs used in the treatment. This review updates on characterisation of striatal neurons, electrophysiology of dopaminergic pathways in PD, biomarkers of PD, approaches for success of neuroprotective agents in clinical trials. The literature was collected from the articles in database of PubMed, MedLine and other available literature resources.

scholarly journals The Role of Salivary Biomarkers in the Early Diagnosis of Alzheimer’s Disease and Parkinson’s Disease

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 371
Author(s):  
Patrycja Pawlik ◽  
Katarzyna Błochowiak
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Early Diagnosis ◽  
Neurodegenerative Diseases ◽  
Diagnostic Tool ◽  
Clinical Symptoms ◽  
Early Screening ◽  
Salivary Biomarkers

Many neurodegenerative diseases present with progressive neuronal degeneration, which can lead to cognitive and motor impairment. Early screening and diagnosis of neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD) are necessary to begin treatment before the onset of clinical symptoms and slow down the progression of the disease. Biomarkers have shown great potential as a diagnostic tool in the early diagnosis of many diseases, including AD and PD. However, screening for these biomarkers usually includes invasive, complex and expensive methods such as cerebrospinal fluid (CSF) sampling through a lumbar puncture. Researchers are continuously seeking to find a simpler and more reliable diagnostic tool that would be less invasive than CSF sampling. Saliva has been studied as a potential biological fluid that could be used in the diagnosis and early screening of neurodegenerative diseases. This review aims to provide an insight into the current literature concerning salivary biomarkers used in the diagnosis of AD and PD. The most commonly studied salivary biomarkers in AD are β-amyloid1-42/1-40 and TAU protein, as well as α-synuclein and protein deglycase (DJ-1) in PD. Studies continue to be conducted on this subject and researchers are attempting to find correlations between specific biomarkers and early clinical symptoms, which could be key in creating new treatments for patients before the onset of symptoms.

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