scholarly journals A Simple and Effective Approach Based on a Multi-Level Feature Selection for Automated Parkinson’s Disease Detection

2022 ◽  
Vol 12 (1) ◽  
pp. 55
Author(s):  
Fatih Demir ◽  
Kamran Siddique ◽  
Mohammed Alswaitti ◽  
Kursat Demir ◽  
Abdulkadir Sengur
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Early Diagnosis ◽  
Neurodegenerative Disorder ◽  
Bayesian Optimization ◽  
L1 Norm ◽  
Bayesian Optimization Algorithm ◽  
Knn Classifier ◽  
Multi Level

Parkinson’s disease (PD), which is a slowly progressing neurodegenerative disorder, negatively affects people’s daily lives. Early diagnosis is of great importance to minimize the effects of PD. One of the most important symptoms in the early diagnosis of PD disease is the monotony and distortion of speech. Artificial intelligence-based approaches can help specialists and physicians to automatically detect these disorders. In this study, a new and powerful approach based on multi-level feature selection was proposed to detect PD from features containing voice recordings of already-diagnosed cases. At the first level, feature selection was performed with the Chi-square and L1-Norm SVM algorithms (CLS). Then, the features that were extracted from these algorithms were combined to increase the representation power of the samples. At the last level, those samples that were highly distinctive from the combined feature set were selected with feature importance weights using the ReliefF algorithm. In the classification stage, popular classifiers such as KNN, SVM, and DT were used for machine learning, and the best performance was achieved with the KNN classifier. Moreover, the hyperparameters of the KNN classifier were selected with the Bayesian optimization algorithm, and the performance of the proposed approach was further improved. The proposed approach was evaluated using a 10-fold cross-validation technique on a dataset containing PD and normal classes, and a classification accuracy of 95.4% was achieved.

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 Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Ehsan Adeli ◽  
Guorong Wu ◽  
Behrouz Saghafi ◽  
Le An ◽  
Feng Shi ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Early Diagnosis ◽  
Margin Classification

scholarly journals Feature Selection Based on L1-Norm Support Vector Machine and Effective Recognition System for Parkinson’s Disease Using Voice Recordings

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 37718-37734 ◽  
Author(s):  
Amin Ul Haq ◽  
Jian Ping Li ◽  
Muhammad Hammad Memon ◽  
Jalaluddin khan ◽  
Asad Malik ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Support Vector Machine ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Recognition System ◽  
Support Vector ◽  
L1 Norm

An intelligent system for Parkinson’s diagnosis using hybrid feature selection approach

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Intelligent System ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Feature Reduction ◽  
Selection Methods ◽  
Selection Approach ◽  
Feature Selection Approach

Parkinson’s is the second most common neurodegenerative disorder after Alzheimer’s disease which adversely affects the nervous system of the patients. During the nascent stage, the symptoms of Parkinson’s disease are mild and sometimes go unnoticeable but as the disease progresses the symptoms go severe, so its diagnosis at an early stage is not easy. Recent research has shown that changes in speech or distortion in voice can be taken effectively used for early Parkinson’s detection. In this work, the authors propose a system of Parkinson's disease detection using speech signals. As the feature selection plays an important role during classification, authors have proposed a hybrid MIRFE feature selection approach. The result of the proposed feature selection approach is compared with the 5 standard feature selection methods by XGBoost classifier. The proposed MIRFE approach selects 40 features out of 754 features with a feature reduction ratio of 94.69%. An accuracy of 93.88% and area under curve (AUC) of 0.978 is obtained by the proposed system.

Multiclass recognition of Alzheimer’s and Parkinson’s disease using various machine learning techniques: A study

2021 ◽  
Author(s):  
Chetan Balaji ◽  
D. S. Suresh
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Diagnosis ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Ensemble Methods ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Input Space

The aging population is primarily affected by Alzheimer’s disease (AD) that is an incurable neurodegenerative disorder. There is a need for an automated efficient technique to diagnose Alzheimer’s in its early stage. Various techniques are used to diagnose AD. EEG and neuroimaging methodologies are widely used to highlight changes in the electrical activity of the brain signals that are helpful for early diagnosis. Parkinson’s disease (PD) is a major neurological disease that results in an average of 50,000 new clinical diagnoses worldwide every year. The voice features are majorly used as the main means to diagnose PD. The major symptoms of PD are loss of intensity, the monotony of loudness and pitch, reduction in stress, unidentified silences, and dysphonia. Even though various innovative models are proposed by explorers about Alzheimer’s and Parkinson’s classification diseases, still there is a need for efficient learning methodologies and techniques. This paper provides a review on using machine learning (ML) together with several feature extraction techniques that is helpful in the early detection of AD with Parkinson’s. The novelty and objective of this study are that the CAD technique is used to improve the accuracy of early diagnosis of AD. The proposed technique depends on the nonlinear process for data dimension reduction, feature removal, and classification using kernel-based support vector machine (SVM) classifiers. The dimension of the input space is radically diminished with kernel methods. As the learning set is labeled, it creates sense to utilize this information to make a dependable method of dropping the input space dimension. The different techniques of ML are explained under the major approaches viz. SVM, artificial neural network (ANN), deep learning (DL), and ensemble methods. A comprehensive assessment is presented at SVM, ANN, and DL approaches for better detection of Alzheimer’s with PD highlighting future insights.

Rapid Diagnosis of Parkinson’s Disease from Sebum using Paper Spray Ionisation Ion Mobility Mass Spectrometry

2020 ◽  
Author(s):  
Depanjan Sarkar ◽  
Drupad Trivedi ◽  
Eleanor Sinclair ◽  
Sze Hway Lim ◽  
Caitlin Walton-Doyle ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Ion Mobility ◽  
Neurodegenerative Disorder ◽  
Treatment Options ◽  
Ion Mobility Mass Spectrometry ◽  
Paper Spray ◽  
Molecular Features ◽  
Validation Set

Parkinson’s disease (PD) is the second most common neurodegenerative disorder for which identification of robust biomarkers to complement clinical PD diagnosis would accelerate treatment options and help to stratify disease progression. Here we demonstrate the use of paper spray ionisation coupled with ion mobility mass spectrometry (PSI IM-MS) to determine diagnostic molecular features of PD in sebum. PSI IM-MS was performed directly from skin swabs, collected from 34 people with PD and 30 matched control subjects as a training set and a further 91 samples from 5 different collection sites as a validation set. PSI IM-MS elucidates ~ 4200 features from each individual and we report two classes of lipids (namely phosphatidylcholine and cardiolipin) that differ significantly in the sebum of people with PD. Putative metabolite annotations are obtained using tandem mass spectrometry experiments combined with accurate mass measurements. Sample preparation and PSI IM-MS analysis and diagnosis can be performed ~5 minutes per sample offering a new route to for rapid and inexpensive confirmatory diagnosis of this disease.

Visual Effects of Deep Brain Stimulation in a Patient with Parkinson’s Disease

2019 ◽  
pp. 158-173
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Visual System ◽  
Brain Stimulation ◽  
Neurodegenerative Disorder ◽  
Initial Examination ◽  
Before And After ◽  
Deep Brain ◽  
Cover Test

Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder caused by a dopamine deficiency that presents with motor symptoms. Visual disorders can occur concomitantly but are frequently overlooked. Deep brain stimulation (DBS) has been an effective treatment to improve tremors, stiffness and overall mobility, but little is known about its effects on the visual system. Case Report: A 75-year-old Caucasian male with PD presented with longstanding binocular diplopia. On baseline examination, the best-corrected visual acuity was 20/25 in each eye. On observation, he had noticeable tremors with an unsteady gait. Distance alternating cover test showed exophoria with a right hyperphoria. Near alternating cover test revealed a significantly larger exophoria accompanied by a reduced near point of convergence. Additional testing with a 24-2 Humphrey visual field and optical coherence tomography (OCT) of the nerve and macula were unremarkable. The patient underwent DBS implantation five weeks after initial examination, and the device was activated four weeks thereafter. At follow up, the patient still complained of intermittent diplopia. There was no significant change in the manifest refraction or prism correction. On observation, the patient had remarkably improved tremors with a steady gait. All parameters measured were unchanged. The patient was evaluated again seven months after device activation. Although vergence ranges at all distances were improved, the patient was still symptomatic for intermittent diplopia. OCT scans of the optic nerve showed borderline but symmetric thinning in each eye. All other parameters measured were unchanged. Conclusion: The case found no significant changes on ophthalmic examination after DBS implantation and activation in a patient with PD. To the best of the authors’ knowledge, there are no other cases in the literature that investigated the effects of DBS on the visual system pathway in a patient with PD before and after DBS implantation and activation.

Peripheral Immunity, Immunoaging and Neuroinflammation in Parkinson’s Disease

2019 ◽  
Vol 26 (20) ◽  
pp. 3719-3753 ◽  
Author(s):  
Natasa Kustrimovic ◽  
Franca Marino ◽  
Marco Cosentino
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Immune System ◽  
Neurodegenerative Disorder ◽  
Neurodegenerative Process ◽  
Progressive Degeneration ◽  
Cytokine Levels ◽  
Inflammatory Phenotype ◽  
Immune Challenges

:Parkinson’s disease (PD) is the second most common neurodegenerative disorder among elderly population, characterized by the progressive degeneration of dopaminergic neurons in the midbrain. To date, exact cause remains unknown and the mechanism of neurons death uncertain. It is typically considered as a disease of central nervous system (CNS). Nevertheless, numerous evidence has been accumulated in several past years testifying undoubtedly about the principal role of neuroinflammation in progression of PD. Neuroinflammation is mainly associated with presence of activated microglia in brain and elevated levels of cytokine levels in CNS. Nevertheless, active participation of immune system as well has been noted, such as, elevated levels of cytokine levels in blood, the presence of auto antibodies, and the infiltration of T cell in CNS. Moreover, infiltration and reactivation of those T cells could exacerbate neuroinflammation to greater neurotoxic levels. Hence, peripheral inflammation is able to prime microglia into pro-inflammatory phenotype, which can trigger stronger response in CNS further perpetuating the on-going neurodegenerative process.:In the present review, the interplay between neuroinflammation and the peripheral immune response in the pathobiology of PD will be discussed. First of all, an overview of regulation of microglial activation and neuroinflammation is summarized and discussed. Afterwards, we try to collectively analyze changes that occurs in peripheral immune system of PD patients, suggesting that these peripheral immune challenges can exacerbate the process of neuroinflammation and hence the symptoms of the disease. In the end, we summarize some of proposed immunotherapies for treatment of PD.

scholarly journals Evaluation of fecal microbiota transplantation in Parkinson's disease patients with constipation

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Xiao-yi Kuai ◽  
Xiao-han Yao ◽  
Li-juan Xu ◽  
Yu-qing Zhou ◽  
Li-ping Zhang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rating Scale ◽  
Neurodegenerative Disorder ◽  
Fecal Microbiota Transplantation ◽  
Fecal Microbiota ◽  
Motor Symptoms ◽  
Gastrointestinal Dysfunction ◽  
Before And After ◽  
Non Motor Symptoms

AbstractParkinson’s disease (PD) is a neurodegenerative disorder and 70–80% of PD patients suffer from gastrointestinal dysfunction such as constipation. We aimed to assess the efficacy and safety of fecal microbiota transplantation (FMT) for treating PD related to gastrointestinal dysfunction. We conducted a prospective, single- study. Eleven patients with PD received FMT. Fecal samples were collected before and after FMT and subjected to 16S ribosomal DNA (rDNA) gene sequencing. Hoehn-Yahr (H-Y) grade, Unified Parkinson's Disease Rating Scale (UPDRS) score, and the Non-Motion Symptom Questionnaire (NMSS) were used to assess improvements in motor and non-motor symptoms. PAC-QOL score and Wexner constipation score were used to assess the patient's constipation symptoms. All patients were tested by the small intestine breath hydrogen test, performed before and after FMT. Community richness (chao) and microbial structure in before-FMT PD patients were significantly different from the after-FMT. We observed an increased abundance of Blautia and Prevotella in PD patients after FMT, while the abundance of Bacteroidetes decreased dramatically. After FMT, the H-Y grade, UPDRS, and NMSS of PD patients decreased significantly. Through the lactulose H2 breath test, the intestinal bacterial overgrowth (SIBO) in PD patients returned to normal. The PAC-QOL score and Wexner constipation score in after-FMT patients decreased significantly. Our study profiles specific characteristics and microbial dysbiosis in the gut of PD patients. FMT might be a therapeutic potential for reconstructing the gut microbiota of PD patients and improving their motor and non-motor symptoms.

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