scholarly journals A Sensor-Based Perspective in Early-Stage Parkinson’s Disease: Current State and the Need for Machine Learning Processes

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 409
Author(s):  
Marios G. Krokidis ◽  
Georgios N. Dimitrakopoulos ◽  
Aristidis G. Vrahatis ◽  
Christos Tzouvelekis ◽  
Dimitrios Drakoulis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Diagnostic System ◽  
Medical Systems ◽  
Body Protein ◽  
Ensemble Techniques ◽  
Current State

Parkinson’s disease (PD) is a progressive neurodegenerative disorder associated with dysfunction of dopaminergic neurons in the brain, lack of dopamine and the formation of abnormal Lewy body protein particles. PD is an idiopathic disease of the nervous system, characterized by motor and nonmotor manifestations without a discrete onset of symptoms until a substantial loss of neurons has already occurred, enabling early diagnosis very challenging. Sensor-based platforms have gained much attention in clinical practice screening various biological signals simultaneously and allowing researchers to quickly receive a huge number of biomarkers for diagnostic and prognostic purposes. The integration of machine learning into medical systems provides the potential for optimization of data collection, disease prediction through classification of symptoms and can strongly support data-driven clinical decisions. This work attempts to examine some of the facts and current situation of sensor-based approaches in PD diagnosis and discusses ensemble techniques using sensor-based data for developing machine learning models for personalized risk prediction. Additionally, a biosensing platform combined with clinical data processing and appropriate software is proposed in order to implement a complete diagnostic system for PD monitoring.

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.

scholarly journals Machine Learning Methods for Managing Parkinson’s Disease

2019 ◽  
Vol 8 (6S2) ◽  
pp. 965-968
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Stage ◽  
Proper Time ◽  
Learning Algorithms ◽  
Diagnostic System ◽  
Machine Learning Algorithms ◽  
Proper Treatment ◽  
The People

This is a fact that more than one and half million patients are suffering from Parkinson’s disease in the big countries like China, United States, Russia and worldwide is around 6 millions. Even after of many worldwide experiments and research the Parkinson’s disease is an major challenge for biomedical research, scientists and doctors. The problem of this research is that the symptoms of the disease can be investigated in the early and late early age. So that it becomes very difficult to know accurately about this disease. In order to do this research initially some random numbers of features are selected for the research. These features are extracted by many neural network algorithms with minimum redundancy and the maximum similar feature selection. The accuracy of the algorithms results is also a very big concern. It is assumed that the selection algorithms must provide overall 92.3%, precision 21.2% and MC coefficient values of 0.75 & ROC value 0.97%. If such results are achieved then that means it is better than previous research and the work is in improvement process. There are many machine learning algorithms used in different countries based on the research approaches like SVM, DT, PPDM, Artificial intelligence etc. Often the people are aware with the symptoms of this disease so if the proper treatment is given at proper time then the patients may get proper treatment on time and this leads to boost the recovery time. There are many machine learning algorithms and models are under development process which may help to predict the disease in early stage. In this research an automated diagnostic system is introduced. The Multilayer perception, BayesNet and other algorithms are used. This research also provides the observation that such models and methods can help to recover a patient in minimum time because of the early stage prediction of disease.

scholarly journals Plasma-borne indicators of inflammasome activity in Parkinson’s disease patients

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Faith L. Anderson ◽  
Katharine M. von Herrmann ◽  
Angeline S. Andrew ◽  
Yuliya I. Kuras ◽  
Alison L. Young ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cell Death ◽  
Nlrp3 Inflammasome ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Inflammasome Activation ◽  
Membrane Pore ◽  
Related Proteins ◽  
Inflammasome Activity

AbstractParkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms and loss of dopaminergic neurons of the substantia nigra. Inflammation and cell death are recognized aspects of PD suggesting that strategies to monitor and modify these processes may improve the management of the disease. Inflammasomes are pro-inflammatory intracellular pattern recognition complexes that couple these processes. The NLRP3 inflammasome responds to sterile triggers to initiate pro-inflammatory processes characterized by maturation of inflammatory cytokines, cytoplasmic membrane pore formation, vesicular shedding, and if unresolved, pyroptotic cell death. Histologic analysis of tissues from PD patients and individuals with nigral cell loss but no diagnosis of PD identified elevated expression of inflammasome-related proteins and activation-related “speck” formation in degenerating mesencephalic tissues compared with controls. Based on previous reports of circulating inflammasome proteins in patients suffering from heritable syndromes caused by hyper-activation of the NLRP3 inflammasome, we evaluated PD patient plasma for evidence of inflammasome activity. Multiple circulating inflammasome proteins were detected almost exclusively in extracellular vesicles indicative of ongoing inflammasome activation and pyroptosis. Analysis of plasma obtained from a multi-center cohort identified elevated plasma-borne NLRP3 associated with PD status. Our findings are consistent with others indicating inflammasome activity in neurodegenerative disorders. Findings suggest mesencephalic inflammasome protein expression as a histopathologic marker of early-stage nigral degeneration and suggest plasma-borne inflammasome-related proteins as a potentially useful class of biomarkers for patient stratification and the detection and monitoring of inflammation in PD.

Olfactory Function and Diffusion Tensor Imaging as Markers of Mild Cognitive Impairment in Early Stages of Parkinson's Disease

2021 ◽  
pp. 155005942110582
Author(s):  
Sophie A. Stewart ◽  
Laura Pimer ◽  
John D. Fisk ◽  
Benjamin Rusak ◽  
Ron A. Leslie ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Diffusion Tensor Imaging ◽  
Mild Cognitive Impairment ◽  
White Matter ◽  
Neurodegenerative Disorder ◽  
Diffusion Tensor ◽  
Early Stage ◽  
White Matter Integrity

Parkinson's disease (PD) is a neurodegenerative disorder that is typified by motor signs and symptoms but can also lead to significant cognitive impairment and dementia Parkinson's Disease Dementia (PDD). While dementia is considered a nonmotor feature of PD that typically occurs later, individuals with PD may experience mild cognitive impairment (PD-MCI) earlier in the disease course. Olfactory deficit (OD) is considered another nonmotor symptom of PD and often presents even before the motor signs and diagnosis of PD. We examined potential links among cognitive impairment, olfactory functioning, and white matter integrity of olfactory brain regions in persons with early-stage PD. Cognitive tests were used to established groups with PD-MCI and with normal cognition (PD-NC). Olfactory functioning was examined using the University of Pennsylvania Smell Identification Test (UPSIT) while the white matter integrity of the anterior olfactory structures (AOS) was examined using magnetic resonance imaging (MRI) diffusion tensor imaging (DTI) analysis. Those with PD-MCI demonstrated poorer olfactory functioning and abnormalities based on all DTI parameters in the AOS, relative to PD-NC individuals. OD and microstructural changes in the AOS of individuals with PD may serve as additional biological markers of PD-MCI.

Early Detection of Parkinson's Disease

2019 ◽  
pp. 61-95
Author(s):  
Debashree Devi ◽  
Saroj K. Biswas ◽  
Biswajit Purkayastha
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Detection ◽  
Neurodegenerative Disorder ◽  
Diagnostic System ◽  
Diagnostic Model ◽  
Neural Signals ◽  
Learning Machine ◽  
Preprocessing Technique ◽  
Thalamic Region

Parkinson's disease (PD) is a neurodegenerative disorder that occurs due to corrosion of the substantia nigra, located in the thalamic region of the human brain, and is responsible for transmission of neural signals throughout the human body by means of a brain chemical, termed as “dopamine.” Diagnosis of PD is difficult, as it is often affected by the characteristics of the medical data of the patients, which include presence of various indicators, imbalance cases of patients' data records, similar cases of healthy/affected persons, etc. Through this chapter, an intelligent diagnostic system is proposed by integrating one-class SVM, extreme learning machine, and data preprocessing technique. The proposed diagnostic model is validated with six existing techniques and four learning models. The experimental results prove the combination of proposed method with ELM learning model to be highly effective in case of early detection of Parkinson's disease, even in presence of underlying data issues.

scholarly journals Prediction of Parkinson’s Disease at Early Stage using Big Data Analytics

2020 ◽  
Vol 9 (4) ◽  
pp. 2453-2459
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Big Data ◽  
Data Analytics ◽  
Clinical Medicine ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Big Data Analytics ◽  
Analytical Techniques ◽  
Predictive Methods

Due to technological improvements in healthcare industry and clinical medicine, it requires to adapt new software techniques and tools to predict, diagnose and analyze disease patterns for making decisions in the early stage of disease. Parkinson’s disease is a neurodegenerative disorder. The PD damage the motor skills and may create speech problem and also affect the decision making process. Many people suffers with PD all over the world from many years. Day by day, the PD data has been increased, so the existing data mining predictive methods and tools does not give accurate results early for making decisions by doctors to save and increase the patient life period. Early PD symptoms can be detected by Big Data Analytics and proper medicine will be provided at the right time. In this paper, we are doing survey of predictive methods, Big Data Analytical techniques and also earlier researchers results presented.

scholarly journals Machine Learning Prediction of Parkinson's Disease Onset and Subtype Using Germline Variants

2021 ◽  
Author(s):  
Saya R Dennis ◽  
Tanya Simuni ◽  
Yuan Luo
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Neurodegenerative Disorder ◽  
Disease Onset ◽  
The United States ◽  
Machine Learning Algorithms ◽  
Progression Rate ◽  
High Importance ◽  
Germline Variants

Parkinson's Disease is the second most common neurodegenerative disorder in the United States, and is characterized by a largely irreversible worsening of motor and non-motor symptoms as the disease progresses. A prominent characteristic of the disease is its high heterogeneity in manifestation as well as the progression rate. For sporadic Parkinson's Disease, which comprises ~90% of all diagnoses, the relationship between the patient genome and disease onset or progression subtype remains largely elusive. Machine learning algorithms are increasingly adopted to study the genomics of diseases due to their ability to capture patterns within the vast feature space of the human genome that might be contributing to the phenotype of interest. In our study, we develop two machine learning models that predict the onset as well as the progression subtype of Parkinson's Disease based on subjects' germline mutations. Our best models achieved an ROC of 0.77 and 0.61 for disease onset and subtype prediction, respectively. To the best of our knowledge, our models present state-of-the-art prediction performances of PD onset and subtype solely based on the subjects' germline variants. The genes with high importance in our best-performing models were enriched for several canonical pathways related to signaling, immune system, and protein modifications, all of which have been previously associated with PD symptoms or pathogenesis. These high-importance gene sets provide us with promising candidate genes for future biomedical and clinical research.

scholarly journals Identification of an early-stage Parkinson’s disease neuromarker using event-related potentials, brain network analytics and machine-learning

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0261947
Author(s):  
Sharon Hassin-Baer ◽  
Oren S. Cohen ◽  
Simon Israeli-Korn ◽  
Gilad Yahalom ◽  
Sandra Benizri ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Stage ◽  
Event Related Potentials ◽  
Brain Network ◽  
Cognitive Tasks ◽  
Network Analytics ◽  
Related Potentials ◽  
Advanced Analysis

Objective The purpose of this study is to explore the possibility of developing a biomarker that can discriminate early-stage Parkinson’s disease from healthy brain function using electroencephalography (EEG) event-related potentials (ERPs) in combination with Brain Network Analytics (BNA) technology and machine learning (ML) algorithms. Background Currently, diagnosis of PD depends mainly on motor signs and symptoms. However, there is need for biomarkers that detect PD at an earlier stage to allow intervention and monitoring of potential disease-modifying therapies. Cognitive impairment may appear before motor symptoms, and it tends to worsen with disease progression. While ERPs obtained during cognitive tasks performance represent processing stages of cognitive brain functions, they have not yet been established as sensitive or specific markers for early-stage PD. Methods Nineteen PD patients (disease duration of ≤2 years) and 30 healthy controls (HC) underwent EEG recording while performing visual Go/No-Go and auditory Oddball cognitive tasks. ERPs were analyzed by the BNA technology, and a ML algorithm identified a combination of features that distinguish early PD from HC. We used a logistic regression classifier with a 10-fold cross-validation. Results The ML algorithm identified a neuromarker comprising 15 BNA features that discriminated early PD patients from HC. The area-under-the-curve of the receiver-operating characteristic curve was 0.79. Sensitivity and specificity were 0.74 and 0.73, respectively. The five most important features could be classified into three cognitive functions: early sensory processing (P50 amplitude, N100 latency), filtering of information (P200 amplitude and topographic similarity), and response-locked activity (P-200 topographic similarity preceding the motor response in the visual Go/No-Go task). Conclusions This pilot study found that BNA can identify patients with early PD using an advanced analysis of ERPs. These results need to be validated in a larger PD patient sample and assessed for people with premotor phase of PD.

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