Identification of Early-stage Parkinson's Disease Utilizing Graph Theory and Machine Learning

2019 ◽  
Author(s):  
Yan Yan ◽  
Jing Ai ◽  
Tiantian Liu ◽  
Tianyi Yan
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Graph Theory ◽  
Early Stage

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.

scholarly journals A Study On Deep Learning And Machine Learning Techniques On Detection Of Parkinson’s Disease

2021 ◽  
Vol 309 ◽  
pp. 01008
Author(s):  
P. Mounika ◽  
S. Govinda Rao
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Early Stage ◽  
Signs And Symptoms ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Machine Learning Methods ◽  
Learning Techniques

Parkinson’s disease (PD) is a sophisticated anxiety malady that impairs movement. Symptoms emerge gradually, initiating with a slight tremor in only one hand occasionally. Tremors are prevalent, although the condition is sometimes associated with stiffness or slowed mobility. In the early degrees of PD, your face can also additionally display very little expression. Your fingers won’t swing while you walk. Your speech can also additionally grow to be gentle or slurred. PD signs and symptoms get worse as your circumstance progresses over time. The goal of this study is to test the efficiency of deep learning and machine learning approaches in order to identify the most accurate strategy for sensing Parkinson’s disease at an early stage. In order to measure the average performance most accurately, we compared deep learning and machine learning methods.

scholarly journals Oscillation-specific nodal alterations in early to middle stages Parkinson’s disease

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Xiaojun Guan ◽  
Tao Guo ◽  
Qiaoling Zeng ◽  
Jiaqiu Wang ◽  
Cheng Zhou ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Graph Theory ◽  
Oscillation Frequency ◽  
Large Scale ◽  
Early Stage ◽  
Degree Centrality ◽  
Middle Stage ◽  
Network Analyses ◽  
Oscillation Frequencies

Abstract Background Different oscillations of brain networks could carry different dimensions of brain integration. We aimed to investigate oscillation-specific nodal alterations in patients with Parkinson’s disease (PD) across early stage to middle stage by using graph theory-based analysis. Methods Eighty-eight PD patients including 39 PD patients in the early stage (EPD) and 49 patients in the middle stage (MPD) and 36 controls were recruited in the present study. Graph theory-based network analyses from three oscillation frequencies (slow-5: 0.01–0.027 Hz; slow-4: 0.027–0.073 Hz; slow-3: 0.073–0.198 Hz) were analyzed. Nodal metrics (e.g. nodal degree centrality, betweenness centrality and nodal efficiency) were calculated. Results Our results showed that (1) a divergent effect of oscillation frequencies on nodal metrics, especially on nodal degree centrality and nodal efficiency, that the anteroventral neocortex and subcortex had high nodal metrics within low oscillation frequencies while the posterolateral neocortex had high values within the relative high oscillation frequency was observed, which visually showed that network was perturbed in PD; (2) PD patients in early stage relatively preserved nodal properties while MPD patients showed widespread abnormalities, which was consistently detected within all three oscillation frequencies; (3) the involvement of basal ganglia could be specifically observed within slow-5 oscillation frequency in MPD patients; (4) logistic regression and receiver operating characteristic curve analyses demonstrated that some of those oscillation-specific nodal alterations had the ability to well discriminate PD patients from controls or MPD from EPD patients at the individual level; (5) occipital disruption within high frequency (slow-3) made a significant influence on motor impairment which was dominated by akinesia and rigidity. Conclusions Coupling various oscillations could provide potentially useful information for large-scale network and progressive oscillation-specific nodal alterations were observed in PD patients across early to middle stages.

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 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.

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.

A Systematic Review of Machine Learning Based Gait characteristics in Parkinson’s disease

2021 ◽  
Vol 21 ◽  
Author(s):  
Pooja Sharma ◽  
SK Pahuja ◽  
Karan Veer
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rating Scales ◽  
Early Stage ◽  
Machine Learning Classification ◽  
Assessment Development ◽  
Gait Characteristics ◽  
Time Period

Objective: Parkinson’s disease is a pervasive neuro disorder that affects people's quality of life throughout the world. The unsatisfactory results of clinical rating scales open the door for more research. PD treatment using current biomarkers seems a difficult task. So automatic evaluation at an early stage may enhance the quality and time-period of life. Methods: Grading of Recommendations Assessment, Development, and Evaluation (GRADE) and Population, intervention, comparison, and outcome (PICO) search methodology schemes are followed to search the data and eligible studies for this survey. Approximate 1500 articles were extracted using related search strings. After the stepwise mapping and elimination of studies, 94 papers are found suitable for the present review. Results: After the quality assessment of extracted studies, nine inhibitors are identified to analyze people's gait with Parkinson’s disease, where four are critical. This review also differentiates the various machine learning classification techniques with their PD analysis characteristics in previous studies. The extracted research gaps are described as future perspectives. Results can help practitioners understand the PD gait as a valuable biomarker for detection, quantification, and classification. Conclusion: Due to less cost and easy recording of gait, gait-based techniques are becoming popular in PD detection. By encapsulating the gait-based studies, it gives an in-depth knowledge of PD, different measures that affect gait detection and classification.

Association of the Non-Motor Burden with Patterns of Striatal Dopamine Loss in de novo Parkinson’s Disease

2020 ◽  
Vol 10 (4) ◽  
pp. 1541-1549
Author(s):  
Seok Jong Chung ◽  
Sangwon Lee ◽  
Han Soo Yoo ◽  
Yang Hyun Lee ◽  
Hye Sun Lee ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
De Novo ◽  
Early Stage ◽  
Striatal Dopamine ◽  
Motor Deficits ◽  
Dopamine Depletion ◽  
Severe Motor ◽  
Severe Group ◽  
Striatal Dopamine Depletion

Background: Striatal dopamine deficits play a key role in the pathogenesis of Parkinson’s disease (PD), and several non-motor symptoms (NMSs) have a dopaminergic component. Objective: To investigate the association between early NMS burden and the patterns of striatal dopamine depletion in patients with de novo PD. Methods: We consecutively recruited 255 patients with drug-naïve early-stage PD who underwent 18F-FP-CIT PET scans. The NMS burden of each patient was assessed using the NMS Questionnaire (NMSQuest), and patients were divided into the mild NMS burden (PDNMS-mild) (NMSQuest score <6; n = 91) and severe NMS burden groups (PDNMS-severe) (NMSQuest score >9; n = 90). We compared the striatal dopamine transporter (DAT) activity between the groups. Results: Patients in the PDNMS-severe group had more severe parkinsonian motor signs than those in the PDNMS-mild group, despite comparable DAT activity in the posterior putamen. DAT activity was more severely depleted in the PDNMS-severe group in the caudate and anterior putamen compared to that in the PDMNS-mild group. The inter-sub-regional ratio of the associative/limbic striatum to the sensorimotor striatum was lower in the PDNMS-severe group, although this value itself lacked fair accuracy for distinguishing between the patients with different NMS burdens. Conclusion: This study demonstrated that PD patients with severe NMS burden exhibited severe motor deficits and relatively diffuse dopamine depletion throughout the striatum. These findings suggest that the level of NMS burden could be associated with distinct patterns of striatal dopamine depletion, which could possibly indicate the overall pathological burden in PD.

Gene expression analysis in modeling Parkinson’s disease

2020 ◽  
pp. 103-104
Author(s):  
М.М. Руденок ◽  
А.Х. Алиева ◽  
А.А. Колачева ◽  
М.В. Угрюмов ◽  
П.А. Сломинский ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Stage ◽  
Systemic Disease ◽  
Molecular Genetic ◽  
Presymptomatic Stage ◽  
Whole Transcriptome Analysis ◽  
Whole Transcriptome ◽  
The Brain ◽  
Transcriptome Level

Несмотря на очевидный прогресс, достигнутый в изучении молекулярно-генетических факторов и механизмов патогенеза болезни Паркинсона (БП), в настоящее время стало ясно, что нарушения в структуре ДНК не описывают весь спектр патологических изменений, наблюдаемых при развитии заболевания. В настоящее время показано, что существенное влияние на патогенез БП могут оказывать изменения на уровне транскриптома. В работе были использованы мышиные модели досимптомной стадии БП, поздней досимптомной и ранней симптомной (РСС) стадиями БП. Для полнотранскриптомного анализа пулов РНК тканей черной субстанции и стриатума мозга мышей использовались микрочипы MouseRef-8 v2.0 Expression BeadChip Kit («Illumina», США). Полученные данные указывают на последовательное вовлечение транскриптома в патогенез БП, а также на то, что изменения на транскриптомном уровне процессов транспорта и митохондриального биогенеза могут играть важную роль в нейродегенерации при БП уже на самых ранних этапах. Parkinson’s disease (PD) is a complex systemic disease, mainly associated with the death of dopaminergic neurons. Despite the obvious progress made in the study of molecular genetic factors and mechanisms of PD pathogenesis, it has now become clear that violations in the DNA structure do not describe the entire spectrum of pathological changes observed during the development of the disease. It has now been shown that changes at the transcriptome level can have a significant effect on the pathogenesis of PD. The authors used models of the presymptomatic stage of PD with mice decapitation after 6 hours (6 h-PSS), presymptomatic stage with decapitation after 24 hours (24 h-PSS), advanced presymptomatic (Adv-PSS) and early symptomatic (ESS) stages of PD. For whole transcriptome analysis of RNA pools of the substantia nigra and mouse striatum, the MouseRef-8 v2.0 Expression BeadChip Kit microchips (Illumina, USA) were used. As a result of the analysis of whole transcriptome data, it was shown that, there are a greater number of statistically significant changes in the tissues of the brain and peripheral blood of mice with Adv-PSS and ESS models of PD compared to 6 h-PSS and 24 h-PSS models. In general, the obtained data indicate the sequential involvement of the transcriptome in the pathogenesis of PD, as well as the fact that changes at the transcriptome level of the processes of transport and mitochondrial biogenesis can play an important role in neurodegeneration in PD at an early stage.

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