Parkinson's Disease Classification Research Articles

Metaheuristics with Deep Learning-Enabled Parkinson's Disease Diagnosis and Classification Model.

Parkinson's disease (PD) affects the movement of people, including the differences in writing skill, speech, tremor, and stiffness in muscles. It is significant to detect the PD at the initial stages so that the person can live a peaceful life for a longer time period. The serious levels of PD are highly risky as the patients get progressive stiffness, which results in the inability of standing or walking. Earlier studies have focused on the detection of PD effectively using voice and speech exams and writing exams. In this aspect, this study presents an improved sailfish optimization algorithm with deep learning (ISFO-DL) model for PD diagnosis and classification. The presented ISFO-DL technique uses the ISFO algorithm and DL model to determine PD and thereby enhances the survival rate of the person. The presented ISFO is a metaheuristic algorithm, which is inspired by a group of hunting sailfish to determine the optimum solution to the problem. Primarily, the ISFO algorithm is applied to derive an optimal subset of features with a fitness function of maximum classification accuracy. At the same time, the rat swarm optimizer (RSO) with the bidirectional gated recurrent unit (BiGRU) is employed as a classifier to determine the existence of PD. The performance validation of the IFSO-DL model takes place using a benchmark Parkinson's dataset, and the results are inspected under several dimensions. The experimental results highlighted the enhanced classification performance of the ISFO-DL technique, and therefore, the proposed model can be employed for the earlier identification of PD.

An optimal dimension reduction strategy and experimental evaluation for Parkinson's disease classification

The amount of data streamed and generated through various healthcare systems is exponentially increasing day by day. Applying traditional data mining algorithms on this massive sized data to construct automated decision support systems is a tedious and time consuming task. In recent years, there has been increasing interest in the development of telediagnosis and telemonitoring systems for Parkinson's disease (PD). Parkinson's disease is a progressive neurodegenerative disease which affect the movement characteristics. PD patients commonly face vocal impairments during the early stages of the disease. This work proposes a computationally efficient method for dimension reduction and classification of healthcare related data. The devised framework is capable to deal with the data having discrete as well as continuous natured features. The experimental evaluation is performed on Parkinson's disease classification database (Sakar et al., 2018). The statistical performance metrices used are - validation and test accuracy, precision, recall, F1-score, etc. There will be computational complexity advantages when this reduced dimension data is further processed for modelling and building prediction system. In order to prove the optimality of proposed framework, comparative analysis is performed with the significant existing approaches.

PD-ResNet for Classification of Parkinson's Disease From Gait.

Objective: To develop an objective and efficient method to automatically identify Parkinson’s disease (PD) and healthy control (HC). Methods: We design a novel model based on residual network (ResNet) architecture, named PD-ResNet, to learn the gait differences between PD and HC and between PD with different severity levels. Specifically, a polynomial elevated dimensions technique is applied to increase the dimensions of the input gait features; then, the processed data is transformed into a 3-dimensional picture as the input of PD-ResNet. The synthetic minority over-sampling technique (SMOTE), data augmentation, and early stopping technologies are adopted to improve the generalization ability. To further enhance the classification performance, a new loss function, named improved focal loss function, is developed to focus on the train of PD-ResNet on the hard samples and to discard the abnormal samples. Results: The experiments on the clinical gait dataset show that our proposed model achieves excellent performance with an accuracy of 95.51%, a precision of 94.44%, a recall of 96.59%, a specificity of 94.44%, and an F1-score of 95.50%. Moreover, the accuracy, precision, recall, specificity, and F1-score for the classification of early PD and HC are 92.03%, 94.20%, 90.28%, 93.94%, and 92.20%, respectively. Furthermore, the accuracy, precision, recall, specificity, and F1-score for the classification of PD with different severity levels are 92.03%, 94.29%, 90.41%, 93.85%, and 92.31%, respectively. Conclusion: Our proposed method shows better performance than the traditional machine learning and deep learning methods. Clinical impact: The experimental results show that the proposed method is clinically meaningful for the objective assessment of gait motor impairment for PD patients.

Radiomics for Parkinson's Disease Classification Using Advanced Texture-Based Biomarkers

Radiomics for Parkinson's Disease Classification Using Advanced Texture-Based Biomarkers

Classification of Parkinson Disease Based on Patient’s Voice Signal Using Machine Learning

Parkinson’s disease (PD) is a nervous system disorder first described as a neurological condition in 1817. It is one of the more prevalent diseases in the elderly, and Alzheimer’s is the second most common neurodegenerative illness. It impacts the patient’s movement. Symptoms start gradually with tremors, stiffness in movement, and speech and voice disorders. Researches proved that 89% of patients with Parkinson’s has speech disorder including uncertain articulation, hoarse and breathy voice and monotone pitch. The cause behind this voice change is the reduction of dopamine due to damage of neurons in the substantia nigra responsible for dopamine production. In this work, Parkinson’s disease is classified with the help of human voice signals. Six different machine learning (ML) algorithms are used in the classification: Stochastic Gradient Descent (SGD) Classifier, Extreme Gradient Boosting (XGB) Classifier, Logistic Regression Classifier, Random Forest Classifier, K-Nearest Neighbour (KNN) Classifier, and Decision Tree (DT) Classifier. This research aims to classify Parkinson’s disease using human voice signals and extract essential features to reduce the complexity of the dataset. Then, human voice signals are analyzed to check the voice intensity and spectrum for PD patients. Then, machine learning classifiers are applied to classify the PD patients based on the extracted features. The results show that SGD-Classifier has 91% accuracy, XGB-Classifier has 95% accuracy, Logistic Regression has 91% accuracy, Random Forest shows 97% accuracy, KNN shows 95% accuracy, and Decision Tree has 95% accuracy. Hence, Random Forest has the highest accuracy. The disease can be studied more by looking for more characteristics of PD patients to enhance its proper use in the medical field.

Classification of Parkinson\u2019s disease with freezing of gait based on 360\xb0 turning analysis using 36 kinematic features

BackgroundFreezing of gait (FOG) is a sensitive problem, which is caused by motor control deficits and requires greater attention during postural transitions such as turning in people with Parkinson’s disease (PD). However, the turning characteristics have not yet been extensively investigated to distinguish between people with PD with and without FOG (freezers and non-freezers) based on full-body kinematic analysis during the turning task. The objectives of this study were to identify the machine learning model that best classifies people with PD and freezers and reveal the associations between clinical characteristics and turning features based on feature selection through stepwise regression.MethodsThe study recruited 77 people with PD (31 freezers and 46 non-freezers) and 34 age-matched older adults. The 360° turning task was performed at the preferred speed for the inner step of the more affected limb. All experiments on the people with PD were performed in the “Off” state of medication. The full-body kinematic features during the turning task were extracted using the three-dimensional motion capture system. These features were selected via stepwise regression.ResultsIn feature selection through stepwise regression, five and six features were identified to distinguish between people with PD and controls and between freezers and non-freezers (PD and FOG classification problem), respectively. The machine learning model accuracies revealed that the random forest (RF) model had 98.1% accuracy when using all turning features and 98.0% accuracy when using the five features selected for PD classification. In addition, RF and logistic regression showed accuracies of 79.4% when using all turning features and 72.9% when using the six selected features for FOG classification.ConclusionWe suggest that our study leads to understanding of the turning characteristics of people with PD and freezers during the 360° turning task for the inner step of the more affected limb and may help improve the objective classification and clinical assessment by disease progression using turning features.

3D convolutional neural networks-based multiclass classification of Alzheimer\u2019s and Parkinson\u2019s diseases using PET and SPECT neuroimaging modalities

BackgroundAlzheimer’s disease (AD) is a neurodegenerative brain pathology formed due to piling up of amyloid proteins, development of plaques and disappearance of neurons. Another common subtype of dementia like AD, Parkinson’s disease (PD) is determined by the disappearance of dopaminergic neurons in the region known as substantia nigra pars compacta located in the midbrain. Both AD and PD target aged population worldwide forming a major chunk of healthcare costs. Hence, there is a need for methods that help in the early diagnosis of these diseases. PD subjects especially those who have confirmed postmortem plaque are a strong candidate for a second AD diagnosis. Modalities such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) can be combined with deep learning methods to diagnose these two diseases for the benefit of clinicians.ResultIn this work, we deployed a 3D Convolutional Neural Network (CNN) to extract features for multiclass classification of both AD and PD in the frequency and spatial domains using PET and SPECT neuroimaging modalities to differentiate between AD, PD and Normal Control (NC) classes. Discrete Cosine Transform has been deployed as a frequency domain learning method along with random weak Gaussian blurring and random zooming in/out augmentation methods in both frequency and spatial domains. To select the hyperparameters of the 3D-CNN model, we deployed both 5- and 10-fold cross-validation (CV) approaches. The best performing model was found to be AD/NC(SPECT)/PD classification with random weak Gaussian blurred augmentation in the spatial domain using fivefold CV approach while the worst performing model happens to be AD/NC(PET)/PD classification without augmentation in the frequency domain using tenfold CV approach. We also found that spatial domain methods tend to perform better than their frequency domain counterparts.ConclusionThe proposed model provides a good performance in discriminating AD and PD subjects due to minimal correlation between these two dementia types on the clinicopathological continuum between AD and PD subjects from a neuroimaging perspective.

Evaluation of Recurrent Neural Network Models for Parkinson's Disease Classification Using Drawing Data.

Parkinson's disease is a disorder that affects the neurons in the human brain. The various symptoms include slowness of motor functions (bradykinesia), motor instability, speech impairment and in some cases, psychiatric effects such as hallucinations. Most of these, however, are also common side effects of natural aging. This makes an accurate diagnosis of Parkinson's disease a challenging task. Some breakthroughs have been made in recent years with the help of deep learning. This work aims at considering figure drawing data as a time series of coordinates, angles and pressure readings to train recurrent neural network models. In addition, the work compares two recurrent network models, Long Short-Term Memory and Echo State Networks, to explore the advantages and disadvantages of both architectures.

Visualising and quantifying relevant parkinsonian gait patterns using 3D convolutional network

Parkinson’s disease (PD) lacks a definitive diagnosis, with the observation of motion patterns being the main method of characterizing disease progression and planning patient treatments. Among PD observations, gait motion patterns, such as step length, flexed posture, and bradykinesia, support the characterization of disease progression. However, this analysis is usually performed with marker-based protocols, which affect the gait and localized segment patterns during locomotion. This work introduces a 3D convolutional gait representation for automatic PD classification that identifies the spatio-temporal patterns used for classification. This approach allows us to obtain an explainable model that classifies markerless sequences and describes the main learned spatio-temporal regions associated with abnormal patterns in a particular video. Initially, a spatio-temporal convolutional network is trained from a set of raw videos and optical flow fields. Then, a PD prediction is obtained from the motion patterns learned by the trained model. Finally, saliency maps, which highlight abnormal motion patterns, are obtained by retro-propagating the output prediction up to the input volume through two different stages: an embedded back-tracking and a pseudo-deconvolution process. From a total of 176 videos from 22 patients, the resulting salient maps highlight lower limb patterns possibly related to step length and speed. In control subjects, the saliency maps highlight the head and trunk posture. The proposed approach achieved an average accuracy score of 94.89%.

Classification of Parkinson disease based on analysis and synthesis of voice signal

The most important application of voice profiling is pathological voice detection. Parkinson's disease is a chronic neurological degenerative disease affecting the central nervous system responsible for essentially progressive evolution movement disorders. 70% to 90% of Parkinson’s disease (PD) patients show an affected voice. This paper proposes a methodology for PD based on acoustic, glottal, physical, and electrical parameters. The results show that the acoustic parameter is more important in the case of Parkinson’s disease as compared to glottal and physical parameters. The authors achieved 97.2% accuracy to differentiate Parkinson and healthy voice using jitter to pitch ratio proposed algorithm. The Authors also proposed an algorithm of poles calculation of the vocal tract to find formants of the vocal tract. Further, formants are used for finding the transfer function of vocal tract filter. In the end, the authors suggested parameters of the electrical vocal tract model are also changed in the case of PD voices.

Classification of Parkinson disease based on analysis and synthesis of voice signal

The most important application of voice profiling is pathological voice detection. Parkinson's disease is a chronic neurological degenerative disease affecting the central nervous system responsible for essentially progressive evolution movement disorders. 70% to 90% of Parkinson’s disease (PD) patients show an affected voice. This paper proposes a methodology for PD based on acoustic, glottal, physical, and electrical parameters. The results show that the acoustic parameter is more important in the case of Parkinson’s disease as compared to glottal and physical parameters. The authors achieved 97.2% accuracy to differentiate Parkinson and healthy voice using jitter to pitch ratio proposed algorithm. The Authors also proposed an algorithm of poles calculation of the vocal tract to find formants of the vocal tract. Further, formants are used for finding the transfer function of vocal tract filter. In the end, the authors suggested parameters of the electrical vocal tract model are also changed in the case of PD voices.

Classification of Parkinson Disease Based on Analysis and Synthesis of Voice Signal

The most important application of voice profiling is pathological voice detection. Parkinson's disease is a chronic neurological degenerative disease affecting the central nervous system responsible for essentially progressive evolution movement disorders. 70% to 90% of Parkinson’s disease (PD) patients show an affected voice. This paper proposes a methodology for PD based on acoustic, glottal, physical, and electrical parameters. The results show that the acoustic parameter is more important in the case of Parkinson’s disease as compared to glottal and physical parameters. The authors achieved 97.2% accuracy to differentiate Parkinson and healthy voice using jitter to pitch ratio proposed algorithm. The Authors also proposed an algorithm of poles calculation of the vocal tract to find formants of the vocal tract. Further, formants are used for finding the transfer function of vocal tract filter. In the end, the authors suggested parameters of the electrical vocal tract model are also changed in the case of PD voices.

A hybrid deep transfer learning-based approach for Parkinson's disease classification in surface electromyography signals

Parkinson's disease (PD) is known as a rampant neurodegenerative disorder, which has afflicted approximately 10 million people throughout the world. Surface Electromyography (sEMG) signal trials received from the upper extremities, such as the arm and wrist, would be an efficient way to assess neuromuscular function in the detection of PD. This paper mainly aimed to utilize pre-trained deep transfer learning (DTL) structures and conventional machine learning (ML) models as an automated approach to diagnose PD from sEMG signals. Primarily, we stacked the extracted features from three deep pre-trained architectures, including AlexNet, VGG-f, and CaffeNet, to generate the discriminative feature vectors. Although the number of stacked features from all the three deep structures was large, the proper features is effective in overcoming the challenge of over-fitting as well as increasing the robustness to added noise with different levels. Subsequently, we proposed a novel soft combination of subset feature selection methods, including receiver operating characteristic (ROC), entropy, and the signal-to-noise (SNR) procedures, in order to reduce the size of the extracted features. Finally, we utilized the support vector machine (SVM) with radial basis function (RBF) kernel for identifying PD disorder. The experimental results in different analysis frameworks illustrated that the hybrid deep transfer learning-based approach to PD classification could lead to hitting rates higher than 99%. Moreover, it can be of a competitive performance with the state-of-the-art SVM-based pattern even though the suggested model needs minimal processing in feature construction of sEMG signals to PD detection.

Clustered regularly interspaced short palindromic repeats as an advanced treatment for Parkinson's disease.

Recently, genome‐editing technology like clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 has improved the translational gap in the treatments mediated through gene therapy. The advantages of the CRISPR system, such as, work in the living cells and tissues, candidate this technique for the employing in experiments and the therapy of central nervous system diseases. Parkinson's disease (PD) is a widespread, disabling, neurodegenerative disease induced by dopaminergic neuron loss and linked to progressive motor impairment. Pathophysiological basis knowledge of PD has modified the PD classification model and expresses in the sporadic and familial types. Analyses of the earliest genetic linkage have shown in PD the inclusion of synuclein alpha (SNCA) genomic duplication and SNCA mutations in the familial types of PD pathogenesis. This review analyzes the structure, development, and function in genome editing regulated through the CRISPR/Cas9. Also, it explains the genes associated with PD pathogenesis and the appropriate modifications to favor PD. This study follows the direction by understanding the PD linking analyses in which the CRISPR technique is applied. Finally, this study explains the limitations and future trends of CRISPR service in relation to the genome‐editing process in PD patients' induced pluripotent stem cells.

Radiomics on routine T1-weighted MRI can delineate Parkinson's disease from multiple system atrophy and progressive supranuclear palsy.

This study aimed to explore the feasibility of radiomics features extracted from T1-weighted MRI images to differentiate Parkinson's disease (PD) from atypical parkinsonian syndromes (APS). Radiomics features were computed from T1 images of 65 patients with PD, 61 patients with APS (31: progressive supranuclear palsy and 30: multiple system atrophy), and 75 healthy controls (HC). These features were extracted from 19 regions of interest primarily from subcortical structures, cerebellum, and brainstem. Separate random forest classifiers were applied to classify different groups based on a reduced set of most important radiomics features for each classification as determined by the random forest-based recursive feature elimination by cross-validation method. The PD vs HC classifier illustrated an accuracy of 70%, while the PD vs APS classifier demonstrated a superior test accuracy of 92%. Moreover, a 3-way PD/MSA/PSP classifier performed with 96% accuracy. While first-order and texture-based differences like Gray Level Co-occurrence Matrix (GLCM) and Gray Level Difference Matrix for the substantia nigra pars compacta and thalamus were highly discriminative for PD vs HC, textural features mainly GLCM of the ventral diencephalon were highlighted for APS vs HC, and features extracted from the ventral diencephalon and nucleus accumbens were highlighted for the classification of PD and APS. This study establishes the utility of radiomics to differentiate PD from APS using routine T1-weighted images. This may aid in the clinical diagnosis of PD and APS which may often be indistinguishable in early stages of disease. • Radiomics features were extracted from T1-weighted MRI images. • Parkinson's disease and atypical parkinsonian syndromes were classified at an accuracy of 92%. • This study establishes the utility of radiomics to differentiate Parkinson's disease and atypical parkinsonian syndromes using routine T1-weighted images.

Machine learning approach for classification of Parkinson disease using acoustic features

Machine learning approach for classification of Parkinson disease using acoustic features

Disrupted morphological grey matter networks in early-stage Parkinson\u2019s disease

While previous structural-covariance studies have an advanced understanding of brain alterations in Parkinson's disease (PD), brain–behavior relationships have not been examined at the individual level. This study investigated the topological organization of grey matter (GM) networks, their relation to disease severity, and their potential imaging diagnostic value in PD. Fifty-four early-stage PD patients and 54 healthy controls (HC) underwent structural T1-weighted magnetic resonance imaging. GM networks were constructed by estimating interregional similarity in the distributions of regional GM volume using the Kullback–Leibler divergence measure. Results were analyzed using graph theory and network-based statistics (NBS), and the relationship to disease severity was assessed. Exploratory support vector machine analyses were conducted to discriminate PD patients from HC and different motor subtypes. Compared with HC, GM networks in PD showed a higher clustering coefficient (P = 0.014) and local efficiency (P = 0.014). Locally, nodal centralities in PD were lower in postcentral gyrus and temporal-occipital regions, and higher in right superior frontal gyrus and left putamen. NBS analysis revealed decreased morphological connections in the sensorimotor and default mode networks and increased connections in the salience and frontoparietal networks in PD. Connection matrices and graph-based metrics allowed single-subject classification of PD and HC with significant accuracy of 73.1 and 72.7%, respectively, while graph-based metrics allowed single-subject classification of tremor-dominant and akinetic–rigid motor subtypes with significant accuracy of 67.0%. The topological organization of GM networks was disrupted in early-stage PD in a way that suggests greater segregation of information processing. There is potential for application to early imaging diagnosis.

WITHDRAWN: A benchmarking study of Parkinson's disease classification base on speech symptom features

In numerous prominent fields, such as e-business, communications, retail, and medical, information exploration in databases has built its success rate. The mining of medical data has immense potential to investigate the out-of-sight trends in the respective medical databases. This paper aims to provide a benchmarking analysis of the classification basis for Parkinson's disease (PD) on speech symptoms utilizing a data mining approach. The Mentation, Tasks of Everyday Life (ADL), Motor Analysis, and Therapy Symptoms will captivate various PD main signs. The speech symptom that is an ADL is a frequent cause for the disease's advancement. The dataset PD classification is accessed from UCI, an open archive of broad datasets of machine learning (ML). In this paper, a comparative review is carried out on various classification algorithms by adding the feature significance analysis and the accuracy analysis to the best classification algorithm. Subsequently, a collection of ML algorithms to classify PD are Averaged Perceptron (AP), Bayes Point Ma-chine (BPM), Boosted Decision Trees (BDT), Convolutional Neural Network (CNN), Decision Forest (DF), Logistic Regression (LR), Neural Network (NN), and Support Vector Machine (SVM). Each algorithm is tested and evaluated via a 5-fold cross-validation approach. The results show that the BDT achieves the highest accuracy of 90.03%, precision of 93.81%, recall of 93.32%, and AUC of 95.67. It outperforms all the other seven tested ML classifiers; nevertheless, the other classifiers also manage to produce high PD diagnosis results, which indicates the suitability of the speech analysis approach in PD diagnosis.

Deep brain stimulation outcomes in the malignant end of Parkinson's disease spectrum

BackgroundHeterogeneity of Parkinson's Disease (PD) phenotype may influence deep brain stimulation (DBS) outcome. However, DBS response in the malignant end of the PD spectrum has been poorly investigated. ObjectiveTo evaluate and compare DBS outcomes in malignant and benign PD patients, defined according to motor and non-motor symptom presentation at the presurgical selection. MethodsWe categorized a cohort of 154 parkinsonian patients fulfilling criteria for subthalamic nucleus (STN)-DBS into malignant, benign, and intermediate subtypes, according to a recently validated clinical PD classification. DBS efficacy on daily living independence (Schwab and England –S&E-score ≥70%), motor symptoms, and motor fluctuations (Unified Parkinson's Disease Rating Scale -UPDRS- part-III and -IV, and Ambulatory Capacity Measure) were compared between malignant and benign patients, using corrected binary logistic regressions and repeated measure general linear model. ResultsOne year after surgery, the probability of losing daily life independence was 16-fold higher in malignant patients, even after adjusting for age at PD onset, PD duration, and percentage of motor improvement after STN-DBS (OR: 16.233; p: 0.035). Conversely, malignant and benign patients showed a similar extent of improvement after STN-DBS (p > 0.05) in motor symptoms, motor fluctuations, and ambulatory capacity, both in medication-ON and medication-OFF conditions. ConclusionDBS candidates in the malignant end of the PD spectrum may profit from a similar improvement of motor symptoms and fluctuations after STN-DBS when compared to benign PD. However, patients of the malignant group have a lower probability of maintaining independence in daily life early after surgery.

Detection of Parkinson's Disease Through Automated Pupil Tracking of the Post-illumination Pupillary Response.

Parkinson's disease (PD) is one of the most common neurodegenerative disorders, but it is often diagnosed after the majority of dopaminergic cells are already damaged. It is critical to develop biomarkers to identify the disease as early as possible for early intervention. PD patients appear to have an altered pupillary response consistent with an abnormality in photoreceptive retinal ganglion cells. Tracking the pupil size manually is a tedious process and offline automated systems can be prone to errors that may require intervention; for this reason in this work we describe a system for pupil size estimation with a user interface to allow rapid adjustment of parameters and extraction of pupil parameters of interest for the present study. We implemented a user-friendly system designed for clinicians to automate the process of tracking the pupil diameter to measure the post-illumination pupillary response (PIPR), permit manual corrections when needed, and continue automation after correction. Tracking was automated using a Kalman filter estimating the pupil center and diameter over time. The resulting system was tested on a PD classification task in which PD subjects are known to have similar responses for two wavelengths of light. The pupillary response is measured in the contralateral eye to two different light stimuli (470 and 610 nm) for 19 PD and 10 control subjects. The measured Net PIPR indicating different responsiveness to the wavelengths was 0.13 mm for PD subjects and 0.61 mm for control subjects, demonstrating a highly significant difference (p < 0.001). Net PIPR has the potential to be a biomarker for PD, suggesting further study to determine clinical validity.