PRESENTING A NEW DECISION SUPPORT SYSTEM FOR SCREENING PARKINSON’S DISEASE PATIENTS USING SYMLET WAVELET

2019 ◽  
Vol 31 (04) ◽  
pp. 1950026
Author(s):  
Yashar Sarbaz ◽  
Behzad Abedi
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Decision Support ◽  
Healthy Subjects ◽  
Disease Diagnosis ◽  
Gait Disorders ◽  
Support Vector ◽  
Double Support ◽  
Number Of Patients ◽  
Classifier Performance

Objective: Parkinson’s Disease (PD) is a neurodegenerative disease that is categorized by tremor, rigidity, and bradykinesia. Currently, there is no standard method to diagnose patients with PD. One of the common symptoms of PD is gait disorders which are caused by rigid muscles. Gait disorders may start some years before disease diagnosis. Therefore, better understanding of the gait signal can lead to early diagnosis of PD. Methods: Computer-aided system has been useful in early detection of PD symptoms. In the present study, gait disturbances have received attention as potential biomarkers for early diagnosing of PD. Time and frequency analysis of gait signals together can provide more useful information. Wavelet-based features were extracted from stride, swing and double support time signals of healthy subjects and PD patients. These signals were decomposed into five levels using “sym4” wavelet. Mean and standard deviation (SD) of the absolute values of the approximation and detailed coefficients at each level were computed. Then final features were picked accordingly to obtain the best result for the classification. Results: Support Vector Machine (SVM) was employed for classification of patients and healthy people. The classifier performance was measured based on accuracy, sensitivity and specificity. The classifier performance is obtained with 93.3% accuracy employing linear kernel. Conclusions: The proposed system can be employed as a Decision Support Systems (DSSs) for early diagnosing of PD. Presenting DSSs can be employed to screen suspected cases of PD disease for further evaluation. Studying large number of patients and healthy subjects may lead to more precise study on PD. Also, it seems that using other different classifiers, along with our features, can reduce the diagnosis error.

scholarly journals A Wearable System to Objectify Assessment of Motor Tasks for Supporting Parkinson’s Disease Diagnosis

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2630 ◽  
Author(s):  
Erika Rovini ◽  
Carlo Maremmani ◽  
Filippo Cavallo
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Objective Assessment ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Motor Tasks ◽  
Wearable System ◽  
Support Tool ◽  
Average Accuracy ◽  
Open Issue

Objective assessment of the motor evaluation test for Parkinson’s disease (PD) diagnosis is an open issue both for clinical and technical experts since it could improve current clinical practice with benefits both for patients and healthcare systems. In this work, a wearable system composed of four inertial devices (two SensHand and two SensFoot), and related processing algorithms for extracting parameters from limbs motion was tested on 40 healthy subjects and 40 PD patients. Seventy-eight and 96 kinematic parameters were measured from lower and upper limbs, respectively. Statistical and correlation analysis allowed to define four datasets that were used to train and test five supervised learning classifiers. Excellent discrimination between the two groups was obtained with all the classifiers (average accuracy ranging from 0.936 to 0.960) and all the datasets (average accuracy ranging from 0.953 to 0.966), over three conditions that included parameters derived from lower, upper or all limbs. The best performances (accuracy = 1.00) were obtained when classifying all the limbs with linear support vector machine (SVM) or gaussian SVM. Even if further studies should be done, the current results are strongly promising to improve this system as a support tool for clinicians in objectifying PD diagnosis and monitoring.

scholarly journals Parkinson’s Disease Diagnosis in Cepstral Domain Using MFCC and Dimensionality Reduction with SVM Classifier

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Atiqur Rahman ◽  
Sanam Shahla Rizvi ◽  
Aurangzeb Khan ◽  
Aaqif Afzaal Abbasi ◽  
Shafqat Ullah Khan ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Dimensionality Reduction ◽  
Intelligent System ◽  
Neurological Diseases ◽  
Binary Classification ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Svm Classifier ◽  
Linear Discriminant

Parkinson’s disease (PD) is one of the most common and serious neurological diseases. Impairments in voice have been reported to be the early biomarkers of the disease. Hence, development of PD diagnostic tool will help early diagnosis of the disease. Additionally, intelligent system developed for binary classification of PD and healthy controls can also be exploited in future as an instrument for prodromal diagnosis. Notably, patients with rapid eye movement (REM) sleep behaviour disorder (RBD) represent a good model as they develop PD with a high probability. It has been shown that slight speech and voice impairment may be a sensitive marker of preclinical PD. In this study, we propose PD detection by extracting cepstral features from the voice signals collected from people with PD and healthy subjects. To classify the extracted features, we propose to use dimensionality reduction through linear discriminant analysis and classification through support vector machine. In order to validate the effectiveness of the proposed method, we also developed ten different machine learning models. It was observed that the proposed method yield area under the curve (AUC) of 88%, sensitivity of 73.33%, and specificity of 84%. Moreover, the proposed intelligent system was simulated using publicly available multiple types of voice database. Additionally, the data were collected from patients under on-state. The obtained results on the public database are promising compared to the previously published work.

scholarly journals Data-Driven Based Approach to Aid Parkinson’s Disease Diagnosis

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 242 ◽  
Author(s):  
Nicolas Khoury ◽  
Ferhat Attal ◽  
Yacine Amirat ◽  
Latifa Oukhellou ◽  
Samer Mohammed
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Performance Evaluation ◽  
Random Forest ◽  
Neurodegenerative Diseases ◽  
Healthy Subjects ◽  
Support Vector ◽  
Classification Methods ◽  
Data Set ◽  
Feature Extraction And Selection

This article presents a machine learning methodology for diagnosing Parkinson’s disease (PD) based on the use of vertical Ground Reaction Forces (vGRFs) data collected from the gait cycle. A classification engine assigns subjects to healthy or Parkinsonian classes. The diagnosis process involves four steps: data pre-processing, feature extraction and selection, data classification and performance evaluation. The selected features are used as inputs of each classifier. Feature selection is achieved through a wrapper approach established using the random forest algorithm. The proposed methodology uses both supervised classification methods including K-nearest neighbour (K-NN), decision tree (DT), random forest (RF), Naïve Bayes (NB), support vector machine (SVM) and unsupervised classification methods such as K-means and the Gaussian mixture model (GMM). To evaluate the effectiveness of the proposed methodology, an online dataset collected within three different studies is used. This data set includes vGRF measurements collected from eight force sensors placed under each foot of the subjects. Ninety-three patients suffering from Parkinson’s disease and 72 healthy subjects participated in the experiments. The obtained performances are compared with respect to various metrics including accuracy, precision, recall and F-measure. The classification performance evaluation is performed using the leave-one-out cross validation. The results demonstrate the ability of the proposed methodology to accurately differentiate between PD subjects and healthy subjects. For the purpose of validation, the proposed methodology is also evaluated with an additional dataset including subjects with neurodegenerative diseases (Amyotrophic Lateral Sclerosis (ALS) and Huntington’s disease (HD)). The obtained results show the effectiveness of the proposed methodology to discriminate PD subjects from subjects with other neurodegenerative diseases with a relatively high accuracy.

scholarly journals A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson’s Disease

2019 ◽  
Vol 19 (S9) ◽  
Author(s):  
Domenico Buongiorno ◽  
Ilaria Bortone ◽  
Giacomo Donato Cascarano ◽  
Gianpaolo Francesco Trotta ◽  
Antonio Brunetti ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Healthy Subjects ◽  
Intelligent Systems ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Microsoft Kinect ◽  
Support Vector ◽  
Medical Specialists ◽  
Foot Tapping

Abstract Background Assessment and rating of Parkinson’s Disease (PD) are commonly based on the medical observation of several clinical manifestations, including the analysis of motor activities. In particular, medical specialists refer to the MDS-UPDRS (Movement Disorder Society – sponsored revision of Unified Parkinson’s Disease Rating Scale) that is the most widely used clinical scale for PD rating. However, clinical scales rely on the observation of some subtle motor phenomena that are either difficult to capture with human eyes or could be misclassified. This limitation motivated several researchers to develop intelligent systems based on machine learning algorithms able to automatically recognize the PD. Nevertheless, most of the previous studies investigated the classification between healthy subjects and PD patients without considering the automatic rating of different levels of severity. Methods In this context, we implemented a simple and low-cost clinical tool that can extract postural and kinematic features with the Microsoft Kinect v2 sensor in order to classify and rate PD. Thirty participants were enrolled for the purpose of the present study: sixteen PD patients rated according to MDS-UPDRS and fourteen healthy paired subjects. In order to investigate the motor abilities of the upper and lower body, we acquired and analyzed three main motor tasks: (1) gait, (2) finger tapping, and (3) foot tapping. After preliminary feature selection, different classifiers based on Support Vector Machine (SVM) and Artificial Neural Networks (ANN) were trained and evaluated for the best solution. Results Concerning the gait analysis, results showed that the ANN classifier performed the best by reaching 89.4% of accuracy with only nine features in diagnosis PD and 95.0% of accuracy with only six features in rating PD severity. Regarding the finger and foot tapping analysis, results showed that an SVM using the extracted features was able to classify healthy subjects versus PD patients with great performances by reaching 87.1% of accuracy. The results of the classification between mild and moderate PD patients indicated that the foot tapping features were the most representative ones to discriminate (81.0% of accuracy). Conclusions The results of this study have shown how a low-cost vision-based system can automatically detect subtle phenomena featuring the PD. Our findings suggest that the proposed tool can support medical specialists in the assessment and rating of PD patients in a real clinical scenario.

Pursuing Multiple Biomarkers for Early Idiopathic Parkinson’s Disease Diagnosis

2021 ◽  
Author(s):  
Yareth Gopar-Cuevas ◽  
Ana P. Duarte-Jurado ◽  
Rosa N. Diaz-Perez ◽  
Odila Saucedo-Cardenas ◽  
Maria J. Loera-Arias ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Diagnosis ◽  
Idiopathic Parkinson’S Disease ◽  
Idiopathic Parkinson's Disease ◽  
Multiple Biomarkers

scholarly journals Holistic Metabolomic Laboratory-Developed Test (LDT): Development and Use for the Diagnosis of Early-Stage Parkinson’s Disease

Metabolites ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 14
Author(s):  
Petr G. Lokhov ◽  
Dmitry L. Maslov ◽  
Steven Lichtenberg ◽  
Oxana P. Trifonova ◽  
Elena E. Balashova
Keyword(s):  
Parkinson’S Disease ◽  
Molecular Weight ◽  
Parkinson's Disease ◽  
High Resolution Mass Spectrometry ◽  
Early Stage ◽  
Disease Diagnosis ◽  
The Body ◽  
Low Molecular Weight ◽  
Low Molecular Weight Compounds

A laboratory-developed test (LDT) is a type of in vitro diagnostic test that is developed and used within a single laboratory. The holistic metabolomic LDT integrating the currently available data on human metabolic pathways, changes in the concentrations of low-molecular-weight compounds in the human blood during diseases and other conditions, and their prevalent location in the body was developed. That is, the LDT uses all of the accumulated metabolic data relevant for disease diagnosis and high-resolution mass spectrometry with data processing by in-house software. In this study, the LDT was applied to diagnose early-stage Parkinson’s disease (PD), which currently lacks available laboratory tests. The use of the LDT for blood plasma samples confirmed its ability for such diagnostics with 73% accuracy. The diagnosis was based on relevant data, such as the detection of overrepresented metabolite sets associated with PD and other neurodegenerative diseases. Additionally, the ability of the LDT to detect normal composition of low-molecular-weight compounds in blood was demonstrated, thus providing a definition of healthy at the molecular level. This LDT approach as a screening tool can be used for the further widespread testing for other diseases, since ‘omics’ tests, to which the metabolomic LDT belongs, cover a variety of them.

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