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

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.

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

Sensors ◽

10.3390/s20092630 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2630 ◽

Cited By ~ 2

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.

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A Deep Learning Approach for Classification and Diagnosis of Parkinson’s Disease

10.21203/rs.3.rs-254647/v1 ◽

2021 ◽

Author(s):

Monika Jyotiyana ◽

Nishtha Kesswani ◽

Munish Kumar

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Deep Learning ◽

Nearest Neighbor ◽

Health Sector ◽

Classification Model ◽

Support Vector ◽

Learning Approaches ◽

K Nearest Neighbor ◽

Linear Discriminant

Abstract Deep learning techniques are playing an important role in the classification and prediction of diseases. Undoubtedly deep learning has a promising future in the health sector, especially in medical imaging. The popularity of deep learning approaches is because of their ability to handle a large amount of data related to the patients with accuracy, reliability in a short span of time. However, the practitioners may take time in analyzing and generating reports. In this paper, we have proposed a Deep Neural Network-based classification model for Parkinson’s disease. Our proposed method is one such good example giving faster and more accurate results for the classification of Parkinson’s disease patients with excellent accuracy of 94.87%. Based on the attributes of the dataset of the patient, the model can be used for the identification of Parkinsonism's. We have also compared the results with other existing approaches like Linear Discriminant Analysis, Support Vector Machine, K-Nearest Neighbor, Decision Tree, Classification and Regression Trees, Random Forest, Linear Regression, Logistic Regression, Multi-Layer Perceptron, and Naive Bayes.

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Alzheimer Disease Diagnosis from fMRI images Based on Latent Low Rank Features and Support Vector Machine (SVM)

Current Signal Transduction Therapy ◽

10.2174/1574362414666191202144116 ◽

2019 ◽

Vol 14 ◽

Author(s):

Nastaran Shahparian ◽

Mehran Yazdi ◽

Mohammad Reza Khosravi

Keyword(s):

Support Vector Machine ◽

Alzheimer Disease ◽

Neurological Diseases ◽

Practical Method ◽

Disease Diagnosis ◽

Low Rank ◽

Gaussian Kernel ◽

Fmri Data ◽

Support Vector ◽

Svm Classifier

Purpose: In recent years, resting-state functional magnetic resonance imaging (rs-fMRI) has been increasingly used as a noninvasive and practical method in different areas of neuroscience and psychology for recognizing brain’s mechanism as well as diagnosing neurological diseases. In this work, we use rs-fMRI data for diagnosing Alzheimer disease. Design/methodology/approach: To do that, by using the rs-fMRI of a patient, we computed the time series of some anatomical regions and then applied the Latent Low Rank Representation method to extract suitable features. Next, based on the extracted features we apply a Support Vector Machine (SVM) classifier to determine whether the patient belongs to healthy category, mild stage of the disease or Alzheimer stage. Findings: The obtained classification accuracy for the proposed method is more than 97.5%. Originality/value: We performed different experiments on a database of rs-fMRI data containing the images of 43 healthy subjects, 36 mild cognitive impairment patients and 32 Alzheimer patients and the obtained results demonstrated that the best performance is achieved when the SVM with Gaussian kernel and the features of only 7 regions were used.

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Effective Dysphonia Detection Using Feature Dimension Reduction and Kernel Density Estimation for Patients with Parkinson’s Disease

10.32920/14668926.v1 ◽

2021 ◽

Author(s):

Sridhar Krishnan ◽

Shanshan Yang ◽

Fang Zheng ◽

Xin Luo ◽

Suxian Cai ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Dimension Reduction ◽

Density Estimation ◽

Kernel Density Estimation ◽

Kernel Density ◽

Support Vector ◽

Linear Discriminant ◽

Feature Dimension ◽

Feature Dimension Reduction

Detection of dysphonia is useful for monitoring the progression of phonatory impairment for patients with Parkinson’s disease (PD), and also helps assess the disease severity. This paper describes the statistical pattern analysis methods to study different vocal measurements of sustained phonations. The feature dimension reduction procedure was implemented by using the sequential forward selection (SFS) and kernel principal component analysis (KPCA) methods. Four selected vocal measures were projected by the KPCA onto the bivariate feature space, in which the class-conditional feature densities can be approximated with the nonparametric kernel density estimation technique. In the vocal pattern classification experiments, Fisher’s linear discriminant analysis (FLDA) was applied to perform the linear classification of voice records for healthy control subjects and PD patients, and the maximum a posteriori (MAP) decision rule and support vector machine (SVM) with radial basis function kernels were employed for the nonlinear classification tasks. Based on the KPCA-mapped feature densities, the MAP classifier successfully distinguished 91.8% voice records, with a sensitivity rate of 0.986, a specificity rate of 0.708, and an area value of 0.94 under the receiver operating characteristic (ROC) curve. The diagnostic performance provided by the MAP classifier was superior to those of the FLDA and SVM classifiers. In addition, the classification results indicated that gender is insensitive to dysphonia detection, and the sustained phonations of PD patients with minimal functional disability are more difficult to be correctly identified.

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Using Tractography to Distinguish SWEDD from Parkinson’s Disease Patients Based on Connectivity

Parkinson s Disease ◽

10.1155/2016/8704910 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Mansu Kim ◽

Hyunjin Park

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Rating Scale ◽

Diffusion Tensor ◽

Magnetic Resonance Images ◽

Future Research ◽

Support Vector ◽

Svm Classifier ◽

Research Database ◽

Significant Group

Background. It is critical to distinguish between Parkinson’s disease (PD) and scans without evidence of dopaminergic deficit (SWEDD), because the two groups are different and require different therapeutic approaches.Objective. The aim of this study was to distinguish SWEDD patients from PD patients using connectivity information derived from diffusion tensor imaging tractography.Methods. Diffusion magnetic resonance images of SWEDD (n=37) and PD (n=40) were obtained from a research database. Tractography, the process of obtaining neural fiber information, was performed using custom software. Group-wise differences between PD and SWEDD patients were quantified using the number of connected fibers between two regions, and correlation analyses were performed based on clinical scores. A support vector machine classifier (SVM) was applied to distinguish PD and SWEDD based on group-wise differences.Results. Four connections showed significant group-wise differences and correlated with the Unified Parkinson’s Disease Rating Scale sponsored by the Movement Disorder Society. The SVM classifier attained 77.92% accuracy in distinguishing between SWEDD and PD using these identified connections.Conclusions. The connections and regions identified represent candidates for future research investigations.

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Cloud-Based Parkinson’s Disease Diagnosis Using Machine Learning

Mathematical Modelling and Engineering Problems ◽

10.18280/mmep.080610 ◽

2021 ◽

Vol 8 (6) ◽

pp. 915-922

Author(s):

Ahmed R. Nasser ◽

Ali M. Mahmood

Keyword(s):

Machine Learning ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Early Treatment ◽

Intelligent System ◽

Feature Selection Method ◽

Disease Diagnosis ◽

Classification Model ◽

Accuracy Measurement ◽

Diagnosis Model

Parkinson’s disease (PD) harms the human brain's nervous system and can affect the patient's life. However, the diagnosis of PD diagnosis in the first stages can lead to early treatment and save costs. In this paper, a cloud-based machine learning diagnosing intelligent system is proposed for the PD with respect to patient voice. The proposed system is composed of two stages. In the first stage, two machine learning approaches, Random-Forest (RF) and Long-Short-Term-Memory (LSTM) are applied to generate a model that can be used for early treatment of PD. In this stage, a feature selection method is used to choose the minimum subset of the best features, which can be utilized later to generate the classification model. In the second stage, the best diagnosis model is deployed in cloud computing. In this stage, an Android application is also designed to provide the interface to the diagnosis model. The performance evaluation of the diagnosis model is conducted based on the F-score accuracy measurement. The result shows that the LTSM model has superior accuracy with 95% of the F-score compared with the RF model. Therefore, the LSTM model is selected for implementing a cloud-based PD diagnosing application using Python and Java.

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

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237219500261 ◽

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.

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Effective Dysphonia Detection Using Feature Dimension Reduction and Kernel Density Estimation for Patients with Parkinson’s Disease

10.32920/14668926 ◽

2021 ◽

Author(s):

Sridhar Krishnan ◽

Shanshan Yang ◽

Fang Zheng ◽

Xin Luo ◽

Suxian Cai ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Dimension Reduction ◽

Density Estimation ◽

Kernel Density Estimation ◽

Kernel Density ◽

Support Vector ◽

Linear Discriminant ◽

Feature Dimension ◽

Feature Dimension Reduction

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Task-Oriented Intelligent Solution to Measure Parkinson’s Disease Tremor Severity

Journal of Healthcare Engineering ◽

10.1155/2021/9624386 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Ghayth AlMahadin ◽

Ahmad Lotfi ◽

Marie Mc Carthy ◽

Philip Breedon

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Data Collection ◽

Rating Scale ◽

Classification Performance ◽

Support Vector ◽

Svm Classifier ◽

Common Symptom ◽

Average Rule ◽

Novel Approach

Tremor is a common symptom of Parkinson’s disease (PD). Currently, tremor is evaluated clinically based on MDS-UPDRS Rating Scale, which is inaccurate, subjective, and unreliable. Precise assessment of tremor severity is the key to effective treatment to alleviate the symptom. Therefore, several objective methods have been proposed for measuring and quantifying PD tremor from data collected while patients performing scripted and unscripted tasks. However, up to now, the literature appears to focus on suggesting tremor severity classification methods without discrimination tasks effect on classification and tremor severity measurement. In this study, a novel approach to identify a recommended system is used to measure tremor severity, including the influence of tasks performed during data collection on classification performance. The recommended system comprises recommended tasks, classifier, classifier hyperparameters, and resampling technique. The proposed approach is based on the above-average rule of five advanced metrics results of four subdatasets, six resampling techniques, six classifiers besides signal processing, and features extraction techniques. The results of this study indicate that tasks that do not involve direct wrist movements are better than tasks that involve direct wrist movements for tremor severity measurements. Furthermore, resampling techniques improve classification performance significantly. The findings of this study suggest that a recommended system consists of support vector machine (SVM) classifier combined with BorderlineSMOTE oversampling technique and data collection while performing set of recommended tasks, which are sitting, stairs up and down, walking straight, walking while counting, and standing.

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