A New Evolutionary Support Vector Machine with Application to Parkinson’s Disease Diagnosis

Background: The challenge of differentiating, at an early stage, Parkinson’s disease from parkinsonism caused by other disorders remains unsolved. We proposed using an artificial neural network (ANN) to process images of dopamine transporter single-photon emission computed tomography (DAT-SPECT). Methods: Abnormal DAT-SPECT images of subjects with Parkinson’s disease and parkinsonism caused by other disorders were divided into training and test sets. Striatal regions of the images were segmented by using an active contour model and were used as the data to perform transfer learning on a pre-trained ANN to discriminate Parkinson’s disease from parkinsonism caused by other disorders. A support vector machine trained using parameters of semi-quantitative measurements including specific binding ratio and asymmetry index was used for comparison. Results: The predictive accuracy of the ANN classifier (86%) was higher than that of the support vector machine classifier (68%). The sensitivity and specificity of the ANN classifier in predicting Parkinson’s disease were 81.8% and 88.6%, respectively. Conclusions: The ANN classifier outperformed classical biomarkers in differentiating Parkinson’s disease from parkinsonism caused by other disorders. This classifier can be readily included into standalone computer software for clinical application.

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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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Diagnosis of Parkinson's disease using genetic algorithm and support vector machine with acoustic characteristics

2012 5th International Conference on BioMedical Engineering and Informatics ◽

10.1109/bmei.2012.6513201 ◽

2012 ◽

Cited By ~ 2

Author(s):

Hanguang Xiao

Keyword(s):

Genetic Algorithm ◽

Parkinson’S Disease ◽

Support Vector Machine ◽

Parkinson's Disease ◽

Support Vector ◽

Acoustic Characteristics

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Tremor Identification Using Machine Learning in Parkinson's Disease

Early Detection of Neurological Disorders Using Machine Learning Systems - Advances in Medical Technologies and Clinical Practice ◽

10.4018/978-1-5225-8567-1.ch008 ◽

2019 ◽

pp. 128-151

Author(s):

Angana Saikia ◽

Vinayak Majhi ◽

Masaraf Hussain ◽

Sudip Paul ◽

Amitava Datta

Keyword(s):

Machine Learning ◽

Parkinson’S Disease ◽

Support Vector Machine ◽

Parkinson's Disease ◽

Discriminant Analysis ◽

Learning Algorithms ◽

The Body ◽

Machine Learning Algorithms ◽

Support Vector

Tremor is an involuntary quivering movement or shake. Characteristically occurring at rest, the classic slow, rhythmic tremor of Parkinson's disease (PD) typically starts in one hand, foot, or leg and can eventually affect both sides of the body. The resting tremor of PD can also occur in the jaw, chin, mouth, or tongue. Loss of dopamine leads to the symptoms of Parkinson's disease and may include a tremor. For some people, a tremor might be the first symptom of PD. Various studies have proposed measurable technologies and the analysis of the characteristics of Parkinsonian tremors using different techniques. Various machine-learning algorithms such as a support vector machine (SVM) with three kernels, a discriminant analysis, a random forest, and a kNN algorithm are also used to classify and identify various kinds of tremors. This chapter focuses on an in-depth review on identification and classification of various Parkinsonian tremors using machine learning algorithms.

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Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine

Journal of Biomedical Science and Engineering ◽

10.4236/jbise.2014.74019 ◽

2014 ◽

Vol 07 (04) ◽

pp. 147-156 ◽

Cited By ~ 40

Author(s):

Mohammad Shahbakhi ◽

Danial Taheri Far ◽

Ehsan Tahami

Keyword(s):

Genetic Algorithm ◽

Parkinson’S Disease ◽

Support Vector Machine ◽

Parkinson's Disease ◽

Speech Analysis ◽

Support Vector

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

Mobile Information Systems ◽

10.1155/2021/8822069 ◽

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.

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Feature Selection Based on L1-Norm Support Vector Machine and Effective Recognition System for Parkinson’s Disease Using Voice Recordings

IEEE Access ◽

10.1109/access.2019.2906350 ◽

2019 ◽

Vol 7 ◽

pp. 37718-37734 ◽

Cited By ~ 27

Author(s):

Amin Ul Haq ◽

Jian Ping Li ◽

Muhammad Hammad Memon ◽

Jalaluddin khan ◽

Asad Malik ◽

...

Keyword(s):

Parkinson’S Disease ◽

Support Vector Machine ◽

Parkinson's Disease ◽

Feature Selection ◽

Recognition System ◽

Support Vector ◽

L1 Norm

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Tremor Identification Using Machine Learning in Parkinson's Disease

Research Anthology on Diagnosing and Treating Neurocognitive Disorders ◽

10.4018/978-1-7998-3441-0.ch018 ◽

2021 ◽

pp. 341-365

Author(s):

Angana Saikia ◽

Vinayak Majhi ◽

Masaraf Hussain ◽

Sudip Paul ◽

Amitava Datta

Keyword(s):

Machine Learning ◽

Parkinson’S Disease ◽

Support Vector Machine ◽

Parkinson's Disease ◽

Discriminant Analysis ◽

Learning Algorithms ◽

The Body ◽

Machine Learning Algorithms ◽

Support Vector

Tremor is an involuntary quivering movement or shake. Characteristically occurring at rest, the classic slow, rhythmic tremor of Parkinson's disease (PD) typically starts in one hand, foot, or leg and can eventually affect both sides of the body. The resting tremor of PD can also occur in the jaw, chin, mouth, or tongue. Loss of dopamine leads to the symptoms of Parkinson's disease and may include a tremor. For some people, a tremor might be the first symptom of PD. Various studies have proposed measurable technologies and the analysis of the characteristics of Parkinsonian tremors using different techniques. Various machine-learning algorithms such as a support vector machine (SVM) with three kernels, a discriminant analysis, a random forest, and a kNN algorithm are also used to classify and identify various kinds of tremors. This chapter focuses on an in-depth review on identification and classification of various Parkinsonian tremors using machine learning algorithms.

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