Detection of Parkinson’s disease based on voice patterns ranking and optimized support vector machine

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.

Download Full-text

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

Download Full-text

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.

Download Full-text

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

Download Full-text

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

Download Full-text

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.

Download Full-text