A novel method for automatic classification of Parkinson gait severity using front-view video analysis

Parkinson’s disease (PD) is a neurodegenerative condition with neuronal cell death in the substantia nigra and striatal dopamine deficiency that produces slowness, stiffness, tremor, shuffling gait and postural instability. More than 1 million people in North America are affected by PD resulting in balance problems and falls. It is observed that postural instability and gait problems become resistant to pharmacologic therapy as the disease progresses. Furthermore, studies suggest that postural sway abnormalities are worsened by levodopa, the mainstay of therapy for PD. This paper presents a classification of postural balance test data using Support Vector Machines (SVM) to identify the effect of medicine (levodopa) as well as dyskinesia. It is demonstrated that SVM is a useful tool and can complement the widely accepted (but very resource intensive) Unified Parkinson’s Disease Rating Scale (UPDRS).

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Comprehensive analysis of resting tremor based on acceleration signals of patients with Parkinson’s disease

Technology and Health Care ◽

10.3233/thc-213205 ◽

2021 ◽

pp. 1-13

Author(s):

Sen Liu ◽

Han Yuan ◽

Jiali Liu ◽

Hai Lin ◽

Cuiwei Yang ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Rating Scale ◽

Statistical Tests ◽

Real Life ◽

Feature Analysis ◽

Support Vector ◽

Monitoring Methods ◽

Resting Tremor ◽

Acceleration Signals

BACKGROUND: Resting tremor is an essential characteristic in patients suffering from Parkinson’s disease (PD). OBJECTIVE: Quantification and monitoring of tremor severity is clinically important to help achieve medication or rehabilitation guidance in daily monitoring. METHODS: Wrist-worn tri-axial accelerometers were utilized to record the long-term acceleration signals of PD patients with different tremor severities rated by Unified Parkinson’s Disease Rating Scale (UPDRS). Based on the extracted features, three kinds of classifiers were used to identify different tremor severities. Statistical tests were further designed for the feature analysis. RESULTS: The support vector machine (SVM) achieved the best performance with an overall accuracy of 94.84%. Additional feature analysis indicated the validity of the proposed feature combination and revealed the importance of different features in differentiating tremor severities. CONCLUSION: The present work obtains a high-accuracy classification in tremor severity, which is expected to play a crucial role in PD treatment and symptom monitoring in real life.

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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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Selecting Clinically Relevant Gait Characteristics for Classification of Early Parkinson’s Disease: A Comprehensive Machine Learning Approach

Scientific Reports ◽

10.1038/s41598-019-53656-7 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 6

Author(s):

Rana Zia Ur Rehman ◽

Silvia Del Din ◽

Yu Guan ◽

Alison J. Yarnall ◽

Jian Qing Shi ◽

...

Keyword(s):

Machine Learning ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Clinical Decision Making ◽

Information Gain ◽

Step Length ◽

Support Vector ◽

Step Width ◽

Gait Characteristics

AbstractParkinson’s disease (PD) is the second most common neurodegenerative disease; gait impairments are typical and are associated with increased fall risk and poor quality of life. Gait is potentially a useful biomarker to help discriminate PD at an early stage, however the optimal characteristics and combination are unclear. In this study, we used machine learning (ML) techniques to determine the optimal combination of gait characteristics to discriminate people with PD and healthy controls (HC). 303 participants (119 PD, 184 HC) walked continuously around a circuit for 2-minutes at a self-paced walk. Gait was quantified using an instrumented mat (GAITRite) from which 16 gait characteristics were derived and assessed. Gait characteristics were selected using different ML approaches to determine the optimal method (random forest with information gain and recursive features elimination (RFE) technique with support vector machine (SVM) and logistic regression). Five clinical gait characteristics were identified with RFE-SVM (mean step velocity, mean step length, step length variability, mean step width, and step width variability) that accurately classified PD. Model accuracy for classification of early PD ranged between 73–97% with 63–100% sensitivity and 79–94% specificity. In conclusion, we identified a subset of gait characteristics for accurate early classification of PD. These findings pave the way for a better understanding of the utility of ML techniques to support informed clinical decision-making.

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Automatic Classification of Tremor Severity in Parkinson’s Disease Using a Wearable Device

Sensors ◽

10.3390/s17092067 ◽

2017 ◽

Vol 17 (9) ◽

pp. 2067 ◽

Cited By ~ 34

Author(s):

Hyoseon Jeon ◽

Woongwoo Lee ◽

Hyeyoung Park ◽

Hong Lee ◽

Sang Kim ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Automatic Classification ◽

Wearable Device

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Voice Assessments for Detecting Patients with Parkinson’s Diseases in Different Stages

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v8i6.pp4265-4271 ◽

2018 ◽

Vol 8 (6) ◽

pp. 4265

Author(s):

Elmehdi Benmalek ◽

Jamal Elmhamdi ◽

Abdelilah Jilbab

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Rating Scale ◽

Principal Component ◽

Support Vector ◽

Speech Signal Processing ◽

Parkinson’S Diseases ◽

Wide Range ◽

Signal Processing Algorithms ◽

Linear Svm

<p class="IJASEITParagraph">Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to detect patients with Parkinson’s disease (PD). So we have computed 19 dysphonia measures from sustained vowels collected from 375 voice samples from healthy and people suffer from PD. All the features are analysed and the more relevant ones are selected by the Principal component analysis (PCA) to classify the subjects in 4 classes according to the UPDRS (unified Parkinson’s disease Rating Scale) score. We used k-folds cross validation method with (k=4) validation scheme; 75% for training and 25% for testing, along with the Support Vector Machines (SVM) with its different types of kernels. The best result obtained was 92.5% using the PCA and the linear SVM.</p>

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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.

Download Full-text