scholarly journals m-ark-Support Vector Machine for Early Detection of Parkinson’s Disease from Speech Signals

2021 ◽  
Vol 15 ◽  
pp. 34-41
Author(s):  
Luca Parisi ◽  
Amir Zaernia ◽  
Renfei Ma ◽  
Mansour Youseffi
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Detection ◽  
Early Diagnosis ◽  
Real Life ◽  
Kernel Functions ◽  
Support Vector ◽  
Margin Width ◽  
Speech Data ◽  
Benchmark Datasets

Recent advances in the state-of-the-art open-source kernel functions for support vector machines (SVMs) have widened the choices of benchmark kernels for Machine Learning (ML)-based classification. However, it is still challenging to achieve margin maximisation in SVM, and further evidence is required to ensure such novel kernel functions can have translational applications with tangible impact. Noteworthily, m-arcsinh, freely available in scikit-learn, was preliminarily proven as a benchmark kernel function on 15 datasets in its seminal paper. Quantifying the benefit from leveraging this kernel in a specific application is essential to provide further evidence of its accuracy and reliability on real-life supervised ML-aided tasks. Thus, the predictive capability of SVM, including that with Lagrange multipliers for the first time coupled with m-arcsinh (m-ark-SVM with soft margin; m-arK-SVM with hard margin), is hereby assessed in aiding early detection of Parkinson’s Disease (PD) from speech data. This is important to leverage the m-arcsinh kernel ‘trick’ to maximise the margin width and, therefore, the linear separability of input speech features via automated pattern recognition. In this study, we demonstrate the accuracy and reliability of m-ark-SVM to aid early diagnosis of PD, evaluated against other gold standard kernel functions. Two benchmark datasets from the University of California-Irvine (UCI) database, pre-processed solely via min-max normalisation, were used to discriminate between speech patterns of 72 healthy subjects and 211 patients with PD. Overtraining was avoided via cross validation and the models were developed and tested in Python 3.7. The supervised model (m-ark-SVM) could detect early Parkinson’s Disease with 87.18% and 86.9% classification accuracy from the two datasets respectively (F1- scores: 85 and 86.2% correspondingly). Furthermore, the model achieved high precision (89.2% and 86.8%) and specificity (87% and 86.8%). Thus, this study validates the application of m-arcsinh to aid real-life supervised ML-based classification, in particular early diagnosis of Parkinson’s Disease from speech data.

scholarly journals Ηyper-sinh-Convolutional Neural Network for Early Detection of Parkinson’s Disease from Spiral Drawings

2021 ◽  
Vol 9 ◽  
pp. 1-7
Author(s):  
Luca Parisi ◽  
Amir Zaernia ◽  
Renfei Ma ◽  
Mansour Youseffi
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Detection ◽  
Early Diagnosis ◽  
Image Classification ◽  
Real Life ◽  
High Sensitivity ◽  
Input Image ◽  
Activation Functions ◽  
First Time

Modern developments in the state-of-the-art open-source activation functions for Convolutional Neural Networks (CNNs) have broadened the selection of benchmark activations for Deep Learning (DL)-aided classification. Nevertheless, achieving discrimination of non-linear input image data in CNN is still not straightforward and it is unclear how such novel activation functions can have translational applications with tangible impact. hyper-sinh, made freely available in TensorFlow and Keras, was demonstrated as a benchmark activation function on five (N=5) datasets in its ground-breaking paper. Measuring the value from deploying this activation in a specific application is pivotal to supply the required evidence of its performance on real-life supervised DL-based image classification tasks. In this study, a CNN was for the first time combined with hypersinh to aid early detection of Parkinson’s Disease (PD) from discriminating pathophysiological patterns extracted from spiral drawings. Thus, the hyper-sinh activation was deployed to maximise the separability of the input features from spiral drawings via automated pattern recognition. We demonstrate the accuracy and reliability of hyper-sinh-CNN to aid early diagnosis of PD, evaluated against other gold standard activation functions, including the recent Quantum ReLU (QReLU) and the modified Quantum ReLU (m-QReLU) that solved the ‘dying ReLU’ problem for the first time in the literature of DL. Two (N=2) benchmark datasets from the database of the Botucatu Medical School, São Paulo State University in Brazil, scaled to be in 28 by 28 pixels as the MNIST benchmark data, were used to discriminate between input image patterns of 158 subjects (53 healthy controls and 105 patients with PD) from spirals drawn on graphics tablets. Overtraining was avoided via early stopping and the models were developed and tested in TensorFlow and Keras (Python 3.6). The supervised model (hyper-sinh-CNN) could detect early Parkinson’s Disease with 81% and 91% classification accuracy from the two datasets respectively (F1-scores: 73% and 91% correspondingly). Furthermore, the model achieved high sensitivity (81% and 91%). Thus, this study validates the application of hyper-sinh to aid real-life supervised DL-based image classification, in particular early diagnosis of PD from spiral drawings.

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

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.

scholarly journals An optimized RNN-LSTM approach for parkinson’s disease early detection using speech features

2021 ◽  
Vol 10 (5) ◽  
pp. 2503-2512
Author(s):  
Hadeel Ahmed Abd El Aal ◽  
Shereen A. Taie ◽  
Nashwa El-Bendary
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Detection ◽  
Short Term Memory ◽  
Neurodegenerative Disorder ◽  
Classification Performance ◽  
The Body ◽  
Testing Dataset ◽  
Benchmark Datasets ◽  
Speech Features

Parkinson's disease (PD) is the second most common neurodegenerative disorder disease right after Alzheimer's and the most common movement disorder for elderly people. It is characterized as a progressive loss of muscle control, which leads to trembling characterized by uncontrollable shaking, or (tremors) in different parts of the body. In recent years, deep learning (DL) models achieved significant progress in automatic speech recognition, however, limited studies addressed the problem of distinguishing people with PD for further clinical diagnosis. In this paper, an approach for the early detection of patients with PD using speech features was proposed, a recurrent neural network (RNN) with long short-term memory (LSTM) is applied with the batch normalization layer and adaptive moment estimation (ADAM) optimization algorithm used after the network hidden layers to improve the classification performance. The proposed approach is applied with 2 benchmark datasets of speech features for patients with PD and healthy control subjects. The proposed approach achieved an accuracy of 95.8% and MCC=92.04% for the testing dataset. In future work, we aim to increase the voice features that will be worked on and consider using handwriting kinematic features.

scholarly journals Predicting the Severity of Parkinson’s Disease Dementia by Assessing the Neuropsychiatric Symptoms with an SVM Regression Model

2021 ◽  
Vol 18 (5) ◽  
pp. 2551
Author(s):  
Haewon Byeon
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Convergence Rate ◽  
Mean Squared Error ◽  
Neuropsychiatric Symptoms ◽  
Neuropsychological Test ◽  
Kernel Functions ◽  
Support Vector ◽  
Parkinson’S Disease Dementia ◽  
Sleep Behavior

In this study, we measured the convergence rate using the mean-squared error (MSE) of the standardized neuropsychological test to determine the severity of Parkinson’s disease dementia (PDD), which is based on support vector machine (SVM) regression (SVR) and present baseline data in order to develop a model to predict the severity of PDD. We analyzed 328 individuals with PDD who were 60 years or older. To identify the SVR with the best prediction power, we compared the classification performance (convergence rate) of eight SVR models (Eps-SVR and Nu-SVR with four kernel functions (a radial basis function (RBF), linear algorithm, polynomial algorithm, and sigmoid)). Among the eight models, the MSE of Nu-SVR-RBF was the lowest (0.078), with the highest convergence rate, whereas the MSE of Eps-SVR-sigmoid was 0.110, with the lowest convergence rate. The results of this study imply that this approach could be useful for measuring the severity of dementia by comprehensively examining axial atypical features, the Korean instrumental activities of daily living (K-IADL), changes in rapid eye movement sleep behavior disorder (RBD), etc. for optimal intervention and caring of the elderly living alone or patients with PDD residing in medically vulnerable areas.

Diagnosis of Parkinson’s Disease Using SVM Classifier

2020 ◽  
pp. 2150011
Author(s):  
G. Wiselin Jiji ◽  
A. Rajesh ◽  
P. Johnson Durai Raj
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Diagnosis ◽  
Active Contour ◽  
Motor System ◽  
Svm Classifier ◽  
Common Disease ◽  
Morphological Operation ◽  
Aged People ◽  
Speech Data

Parkinson’s disease (PD) is the most common disease that affects aged people which leads to dopamine-producing cells in substantia nigra to be damaged when motor system degenerates. Clinical Diagnosis of Parkinson’s disease at the earlier stage is very difficult. This work is carried out to find the significance of cognition function of basal ganglia (BG) region and speech data values. The BG can be segmented using morphological operation and active contour algorithm. Co-occurrences features are extracted and out of 720 features, the promising 110 features are selected using variance method. More promising 22 features are selected in speech data and both features are individually classified using SVM to find out the efficiency in Diagnosis. The outcome shows cognition function of BG performing a major role in early diagnosis of Parkinson’s disease when compared to speech data.

Multiclass recognition of Alzheimer’s and Parkinson’s disease using various machine learning techniques: A study

2021 ◽  
Author(s):  
Chetan Balaji ◽  
D. S. Suresh
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Diagnosis ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Ensemble Methods ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Input Space

The aging population is primarily affected by Alzheimer’s disease (AD) that is an incurable neurodegenerative disorder. There is a need for an automated efficient technique to diagnose Alzheimer’s in its early stage. Various techniques are used to diagnose AD. EEG and neuroimaging methodologies are widely used to highlight changes in the electrical activity of the brain signals that are helpful for early diagnosis. Parkinson’s disease (PD) is a major neurological disease that results in an average of 50,000 new clinical diagnoses worldwide every year. The voice features are majorly used as the main means to diagnose PD. The major symptoms of PD are loss of intensity, the monotony of loudness and pitch, reduction in stress, unidentified silences, and dysphonia. Even though various innovative models are proposed by explorers about Alzheimer’s and Parkinson’s classification diseases, still there is a need for efficient learning methodologies and techniques. This paper provides a review on using machine learning (ML) together with several feature extraction techniques that is helpful in the early detection of AD with Parkinson’s. The novelty and objective of this study are that the CAD technique is used to improve the accuracy of early diagnosis of AD. The proposed technique depends on the nonlinear process for data dimension reduction, feature removal, and classification using kernel-based support vector machine (SVM) classifiers. The dimension of the input space is radically diminished with kernel methods. As the learning set is labeled, it creates sense to utilize this information to make a dependable method of dropping the input space dimension. The different techniques of ML are explained under the major approaches viz. SVM, artificial neural network (ANN), deep learning (DL), and ensemble methods. A comprehensive assessment is presented at SVM, ANN, and DL approaches for better detection of Alzheimer’s with PD highlighting future insights.

Current Therapeutic Strategies and Perspectives for Neuroprotection in Parkinson’s Disease

2020 ◽  
Vol 26 (37) ◽  
pp. 4738-4746
Author(s):  
Mohan K. Ghanta ◽  
P. Elango ◽  
Bhaskar L. V. K. S.
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Diagnosis ◽  
Neurodegenerative Disorder ◽  
Therapeutic Strategies ◽  
Neuroprotective Agents ◽  
Striatal Neurons ◽  
The Status ◽  
Advanced Stages ◽  
Dopaminergic Pathways

Parkinson’s disease is a progressive neurodegenerative disorder of dopaminergic striatal neurons in basal ganglia. Treatment of Parkinson’s disease (PD) through dopamine replacement strategies may provide improvement in early stages and this treatment response is related to dopaminergic neuronal mass which decreases in advanced stages. This treatment failure was revealed by many studies and levodopa treatment became ineffective or toxic in chronic stages of PD. Early diagnosis and neuroprotective agents may be a suitable approach for the treatment of PD. The essentials required for early diagnosis are biomarkers. Characterising the striatal neurons, understanding the status of dopaminergic pathways in different PD stages may reveal the effects of the drugs used in the treatment. This review updates on characterisation of striatal neurons, electrophysiology of dopaminergic pathways in PD, biomarkers of PD, approaches for success of neuroprotective agents in clinical trials. The literature was collected from the articles in database of PubMed, MedLine and other available literature resources.

scholarly journals The Role of Salivary Biomarkers in the Early Diagnosis of Alzheimer’s Disease and Parkinson’s Disease

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 371
Author(s):  
Patrycja Pawlik ◽  
Katarzyna Błochowiak
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Early Diagnosis ◽  
Neurodegenerative Diseases ◽  
Diagnostic Tool ◽  
Clinical Symptoms ◽  
Early Screening ◽  
Salivary Biomarkers

Many neurodegenerative diseases present with progressive neuronal degeneration, which can lead to cognitive and motor impairment. Early screening and diagnosis of neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD) are necessary to begin treatment before the onset of clinical symptoms and slow down the progression of the disease. Biomarkers have shown great potential as a diagnostic tool in the early diagnosis of many diseases, including AD and PD. However, screening for these biomarkers usually includes invasive, complex and expensive methods such as cerebrospinal fluid (CSF) sampling through a lumbar puncture. Researchers are continuously seeking to find a simpler and more reliable diagnostic tool that would be less invasive than CSF sampling. Saliva has been studied as a potential biological fluid that could be used in the diagnosis and early screening of neurodegenerative diseases. This review aims to provide an insight into the current literature concerning salivary biomarkers used in the diagnosis of AD and PD. The most commonly studied salivary biomarkers in AD are β-amyloid1-42/1-40 and TAU protein, as well as α-synuclein and protein deglycase (DJ-1) in PD. Studies continue to be conducted on this subject and researchers are attempting to find correlations between specific biomarkers and early clinical symptoms, which could be key in creating new treatments for patients before the onset of symptoms.

scholarly journals Early Detection of Parkinson’s Disease Using Deep Learning and Machine Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 147635-147646 ◽  
Author(s):  
Wu Wang ◽  
Junho Lee ◽  
Fouzi Harrou ◽  
Ying Sun
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Early Detection
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