Voice Analysis for Telediagnosis of Parkinson Disease Using Artificial Neural Networks and Support Vector Machines

Sharma, R. K.; Gupta, Anil Kumar

doi:10.5815/ijisa.2015.06.04

Cited by 15 publications

(8 citation statements)

References 13 publications

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“…Here, pro ximity matrices that contain DTW distances could be in use [1]. Another research direction could involve co mparing accuracy of the presented approach with results of other classifiers, such as neural networks or support vector machines [36].…”

Section: Discussionmentioning

confidence: 99%

An Approach to Gesture Recognition with Skeletal Data Using Dynamic Time Warping and Nearest Neighbour Classifier

Ribó¹,

Warchoł²,

Oszust³

2016

IJISA

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Gestures are natural means of co mmun ication between humans, and therefore their application would benefit to many fields where usage of typical input devices, such as keyboards or joysticks is cu mbersome or unpractical (e.g., in noisy environ ment). Recently, together with emergence of new cameras that allow obtaining not only colour images of observed scene, but also offer the software developer rich informat ion on the number of seen hu mans and, what is most interesting, 3D positions of their body parts, practical applications using body gestures have become more popular. Such informat ion is presented in a form o f skeletal data. In this paper, an approach to gesture recognition bas ed on skeletal data using nearest neighbour classifier with dynamic time warping is presented. Since similar approaches are widely used in the literature, a few practical improvements that led to better recognition results are proposed. The approach is extensively evaluated on three publicly availab le gesture datasets and compared with state-of-the-art classifiers. For some gesture datasets, the proposed approach outperformed its competitors in terms of recognition rate and time of recognition.

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Section: Discussionmentioning

confidence: 99%

An Approach to Gesture Recognition with Skeletal Data Using Dynamic Time Warping and Nearest Neighbour Classifier

Ribó¹,

Warchoł²,

Oszust³

2016

IJISA

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show abstract

“…A regression-based model for the detection of the disease was proposed by [ 8 ] where a neural network and decision tree were utilized to that detect Parkinson’s disease. Saloni et al [ 9 ] proposed a similar approach [ 4 ] to detect Parkinson’s disease on the Unified Parkinson’s Disease Scale by using voice data. The proposed technique uses a support vector machine for the identification of Parkinson’s disease on the Unified Parkinson’s Disease Scale.…”

Section: Literaturementioning

confidence: 99%

“…The principal component analysis does dimension reductions by finding the correlation between the different features of the data. Once we were able to remove the multivariate property of the data, it was ready for the learning phase; the resulting dataset was transferred to an acoustic deep learning neural network where backpropagation help the model to reduce the error by minimizing the loss function [ 9 , 12 ]. The acoustic deep learning model was trained on 60% of the training data and tested on 30% of the testing dataset while the rest of the 10% was used for cross-validation.…”

Section: Proposed Modelmentioning

confidence: 99%

End-to-End Deep Learning Method for Detection of Invasive Parkinson’s Disease

et al. 2023

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Parkinson’s disease directly affects the nervous system are causes a change in voice, lower efficiency in daily routine tasks, failure of organs, and death. As an estimate, nearly ten million people are suffering from Parkinson’s disease worldwide, and this number is increasing day by day. The main cause of an increase in Parkinson’s disease patients is the unavailability of reliable procedures for diagnosing Parkinson’s disease. In the literature, we observed different methods for diagnosing Parkinson’s disease such as gait movement, voice signals, and handwriting tests. The detection of Parkinson’s disease is a difficult task because the important features that can help in detecting Parkinson’s disease are unknown. Our aim in this study is to extract those essential voice features which play a vital role in detecting Parkinson’s disease and develop a reliable model which can diagnose Parkinson’s disease at its early stages. Early diagnostic systems for the detection of Parkinson’s disease are needed to diagnose Parkinson’s disease early so that it can be controlled at the initial stages, but existing models have limitations that can lead to the misdiagnosing of the disease. Our proposed model can assist practitioners in continuously monitoring the Parkinson’s disease rating scale, known as the Total Unified Parkinson’s Disease Scale, which can help practitioners in treating their patients. The proposed model can detect Parkinson’s disease with an error of 0.10 RMSE, which is lower than that of existing models. The proposed model has the capability to extract vital voice features which can help detect Parkinson’s disease in its early stages.

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“…A 10-flod cross validation analysis has been carried out for all classification. The proposed model achieved 97.37% Aprajita Sharma and Ram Nivas [5] evaluated the performance of the model build using artificial neural networks (ANN), K-nearest neighbor (KNN), and SVM with radial basis function. The models offered a high accuracy of ~85.29%.…”

Section: Related Workmentioning

confidence: 99%

Parkinson’s Disease Diagnosis: Detecting the Effect of Attributes Selection and Discretization of Parkinson’s Disease Dataset on the Performance of Classifier Algorithms

Mohamed¹

2016

OALib

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Precise detection of PD is important in its early stages. Precise result can be achieved through data mining, classification techniques such as Naïve Bayes, support vector machine (SVM), multilayer perceptron neural network (MLP) and decision tree. In this paper, four types of classifiers based on Naïve Bayes, SVM, MLP neural network, and decision tree (j48) are used to classify the PD dataset and the performances of these classifiers are examined when they are implemented upon the actual PD dataset, discretized PD dataset, and selected set of attributes from PD dataset. The dataset used in this study comprises a range of voice signals from 31 people: 23 with PD and 8 healthy people. The result shows that Naïve Bayes and decision tree (j48) yield better accuracy when performed upon the discretized PD dataset with cross-validation test mode without applying any attributes selection algorithms. SVM gives high accuracy of 70% for training and 30% for the test when implemented on a discretized PD dataset and a splitting dataset. The MLP neural network gives the highest accuracy when used to classify actual PD dataset without discretization, attribute selection, or changing test mode.

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Voice Analysis for Telediagnosis of Parkinson Disease Using Artificial Neural Networks and Support Vector Machines

Cited by 15 publications

References 13 publications

An Approach to Gesture Recognition with Skeletal Data Using Dynamic Time Warping and Nearest Neighbour Classifier

An Approach to Gesture Recognition with Skeletal Data Using Dynamic Time Warping and Nearest Neighbour Classifier

End-to-End Deep Learning Method for Detection of Invasive Parkinson’s Disease

Parkinson’s Disease Diagnosis: Detecting the Effect of Attributes Selection and Discretization of Parkinson’s Disease Dataset on the Performance of Classifier Algorithms

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