A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests

Behroozi, Mahnaz; Sami, Ashkan

doi:10.1155/2016/6837498

Cited by 48 publications

(17 citation statements)

References 28 publications

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“…Numbers of studies have investigated the voice parameters obtained from sustained phonemes to determine the differences between PD patients and healthy participants [15] – [19] . Behroozi and Sami [20] introduced a multi-classifier framework to separate PD patients from healthy controls. The use of deep learning has also been applied for the classification of voice recordings [21] .…”

Section: Introductionmentioning

confidence: 99%

Detecting Effect of Levodopa in Parkinson’s Disease Patients Using Sustained Phonemes

Pah

Motin

Kempster

et al. 2021

IEEE J. Transl. Eng. Health Med.

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Background: Parkinson’s disease (PD) is a multi-symptom neurodegenerative disease generally managed with medications, of which levodopa is the most effective. Determining the dosage of levodopa requires regular meetings where motor function can be observed. Speech impairment is an early symptom in PD and has been proposed for early detection and monitoring of the disease. However, findings from previous research on the effect of levodopa on speech have not shown a consistent picture. Method: This study has investigated the effect of medication on PD patients for three sustained phonemes; /a/, /o/, and /m/, which were recorded from 24 PD patients during medication off and on stages, and from 22 healthy participants. The differences were statistically investigated, and the features were classified using Support Vector Machine (SVM). Results: The results show that medication has a significant effect on the change of time and amplitude perturbation (jitter and shimmer) and harmonics of /m/, which was the most sensitive individual phoneme to the levodopa response. /m/ and /o/ performed at a comparable level in discriminating PD- off from control recordings. However, SVM classifications based on the combined use of the three phonemes /a/, /o/, and /m/ showed the best classifications, both for medication effect and for separating PD from control voice. The SVM classification for PD- off versus PD- on achieved an AUC of 0.81. Conclusion: Studies of phonation by computerized voice analysis in PD should employ recordings of multiple phonemes. Our findings are potentially relevant in research to identify early parkinsonian dysarthria, and to tele-monitoring of the levodopa response in patients with established PD.

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Section: Introductionmentioning

confidence: 99%

Detecting Effect of Levodopa in Parkinson’s Disease Patients Using Sustained Phonemes

Pah

Motin

Kempster

et al. 2021

IEEE J. Transl. Eng. Health Med.

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“…For comparison with study [59], filter-based method named A-MCFS feature selection approach was also included. A-MCFS also use Pearson’s correlation coefficient for selection of the most relevant features; features are stated in Table 5 and, to a certain extent, satisfy Pearson’s correlation coefficient r>|0.3114|.…”

Section: Resultsmentioning

confidence: 99%

Classifying Parkinson’s Disease Based on Acoustic Measures Using Artificial Neural Networks

Berus

Klančnik

Brezočnik

et al. 2018

Sensors

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In recent years, neural networks have become very popular in all kinds of prediction problems. In this paper, multiple feed-forward artificial neural networks (ANNs) with various configurations are used in the prediction of Parkinson’s disease (PD) of tested individuals, based on extracted features from 26 different voice samples per individual. Results are validated via the leave-one-subject-out (LOSO) scheme. Few feature selection procedures based on Pearson’s correlation coefficient, Kendall’s correlation coefficient, principal component analysis, and self-organizing maps, have been used for boosting the performance of algorithms and for data reduction. The best test accuracy result has been achieved with Kendall’s correlation coefficient-based feature selection, and the most relevant voice samples are recognized. Multiple ANNs have proven to be the best classification technique for diagnosis of PD without usage of the feature selection procedure (on raw data). Finally, a neural network is fine-tuned, and a test accuracy of 86.47% was achieved.

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“…This parameter takes a value between −1 and +1. The +1 coefficient means an excellent estimate, 0 indicates that the classifier is not better than random estimates, and −1 means a discrepancy between the actual and predicted values [25]:MCC=TP×TN−FP×FNTP+FPTP+FNTN+FPTN+FN.…”

Section: The Proposed Methodsmentioning

confidence: 99%

Optimization of SVM Parameters with Hybrid CS-PSO Algorithms for Parkinson’s Disease in LabVIEW Environment

Kaya

2019

Parkinson's Disease

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Optimization is the process of achieving the best solution for a problem. LabVIEW based on an SVM model is proposed in this paper to get the best SVM parameters using the hybrid CS and PSO method. PCA is used as a preprocessor of SVM for reducing the dimension of data and extracting features of training samples. Also, SVM parameters are optimized for Parkinson’s disease data by combining CS and PSO. The designed system is used to determine the best SVM parameters, and it is compared to PSO and CS optimization methods and found that the used CS-PSO hybrid optimization method is better. The hybrid model shows that the accuracy of the performance achieved is 97.4359%. Also, the data classification results obtained by using SVM parameters determined by optimization are measured by precision, recall, F1 score, false positive rate (FPR), false discovery rate (FDR), false negative rate (FNR), negative predictive value (NPV), and Matthews’ correlation coefficient (MCC) parameters.

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A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests

Cited by 48 publications

References 28 publications

Detecting Effect of Levodopa in Parkinson’s Disease Patients Using Sustained Phonemes

Detecting Effect of Levodopa in Parkinson’s Disease Patients Using Sustained Phonemes

Classifying Parkinson’s Disease Based on Acoustic Measures Using Artificial Neural Networks

Optimization of SVM Parameters with Hybrid CS-PSO Algorithms for Parkinson’s Disease in LabVIEW Environment

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