Parkinson's Disease Diagnosis Based on Multivariate Deep Features of Speech Signal

Khojasteh, Parham; Viswanathan, R.; Aliahmad, Behzad; Ragnav, S.; Zham, Poonam; Kumar, Deepak

doi:10.1109/lsc.2018.8572136

Cited by 25 publications

(15 citation statements)

References 13 publications

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“…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] . Vaiciuknas et al [12] investigated the strategy for PD screening from sustained phoneme parameters and text-dependent speech modalities.…”

Section: Introductionmentioning

confidence: 99%

“…Low HNR is an indicator of the existence of dysarthria. Studies have reported the use of other features such as the fractal dimension (FD) [25] linear predictive model (LPM) [13] , multivariate deep features [21] , and entropy [25] . Many of these studies have shown these features are very effective in differentiating between the voices of PD and healthy people.…”

Section: Introductionmentioning

confidence: 99%

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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%

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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show abstract

“…Over the last few years, with the increase of computing power, several Deep Neural Network (DNN) techniques have emerged in PD detection. Some studies applied Convolutional Neural Networks on spectrograms (Vásquez-Correa et al, 2017;Khojasteh et al, 2018;Zhang et al, 2018). Others used DNNs to extract phonological features from MFCCs (Garcia-Ospina et al, 2018), or to detect directly PD from global features (Rizvi et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech

Jeancolas

Petrovska‐Delacrétaz

Mangone

et al. 2021

Front. Neuroinform.

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Many articles have used voice analysis to detect Parkinson's disease (PD), but few have focused on the early stages of the disease and the gender effect. In this article, we have adapted the latest speaker recognition system, called x-vectors, in order to detect PD at an early stage using voice analysis. X-vectors are embeddings extracted from Deep Neural Networks (DNNs), which provide robust speaker representations and improve speaker recognition when large amounts of training data are used. Our goal was to assess whether, in the context of early PD detection, this technique would outperform the more standard classifier MFCC-GMM (Mel-Frequency Cepstral Coefficients—Gaussian Mixture Model) and, if so, under which conditions. We recorded 221 French speakers (recently diagnosed PD subjects and healthy controls) with a high-quality microphone and via the telephone network. Men and women were analyzed separately in order to have more precise models and to assess a possible gender effect. Several experimental and methodological aspects were tested in order to analyze their impacts on classification performance. We assessed the impact of the audio segment durations, data augmentation, type of dataset used for the neural network training, kind of speech tasks, and back-end analyses. X-vectors technique provided better classification performances than MFCC-GMM for the text-independent tasks, and seemed to be particularly suited for the early detection of PD in women (7–15% improvement). This result was observed for both recording types (high-quality microphone and telephone).

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“…However, CNN and transfer learning techniques were not limited to imaging data; they also learn complex features from voices and signal data [ 29 ]. Numerous studies used the biomedical voice ( n = 21) [ 4 , 6 , 22 , 23 , 29 , 33 , 44 , 48 , 50 , 52 , 53 , 55 , 60 , 61 , 73 , 74 , 84 , 93 , 100 , 104 , 105 ] and biometric signal ( n = 14) [ 26 , 31 , 34 , 36 , 45 , 46 , 57 , 62 , 64 , 65 , 68 , 89 , 96 , 98 ]; a few of the included studies used EEG and EMG signals ( n = 5) [ 32 , 39 , 51 , 83 , 85 ].…”

Section: Resultsmentioning

confidence: 99%

The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

et al. 2021

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Background: Parkinson’s Disease (PD) is a chronic neurodegenerative disorder that has been ranked second after Alzheimer’s disease worldwide. Early diagnosis of PD is crucial to combat against PD to allow patients to deal with it properly. However, there is no medical test(s) available to diagnose PD conclusively. Therefore, computer-aided diagnosis (CAD) systems offered a better solution to make the necessary data-driven decisions and assist the physician. Numerous studies were conducted to propose CAD to diagnose PD in the early stages. No comprehensive reviews have been conducted to summarize the role of AI tools to combat PD. Objective: The study aimed to explore and summarize the applications of neural networks to diagnose PD. Methods: PRISMA Extension for Scoping Reviews (PRISMA-ScR) was followed to conduct this scoping review. To identify the relevant studies, both medical databases (e.g., PubMed) and technical databases (IEEE) were searched. Three reviewers carried out the study selection and extracted the data from the included studies independently. Then, the narrative approach was adopted to synthesis the extracted data. Results: Out of 1061 studies, 91 studies satisfied the eligibility criteria in this review. About half of the included studies have implemented artificial neural networks to diagnose PD. Numerous studies included focused on the freezing of gait (FoG). Biomedical voice and signal datasets were the most commonly used data types to develop and validate these models. However, MRI- and CT-scan images were also utilized in the included studies. Conclusion: Neural networks play an integral and substantial role in combating PD. Many possible applications of neural networks were identified in this review, however, most of them are limited up to research purposes.

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Parkinson's Disease Diagnosis Based on Multivariate Deep Features of Speech Signal

Cited by 25 publications

References 13 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

X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech

The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

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