DeepVoice: A voiceprint-based mobile health framework for Parkinson's disease identification

Zhang, Hanbin; Wang, Aosen; Li, Dongmei; Xu, Wenyao

doi:10.1109/bhi.2018.8333407

Cited by 27 publications

(17 citation statements)

References 11 publications

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“…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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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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“…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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“…Several databases are available in the form of corpuses for the speech processing of PD patients. Few are like UCI (University of California at Irvine) machine learning database [2], National Center for Voice and Speech (NCVS) [9], mPower [10], Neurovoz, GITA, CzechPD, Albayzin, and Fisher SP [11]. Neurovoz dataset is collected at otorhinoaringology and neurology services of the Gregorio Mara˜nón hospital in Madrid, Spain.…”

Section: Databasesmentioning

confidence: 99%

Early Detection of Parkinson’s Disease Through Speech Features and Machine Learning: A Review

Gullapalli

Mittal

2021

ICT With Intelligent Applications

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Parkinson's Disease (PD) is a kind of neurodegenerative disorder. The disease causes communication impairment based on its progression. In general, identification of PD carried out based on medical images of brain. But it was recently identified that voice is acting as biomarkers for several neurological disorders. A review of speech features and machine learning algorithms is presented. This might be helpful for development of a non-invasive signal processing techniques for early detection of PD. Several models developed for disease detection is discussed, which are developed based on features like acoustic, phonation, articulation, dysphonia, etc. Machine learning algorithms like Logistic Regression (LG), Support Vector Machine (SVM), Boosting Regression Tree, bagging Regression, etc., and their performance accuracies in classification of Patient with PD (PWP) and Healthy Controls (HC) are reviewed. All these classification algorithms are trained and tested on several repository corpuses and customized datasets. The Spontaneous Speech (SS) is an efficient tool for the early detection of diseases like Parkinson's, Alzheimer's, Autism and several other dementia types in elderly people.

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DeepVoice: A voiceprint-based mobile health framework for Parkinson's disease identification

Cited by 27 publications

References 11 publications

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

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

Early Detection of Parkinson’s Disease Through Speech Features and Machine Learning: A Review

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