Selection of voice parameters for Parkinson´s disease prediction from collected mobile data

Giuliano, Mónica; Garcia-Lopez, Alfonsa; Pérez, Silvia N.; Perez, Fransisco Diaz; Spositto, Osvaldo Mario; Bossero, Julio

doi:10.1109/stsiva.2019.8730219

Cited by 16 publications

(9 citation statements)

References 9 publications

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“…Recently, with the developments of new techniques such as convolutional neural network [ 101 ] and transfer learning [ 63 ], deep learning gained significant advances in the computer vision tasks, e.g., ImageNet [ 77 ]. Therefore, most of the studies used different imaging data to diagnose PD, such as MRI ( n = 12) [ 41 , 47 , 54 , 56 , 58 , 66 , 72 , 78 , 82 , 86 , 90 , 95 ] and handwritten images ( n = 9) [ 3 , 19 , 25 , 30 , 69 , 75 , 101 , 102 ], as well as PET and CT imaging ( n = 6) [ 28 , 59 , 67 , 71 , 88 , 90 ] and DaTscan imaging ( n = 4) [ 54 , 76 , 99 , 103 ]. However, CNN and transfer learning techniques were not limited to imaging data; they also learn complex features from voices and signal data [ 29 ].…”

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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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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“…Prediction performances considerably decreased for all tasks after matching for age and sex indicating the importance of controlling for such confounds in DB data. Such effects may also explain the high accuracies in some of the previous studies using mPower dataset, where no proper matching for these confounds was performed, age and/or sex were used as features despite a large imbalance across groups or non-balanced accuracies were reported [25], [27], [28], [32]. In example, in the overall mPower dataset HC outnumber PD by a factor of five and age and sex alone provide a high discrimination accuracy between PD and HC with PD being on average 28 years older and more often female (34% of PD vs 19% of HC).…”

Section: Discussionmentioning

confidence: 99%

“…Other potential data collection confounds comprise inclusion of several recordings per subject and use of signals of different time length [16], [25], [28], which may potentially lead the classifier to detect the idiosyncrasies of each subject rather than specific PD related symptoms, as demonstrated by Neto et al [29]–[31]. Whilst plausible, the impact of these confounds on ML-based detection of PD using different at-home digital assessments has not been yet systematically established and has indeed been ignored in many previous studies [16], [25], [28], [32], [33].…”

Section: Introductionmentioning

confidence: 99%

Smartphone-based digital biomarkers for Parkinson’s disease in a remotely-administered setting

Goñi

Eickhoff

Far

et al. 2021

Preprint

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BackgroundSmartphone-based digital biomarker (DB) assessments provide objective measures of daily-life tasks and thus hold the promise to improve diagnosis and monitoring of Parkinson’s disease (PD). To date, little is known about which tasks perform best for these purposes and how different confounds including comorbidities, age and sex affect their accuracy. Here we systematically assess the ability of common self-administered smartphone-based tasks to differentiate PD patients and healthy controls (HC) with and without accounting for the above confounds.MethodsUsing a large cohort of PD patients and healthy volunteers acquired in the mPower study, we extracted about 700 features commonly reported in previous PD studies for gait, balance, voice and tapping tasks. We perform a series of experiments systematically assessing the effects of age, sex and comorbidities on the accuracy of the above tasks for differentiation of PD patients and HC using several machine learning algorithms.ResultsWhen accounting for age, sex and comorbidities, the highest balanced accuracy on hold-out data (67%) was achieved using relevance vector machine on tapping and when combining all tasks. Only moderate accuracies were achieved for other tasks (60% for balance, 56% for gait and 55% for voice data). Not accounting for the confounders consistently yielded higher accuracies of up to 73% (for tapping) for all tasks.DiscussionOur results demonstrate the importance of controlling DB data for age and comorbidities. They further point to a moderate power of commonly applied DB tasks to differentiate between PD and HC when conducted in poorly controlled self-administered settings.

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“…Within this frame, the present research project was designed to test a novel COVID-19 screening tool based on voice analysis through machine learning (ML). Conventional voice analysis proved useful in detecting distinguishing acoustic features of pathologies impairing all structures and systems responsible for phonation, including lungs [17][18][19], trachea [20], larynx [21][22][23], vocal folds [24,25] and central nervous system [26][27][28][29][30]. Furthermore, encouraging results had been obtained for disorders impairing voice production mechanisms only secondarily, including cardiovascular diseases [31][32][33][34] and diabetes [35].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-based Voice Assessment for the Detection of Positive and Recovered COVID-19 Patients

et al. 2024

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Selection of voice parameters for Parkinson´s disease prediction from collected mobile data

Cited by 16 publications

References 9 publications

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

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

Smartphone-based digital biomarkers for Parkinson’s disease in a remotely-administered setting

Machine Learning-based Voice Assessment for the Detection of Positive and Recovered COVID-19 Patients

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