A Deep Learning Based Method for Parkinson’s Disease Detection Using Dynamic Features of Speech

Quan, Changqin; Ren, Kang; Luo, Zhiwei

doi:10.1109/access.2021.3051432

Cited by 81 publications

(41 citation statements)

References 41 publications

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“…Around half of the included studies used convolution neural networks ( n = 37); afterward, other neural networks ( n = 31) were implemented in the included studies, followed by artificial neural networks (ANNs) ( n = 10), recurrent neural networks (RNNs) ( n = 9), and fuzzy neural networks (FNNs), as shown in Table 3 . In the end, the most imitated neural network architecture in the included studies was LSTM ( n = 11) [ 6 , 34 , 36 , 38 , 40 , 65 , 70 , 74 , 77 , 80 , 83 ], VGG ( n = 3) [ 18 , 27 , 58 ], and DNN ( n = 6) [ 34 , 35 , 60 , 91 , 92 , 103 ]. 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 ].…”

Section: Resultsmentioning

confidence: 99%

“…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%

“…In the included studies [ 6 , 18 , 19 , 30 , 61 , 75 , 87 , 91 , 92 , 96 , 98 , 101 ], clinicians could obtain PD Patient data in telemonitoring using devices such as tablets and smartphones. It is a promising solution because they can increase monitoring frequency without putting a strain on professional resources during the COVID-19 pandemic.…”

Section: Discussionmentioning

confidence: 99%

“…PD symptoms are characterized such as motor and nonmotor [ 4 ]. Motor symptoms include movement disorders, shaking, walking issues [ 5 ], stiffness, and postural instability [ 6 ], while nonmotor symptoms including cognitive dysfunction, mood disorder [ 7 ], depression, and anxiety [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…However, these symptoms also resemble other symptoms of an average person. is is why diagnostic technicians and clinical practitioners are nowadays exploring machine learning and artificial intelligence approaches [12][13][14] to predict the presence and severity of disease among their subjects. e main contribution of this article is as follows:…”

Section: Introductionmentioning

confidence: 99%

Assessment of Acoustic Features and Machine Learning for Parkinson’s Detection

Pramanik

Pradhan

Nandy

et al. 2021

Journal of Healthcare Engineering

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This article presents a machine learning approach for Parkinson’s disease detection. Potential multiple acoustic signal features of Parkinson’s and control subjects are ascertained. A collaborated feature bank is created through correlated feature selection, Fisher score feature selection, and mutual information-based feature selection schemes. A detection model on top of the feature bank has been developed using the traditional Naïve Bayes, which proved state of the art. The Naïve Bayes detector on collaborative acoustic features can detect the presence of Parkinson’s magnificently with a detection accuracy of 78.97% and precision of 0.926, under the hold-out cross validation. The collaborative feature bank on Naïve Bayes revealed distinguishable results as compared to many other recently proposed approaches. The simplicity of Naïve Bayes makes the system robust and effective throughout the detection process.

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Machine Learning for Parkinson’s Disease and Related Disorders

Faouzi

Colliot

Corvol

2023

Machine Learning for Brain Disorders

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Parkinson’s disease is a complex heterogeneous neurodegenerative disorder characterized by the loss of dopamine neurons in the basal ganglia, resulting in many motor and non-motor symptoms. Although there is no cure to date, the dopamine replacement therapy can improve motor symptoms and the quality of life of the patients. The cardinal symptoms of this disorder are tremor, bradykinesia, and rigidity, referred to as parkinsonism. Other related disorders, such as dementia with Lewy bodies, multiple system atrophy, and progressive supranuclear palsy, share similar motor symptoms although they have different pathophysiology and are less responsive to the dopamine replacement therapy. Machine learning can be of great utility to better understand Parkinson’s disease and related disorders and to improve patient care. Many challenges are still open, including early accurate diagnosis, differential diagnosis, better understanding of the pathologies, symptom detection and quantification, individual disease progression prediction, and personalized therapies. In this chapter, we review research works on Parkinson’s disease and related disorders using machine learning.

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A Deep Learning Based Method for Parkinson’s Disease Detection Using Dynamic Features of Speech

Cited by 81 publications

References 41 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

Assessment of Acoustic Features and Machine Learning for Parkinson’s Detection

Machine Learning for Parkinson’s Disease and Related Disorders

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