Using a deep recurrent neural network with EEG signal to detect Parkinson’s disease

Xu, Shixiao; Wang, Zhihua; Sun, Jingfan; Zhang, Zhiqiang; Wu, Zhaoyun; Tiezhao, Yang; Xue, Gang; Cheng, Chuance

doi:10.21037/atm-20-5100

Cited by 42 publications

(15 citation statements)

References 35 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%

“…In [ 38 , 83 ], we found that long short-term memory (LSTM) achieved outstanding results, indicating the best option to deal with EEG data. On the other hand, seven studies [ 3 , 19 , 25 , 27 , 40 , 69 , 101 , 102 ] focused on the classification of handwriting image to identify PD in the early stage, and we found that outstanding results were achieved in ANN + SVM in [ 3 ], dual-path RNN (DPRNN) in [ 40 ], and CNN + Optimum-Path Forest (OPF) in [ 102 ], respectively.…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%

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

et al. 2021

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“…AEN posed outstanding scores in terms of all performance metrics, leading to a conclusion that AEN is potential of detecting AD. In [170], the authors claimed that pooling based deep RNN on EEG signals to detect PD was investigated for the first time. The results of the study proved the model's compatibility in terms of PD detection.…”

Section: ) Ml-based Approaches In Pd Diagnosismentioning

confidence: 99%

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

et al. 2021

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Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure it. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer's disease (AD), brain tumor, epilepsy, and Parkinson's disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment. INDEX TERMS Alzheimer's disease, brain tumor, deep learning, epilepsy, Parkinson's disease, machine learning NOMENCLATURE The most commonly used abbreviations in this survey are summarized in Table 1.

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“…Deep learning models have been applied to PD EEG to perform a range of tasks such as discriminating on-medication from offmedication conditions (Shah et al, 2020) and classification between PD and healthy controls (HC) based on EEG collected during a specific task (Shi et al, 2019). However, there are only three studies (Lee et al, 2019a;Oh et al, 2018;Xu et al, 2020) that have deployed deep learning to discriminate between PD and HC using resting-state EEG. In (Oh et al, 2018), a thirteen-layer convolutional neural network (CNN) structure was proposed, which achieved 88.3% accuracy, 84.7% sensitivity, and 91.8% specificity using raw resting EEG data collected from 20 PD and 20 HC.…”

Section: Introductionmentioning

confidence: 99%

A convolutional-recurrent neural network approach to resting-state EEG classification in Parkinson’s disease

Lee

Hussein

Ward³

et al. 2021

Preprint

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Background: Parkinsons disease (PD) is expected to become more common, particularly with an aging population. Diagnosis and monitoring of the disease typically rely on the laborious examination of physical symptoms by medical experts, which is necessarily limited and may not detect the prodromal stages of the disease. New Method: We propose a lightweight (20K parameters) deep learning model, to discriminate between resting-state EEG recorded from people with PD and healthy controls. The proposed CRNN model consists of convolutional neural networks (CNN) and a recurrent neural network (RNN) with gated recurrent units (GRUs). The 1D CNN layers are designed to extract spatiotemporal features across EEG channels, which are subsequently supplied to the GRUs to discover temporal features pertinent to the classification. Results: The CRNN model achieved 99.2% accuracy, 98.9% precision, and 99.4% recall in classifying PD from healthy controls (HC). Interrogating the model, we further demonstrate that the model is sensitive to dopaminergic medication effects and predominantly uses phase information of the EEG signals. Comparison with Existing Methods: The CRNN model achieves superior performance compared to baseline machine learning methods and other recently proposed deep learning models. Conclusion: The approach proposed in this study adequately extracts the spatial and temporal features in multi-channel EEG signals that enable the accurate differentiation between PD and HC. It has excellent potential for use as an oscillatory biomarker for assisting in the diagnosis and monitoring of people with PD. Future studies to further improve and validate the model performance in clinical practice are warranted.

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Using a deep recurrent neural network with EEG signal to detect Parkinson’s disease

Cited by 42 publications

References 35 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

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

A convolutional-recurrent neural network approach to resting-state EEG classification in Parkinson’s disease

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