ArticleAnnals of translational medicine2020
Using a deep recurrent neural network with EEG signal to detect Parkinson's disease.
Article in Annals of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 56 citations in OpenAlex.
- Advancing spinal cord injury care through non-invasive autonomic dysreflexia detection with AI.Scientific reports · 2024Article
- Optimized adaptive neuro-fuzzy inference system based on hybrid grey wolf-bat algorithm for schizophrenia recognition from EEG signals.Cognitive neurodynamics · 2023Article
- A DM-ELM based classifier for EEG brain signal classification for epileptic seizure detection.Communicative & integrative biology · 2023Article
- Identification of Lower-Limb Motor Tasks via Brain-Computer Interfaces: A Topical Overview.Sensors (Basel, Switzerland) · 2022Review
- Application of Graph Neural Network in Driving Fatigue Detection Based on EEG Signals.Computational intelligence and neuroscience · 2022Article
- Application of Deep Learning Models for Automated Identification of Parkinson's Disease: A Review (2011-2021).Sensors (Basel, Switzerland) · 2021Review
- Retraction to using a deep recurrent neural network with EEG signal to detect Parkinson's disease.Annals of translational medicine · 2021Article
- A convolutional-recurrent neural network approach to resting-state EEG classification in Parkinson's disease.Journal of neuroscience methods · 2021Article
- The Role of Neural Network for the Detection of Parkinson's Disease: A Scoping Review.Healthcare (Basel, Switzerland) · 2021Article
Corrections and comments
- Retraction · 2021-09-01Error in Data ·
- Retracted
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundParkinson's disease (PD) gradually degrades the functionality of the brain. Because of its relevance to the abnormality of the brain, electroencephalogram (EEG) signal is used for the early detection of this disease. This paper introduces a novel computer-aided diagnosis method to detect PD, which is an efficient deep learning method based on a pooling-based deep recurrent neural network (PDRNN). Therefore, the purpose of this study is to detect Parkinson's disease based on deep recurrent neural network of EEG signal.
methodsThe EEG signals of 20 patients with Parkinson's disease and 20 healthy people in Henan Provincial People's Hospital (People's Hospital of Zhengzhou University) were examined, and a PDRNN learning method was applied on the dataset for managing the demand of the traditional feature presentation step.
resultsThe suggested DPRNN network gives the precision, sensitivity and specificity of 88.31%, 84.84% and 91.81%, respectively. Nevertheless, 11.28% of the healthy cases are wrongly categorized in Parkinson class. Also, 11.49% percent of Parkinson cases are classified wrongly in the healthy class.
conclusionsThe experimental model has high efficiency and can be used as a reliable tool for clinical PD detection. In future research, more cases should be used to test and develop the proposed model.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.