ArticleJournal of neuroscience methods2021
A convolutional-recurrent neural network approach to resting-state EEG classification in Parkinson's disease.
Article in Journal of neuroscience methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 97 citations in OpenAlex.
- Explainable EEG-based machine learning for early diagnosis of Alzheimer's disease and frontotemporal dementia.Scientific reports · 2026Article
- Individual cases of Parkinson's disease can be robustly classified using magnetoencephalography.NPJ Parkinson's disease · 2026Article
- Artificial Intelligence in Vascular Neurology: Applications, Challenges, and a Review of AI Tools for Stroke Imaging, Clinical Decision Making, and Outcome Prediction Models.Current neurology and neuroscience reports · 2025Review
- A Novel Fusion Framework Combining Graph Embedding Class-Based Convolutional Recurrent Attention Network with Brown Bear Optimization Algorithm for EEG-Based Parkinson's Disease Recognition.Journal of molecular neuroscience : MN · 2025Article
- Neural signals, machine learning, and the future of inner speech recognition.Frontiers in human neuroscience · 2025Review
- Multivariate Modelling and Prediction of High-Frequency Sensor-Based Cerebral Physiologic Signals: Narrative Review of Machine Learning Methodologies.Sensors (Basel, Switzerland) · 2024Review
- Artificial intelligence for brain disease diagnosis using electroencephalogram signals.Journal of Zhejiang University. Science. B · 2024Review
- Automatic rehabilitation exercise task assessment of stroke patients based on wearable sensors with a lightweight multichannel 1D-CNN model.Scientific reports · 2024Article
- Multi-Scale Feature and Multi-Channel Selection toward Parkinson's Disease Diagnosis with EEG.Sensors (Basel, Switzerland) · 2024Article
- Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures.bioRxiv : the preprint server for biology · 2024Article
- An interpretable model based on graph learning for diagnosis of Parkinson's disease with voice-related EEG.NPJ digital medicine · 2024Article
- Prediction of Arteriovenous Access Dysfunction by Mel Spectrogram-based Deep Learning Model.International journal of medical sciences · 2024Article
- The applied principles of EEG analysis methods in neuroscience and clinical neurology.Military Medical Research · 2023Review
- A review of emergent intelligent systems for the detection of Parkinson's disease.Biomedical engineering letters · 2023Review
- Automatic detection of Parkinson's disease from power spectral density of electroencephalography (EEG) signals using deep learning model.Physical and engineering sciences in medicine · 2023Article
- Distinguishing Parkinson's Disease with GLCM Features from the Hankelization of EEG Signals.Diagnostics (Basel, Switzerland) · 2023Article
- [Resting-state electroencephalogram relevance state recognition of Parkinson's disease based on dynamic weighted symbolic mutual information andSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Multi-scale convolutional recurrent neural network for psychiatric disorder identification in resting-state EEG.Frontiers in psychiatry · 2023Article
- Optimized MobileNetV3: a deep learning-based Parkinson's disease classification using fused images.PeerJ. Computer science · 2023Article
- Detection of Parkinson's disease from EEG signals using discrete wavelet transform, different entropy measures, and machine learning techniques.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
Abstract
backgroundParkinson's 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
methodWe propose a lightweight (~20 K parameters) deep learning model to classify resting-state EEG recorded from people with PD and healthy controls (HC). 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.
resultsThe CRNN model achieved 99.2% accuracy, 98.9% precision, and 99.4% recall in classifying PD from HC. Interrogating the model, we further demonstrate that the model is sensitive to dopaminergic medication effects and predominantly uses phase information in the EEG signals. COMPARISON WITH EXISTING
methodsThe CRNN model achieves superior performance compared to baseline machine learning methods and other recently proposed deep learning model.
conclusionThe approach proposed in this study adequately extracts spatial and temporal features in multi-channel EEG signals that enable accurate differentiation between PD and HC. The CRNN model 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's performance in clinical practice are warranted.
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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.