ArticlePloS one2022
Resting-state electroencephalography based deep-learning for the detection of Parkinson's disease.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Houston, We Have AI Problem! Quality Issues with Neuroimaging-Based Artificial Intelligence in Parkinson's Disease: A Systematic Review.Movement disorders : official journal of the Movement Disorder Society · 2024Pooled it
- Technical system of electroencephalography-based brain-computer interface: Advances, applications, and challenges.Neural regeneration research · 2026Article
- Magnetic resonance imaging in Parkinson's disease: from routine imaging to molecular biomarkers - an update for radiologists.Polish journal of radiology · 2026Review
- Enhanced hybrid deep neural network for EEG-based schizophrenia diagnosis using functional and temporal features.Scientific reports · 2025Article
- Brain region localization: a rapid Parkinson's disease detection method based on EEG signals.Medical & biological engineering & computing · 2025Article
- Stimulus-Evoked Brain Signals for Parkinson's Detection: A Comprehensive Benchmark Performance Analysis on Cross-Stimulation and Channel-Wise Experiments.Bioengineering (Basel, Switzerland) · 2025Article
- Non-Invasive Biosensing for Healthcare Using Artificial Intelligence: A Semi-Systematic Review.Biosensors · 2024Review
- Data leakage in deep learning studies of translational EEG.Frontiers in neuroscience · 2024Article
- Deep-learning detection of mild cognitive impairment from sleep electroencephalography for patients with Parkinson's disease.PloS one · 2023Article
- A novel automated Parkinson's disease identification approach using deep learning and EEG.PeerJ. Computer science · 2023Article
- Article
- Parkinson's disease detection based on multi-pattern analysis and multi-scale convolutional neural networks.Frontiers in neuroscience · 2022Article
- Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
Parkinson's disease (PD) is one of the most serious and challenging neurodegenerative disorders to diagnose. Clinical diagnosis on observing motor symptoms is the gold standard, yet by this point nerve cells are degenerated resulting in a lower efficacy of therapeutic treatments. In this study, we introduce a deep-learning approach based on a recently-proposed 20-Layer Convolutional Neural Network (CNN) applied on the visual realization of the Wavelet domain of a resting-state EEG. The proposed approach was able to efficiently and accurately detect PD as well as distinguish subjects with PD on medications from subjects who are off medication. The gradient-weighted class activation mapping (Grad-CAM) was used to visualize the features based on which the approach provided the predictions. A significantly high accuracy, sensitivity, specificity, AUC, and Weighted Kappa Score up to 99.9% were achieved and the visualization of the regions in the Wavelet images that contributed to the deep-learning approach decisions was provided. The proposed framework can then serve as an effective computer-aided diagnostic tool that will support physicians and scientists in further understanding the nature of PD and providing an objective and confident opinion regarding the clinical diagnosis of the disease.
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