ArticleSensors (Basel, Switzerland)2020
Parkinson's Disease EMG Data Augmentation and Simulation with DCGANs and Style Transfer.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy.Frontiers in radiology · 2024Pooled it
- Flexible and Self-Powered Wearable Sensors for Tremor Monitoring in Parkinson'S Disease: Recent Advances in Materials and Device Architectures.Advanced healthcare materials · 2026Review
- Selective Motor Entropy Modulation and Targeted Augmentation for the Identification of Parkinsonian Gait Patterns Using Multimodal Gait Analysis.Life (Basel, Switzerland) · 2026Article
- Generative Adversarial Networks for Modeling Bio-Electric Fields in Medicine: A Review of EEG, ECG, EMG, and EOG Applications.Bioengineering (Basel, Switzerland) · 2026Review
- ChatEMG: Synthetic Data Generation to Control a Robotic Hand Orthosis for Stroke.IEEE robotics and automation letters · 2025Article
- Diffusion-based skin disease data augmentation with fine-grained detail preservation and interpolation for data diversity.PloS one · 2025Article
- A review of machine learning and deep learning for Parkinson's disease detection.Discover artificial intelligence · 2025Review
- CODI: Enhancing machine learning-based molecular profiling through contextual out-of-distribution integration.PNAS nexus · 2024Article
- EEGNet classification of sleep EEG for individual specialization based on data augmentation.Cognitive neurodynamics · 2024Article
- Deep learning and predictive modelling for generating normalised muscle function parameters from signal images of mandibular electromyography.Medical & biological engineering & computing · 2024Article
- Synthesis of sEMG Signals for Hand Gestures Using a 1DDCGAN.Bioengineering (Basel, Switzerland) · 2023Article
- Graph Neural Networks for Parkinson's Disease Monitoring and Alerting.Sensors (Basel, Switzerland) · 2023Review
- FoGGAN: Generating Realistic Parkinson's Disease Freezing of Gait Data Using GANs.Sensors (Basel, Switzerland) · 2023Article
- Decoding Mental Effort in a Quasi-Realistic Scenario: A Feasibility Study on Multimodal Data Fusion and Classification.Sensors (Basel, Switzerland) · 2023Article
- Article
- A myoelectric digital twin for fast and realistic modelling in deep learning.Nature communications · 2023Article
- Investigation of Phase Shifts Using AUC Diagrams: Application to Differential Diagnosis of Parkinson's Disease and Essential Tremor.Sensors (Basel, Switzerland) · 2023Article
- Deep Convolutional Generative Adversarial Network-Based EMG Data Enhancement for Hand Motion Classification.Frontiers in bioengineering and biotechnology · 2022Article
- A deep learning based framework for the registration of three dimensional multi-modal medical images of the head.Scientific reports · 2021Article
- Detecting Task Difficulty of Learners in Colonoscopy: Evidence from Eye-Tracking.Journal of eye movement research · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper proposes two new data augmentation approaches based on Deep Convolutional Generative Adversarial Networks (DCGANs) and Style Transfer for augmenting Parkinson's Disease (PD) electromyography (EMG) signals. The experimental results indicate that the proposed models can adapt to different frequencies and amplitudes of tremor, simulating each patient's tremor patterns and extending them to different sets of movement protocols. Therefore, one could use these models for extending the existing patient dataset and generating tremor simulations for validating treatment approaches on different movement scenarios.
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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.