PGANs: Personalized Generative Adversarial Networks for ECG Synthesis to Improve Patient-Specific Deep ECG Classification
| Autor: | Tomer Golany, Kira Radinsky |
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| Rok vydání: | 2019 |
| Předmět: |
Training set
business.industry Computer science Deep learning 020206 networking & telecommunications 02 engineering and technology General Medicine Patient specific Machine learning computer.software_genre Generative model 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Classifier (UML) Generative grammar |
| Zdroj: | AAAI |
| ISSN: | 2374-3468 2159-5399 |
| DOI: | 10.1609/aaai.v33i01.3301557 |
| Popis: | The Electrocardiogram (ECG) is performed routinely by medical personnel to identify structural, functional and electrical cardiac events. Many attempts were made to automate this task using machine learning algorithms including classic supervised learning algorithms and deep neural networks, reaching state-of-the-art performance. The ECG signal conveys the specific electrical cardiac activity of each subject thus extreme variations are observed between patients. These variations are challenging for deep learning algorithms, and impede generalization. In this work, we propose a semisupervised approach for patient-specific ECG classification. We propose a generative model that learns to synthesize patient-specific ECG signals, which can then be used as additional training data to improve a patient-specific classifier performance. Empirical results prove that the generated signals significantly improve ECG classification in a patient-specific setting. |
| Databáze: | OpenAIRE |
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