PGANs: Personalized Generative Adversarial Networks for ECG Synthesis to Improve Patient-Specific Deep ECG Classification

Authors

  • Tomer Golany Technion – Israel Institute of Technology
  • Kira Radinsky Technion – Israel Institute of Technology

DOI:

https://doi.org/10.1609/aaai.v33i01.3301557

Abstract

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.

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Published

2019-07-17

How to Cite

Golany, T., & Radinsky, K. (2019). PGANs: Personalized Generative Adversarial Networks for ECG Synthesis to Improve Patient-Specific Deep ECG Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 557-564. https://doi.org/10.1609/aaai.v33i01.3301557

Issue

Section

AAAI Special Technical Track: AI for Social Impact