Generalized Generative Deep Learning Models for Biosignal Synthesis and Modality Transfer.

Area of Science:

• Machine Learning
• Biomedical Engineering
• Artificial Intelligence

Background:

• Generative Adversarial Networks (GANs) excel at artificial data synthesis.
• Medical data synthesis is crucial due to privacy, cost, and expert access limitations.
• Biomedical research has underutilized GANs for biosignal generation and modality transfer.

Purpose of the Study:

• To analyze adversarial learning for 1D biosignal data synthesis.
• To explore GANs for biosignal modality transfer.
• To evaluate the generalizability of GAN models on unseen biosignal data.

Main Methods:

• Implementation of three deep generative adversarial network architectures: classical GAN, adversarial AE, and modality transfer GAN.
• Evaluation on diverse biosignal datasets including PCG, ECG, and vectorcardiogram.
• Subject-independent evaluation protocols to assess model generalizability.

Main Results:

• Superior performance in generating realistic biosignals with preserved domain characteristics.
• Successful biosignal modality transfer, enabling expanded representations from limited input leads.
• Generated longer-duration ECGs maintained clear rhythmic regions, validated by segmentation models.

Conclusions:

• Adversarial learning offers a powerful approach for 1D biosignal synthesis and modality transfer.
• GANs can significantly enhance medical data availability and facilitate more convenient patient monitoring.
• The developed models demonstrate strong generalizability and potential for clinical applications.