# Complementarity-Preserving Generative Theory for Multimodal ECG Synthesis: A Quantum-Inspired Approach

arXiv: https://arxiv.org/abs/2603.26695 · HTML: https://mlsanity.com/paper/2603.26695/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 50 references that are in the map.
- First posted on arXiv 16 Mar 2026.

## Builds on (most cited first, 17 of 17)

- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [Multimodal Learning with Transformers: A Survey](https://mlsanity.com/paper/2206.06488/) (2022)
- [Adversarial Audio Synthesis](https://mlsanity.com/paper/1802.04208/) (2018)
- [A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models](https://mlsanity.com/paper/1611.03852/) (2016)
- [Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL](https://mlsanity.com/paper/2004.13701/) (2020)
- [Cross-Modal Retrieval: A Systematic Review of Methods and Future Directions](https://mlsanity.com/paper/2308.14263/) (2023)
- [SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification](https://mlsanity.com/paper/2006.15353/) (2020)
- [ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram Digitization](https://mlsanity.com/paper/2307.01946/) (2023)
- [Synthetic ECG Signal Generation Using Generative Neural Networks](https://mlsanity.com/paper/2112.03268/) (2021)
- [A Survey of Quantum Theory Inspired Approaches to Information Retrieval](https://mlsanity.com/paper/2007.04357/) (2020)
- [Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification](https://mlsanity.com/paper/2508.00963/) (2025)
- [Explainable Deep Neural Network for Multimodal ECG Signals: Intermediate vs Late Fusion](https://mlsanity.com/paper/2508.11666/) (2025)
- [Deep Learning for Personalized Electrocardiogram Diagnosis: A Review](https://mlsanity.com/paper/2409.07975/) (2024)
- [When Should a Model Change Its Mind? An Energy-Based Theory and Regularizer for Concept Drift in Electrocardiogram (ECG) Signals](https://mlsanity.com/paper/2602.22294/) (2026)
- [ECGNet: A generative adversarial network (GAN) approach to the synthesis of 12-lead ECG signals from single lead inputs](https://mlsanity.com/paper/2310.03753/) (2023)
- [QIXAI: A Quantum-Inspired Framework for Enhancing Classical and Quantum Model Transparency and Understanding](https://mlsanity.com/paper/2410.16537/) (2024)
- [Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification](https://mlsanity.com/paper/2411.18456/) (2024)

## Built on by (most cited first, 2 of 2)

- [Cardiac Stability Theory: An Axiomatically Grounded Framework for Continuous Cardiac Health Monitoring via Smartphone Photoplethysmography](https://mlsanity.com/paper/2604.23876/) (2026)
- [CFD-Guided Detection of Concept Drift in Multimodal Physiologic Signals](https://mlsanity.com/paper/2608.07759/) (2026)

## Abstract (arXiv)

Multimodal deep learning has substantially improved electrocardiogram (ECG) classification by jointly leveraging time, frequency, and time-frequency representations. However, existing generative models typically synthesize these modalities independently, resulting in synthetic ECG data that are visually plausible yet physiologically inconsistent across domains. This work establishes a Complementarity-Preserving Generative Theory (CPGT), which posits that physiologically valid multimodal signal generation requires explicit preservation of cross-domain complementarity rather than loosely coupled modality synthesis. We instantiate CPGT through Q-CFD-GAN, a quantum-inspired generative framework that models multimodal ECG structure within a complex-valued latent space and enforces complementarity-aware constraints regulating mutual information, redundancy, and morphological coherence. Experimental evaluation demonstrates that Q-CFD-GAN reduces latent embedding variance by 82%, decreases classifier-based plausibility error by 26.6%, and restores tri-domain complementarity from 0.56 to 0.91, while achieving the lowest observed morphology deviation (3.8%). These findings show that preserving multimodal information geometry, rather than optimizing modality-specific fidelity alone, is essential for generating synthetic ECG signals that remain physiologically meaningful and suitable for downstream clinical machine-learning applications.