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

TL;DR

This paper introduces a quantum-inspired generative framework, CPGT, for multimodal ECG synthesis that explicitly preserves cross-domain complementarity, leading to more physiologically consistent synthetic data.

Contribution

It proposes the Complementarity-Preserving Generative Theory and instantiates it with Q-CFD-GAN, a novel quantum-inspired model that maintains multimodal ECG structure within a complex-valued latent space.

Findings

Reduces latent embedding variance by 82%

Decreases classifier-based plausibility error by 26.6%

Restores tri-domain complementarity from 0.56 to 0.91

Abstract

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.