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📄 Abstract
Abstract: The coupling signal refers to a latent physiological signal that
characterizes the transformation from cardiac electrical excitation, captured
by the electrocardiogram (ECG), to mechanical contraction, recorded by the
phonocardiogram (PCG). By encoding the temporal and functional interplay
between electrophysiological and hemodynamic events, it serves as an intrinsic
link between modalities and offers a unified representation of cardiac
function, with strong potential to enhance multi-modal cardiovascular disease
(CVD) detection. However, existing coupling signal estimation methods remain
highly vulnerable to noise, particularly in real-world clinical and
physiological settings, which undermines their robustness and limits practical
value. In this study, we propose Noise-Robust Multi-Modal Coupling Signal
Estimation (NMCSE), which reformulates coupling signal estimation as a
distribution matching problem solved via optimal transport. By jointly aligning
amplitude and timing, NMCSE avoids noise amplification and enables stable
signal estimation. When integrated into a Temporal-Spatial Feature Extraction
(TSFE) network, the estimated coupling signal effectively enhances multi-modal
fusion for more accurate CVD detection. To evaluate robustness under real-world
conditions, we design two complementary experiments targeting distinct sources
of noise. The first uses the PhysioNet 2016 dataset with simulated hospital
noise to assess the resilience of NMCSE to clinical interference. The second
leverages the EPHNOGRAM dataset with motion-induced physiological noise to
evaluate intra-state estimation stability across activity levels. Experimental
results show that NMCSE consistently outperforms existing methods under both
clinical and physiological noise, highlighting it as a noise-robust estimation
approach that enables reliable multi-modal cardiac detection in real-world
conditions.
Key Contributions
This paper proposes NMCSE, a novel method for estimating coupling signals between ECG and PCG that is robust to noise. It reformulates the problem as distribution matching solved via optimal transport, jointly aligning amplitude and timing to enhance multi-modal cardiovascular disease detection.
Business Value
Improved accuracy and reliability in diagnosing cardiovascular diseases, potentially leading to earlier interventions and better patient outcomes. This could reduce healthcare costs associated with misdiagnosis or delayed treatment.