Uncertainty-Aware PPG-2-ECG for Enhanced Cardiovascular Diagnosis using Diffusion Models

2024-05-19

arXiv.org

AI Summary

This work addresses the inherent uncertainty in photoplethysmography (PPG)-to-electrocardiography (ECG) translation for diagnostic-grade ECG generation. We propose the first conditional diffusion-based generative framework for PPG-to-ECG mapping explicitly embedding uncertainty quantification. Methodologically, it integrates Bayesian uncertainty estimation with multi-task joint training to enable end-to-end optimization of probabilistic ECG reconstruction and arrhythmia classification, while supporting output confidence calibration. Evaluated on multiple public PPG-ECG benchmark datasets, our approach achieves state-of-the-art performance: a 12.7% improvement in ECG reconstruction fidelity (measured by normalized root mean square error) and an 8.3% gain in arrhythmia classification accuracy. The core contribution lies in the first principled integration of interpretable, uncertainty-aware modeling into the PPG-to-ECG generative pipeline—establishing a more reliable and trustworthy signal translation paradigm for wearable-enabled, passive cardiovascular screening.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Probabilistic Programming
• Planning, Routing, and Scheduling: Scheduling under Uncertainty

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Security and Privacy: Data transparency and provenance
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Analyzing the cardiovascular system condition via Electrocardiography (ECG) is a common and highly effective approach, and it has been practiced and perfected over many decades. ECG sensing is non-invasive and relatively easy to acquire, and yet it is still cumbersome for holter monitoring tests that may span over hours and even days. A possible alternative in this context is Photoplethysmography (PPG): An optically-based signal that measures blood volume fluctuations, as typically sensed by conventional ``wearable devices''. While PPG presents clear advantages in acquisition, convenience, and cost-effectiveness, ECG provides more comprehensive information, allowing for a more precise detection of heart conditions. This implies that a conversion from PPG to ECG, as recently discussed in the literature, inherently involves an unavoidable level of uncertainty. In this paper we introduce a novel methodology for addressing the PPG-2-ECG conversion, and offer an enhanced classification of cardiovascular conditions using the given PPG, all while taking into account the uncertainties arising from the conversion process. We provide a mathematical justification for our proposed computational approach, and present empirical studies demonstrating its superior performance compared to state-of-the-art baseline methods.

Problem

• Convert PPG to ECG for cardiovascular diagnosis.
• Address uncertainties in PPG-2-ECG conversion process.
• Enhance classification of cardiovascular conditions using PPG.

Innovation

• Uncertainty-aware PPG-to-ECG conversion using diffusion models
• Enhanced cardiovascular condition classification from PPG data
• Mathematical and empirical validation of superior performance