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

TL;DR

This paper introduces a novel uncertainty-aware method for converting PPG signals to ECG signals and improves cardiovascular diagnosis accuracy by accounting for conversion uncertainties using diffusion models.

Contribution

It presents a new diffusion model-based approach for PPG to ECG conversion that explicitly models uncertainty, enhancing diagnostic performance over existing methods.

Findings

Outperforms baseline methods in ECG classification accuracy

Effectively models uncertainty in PPG to ECG conversion

Provides a mathematically justified computational framework

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.