Abstract:
Aim:
This project investigates the unexplored clinical potentials of photoplethysmography (PPG) as an assessment tool for patients with atrial fibrillation (AF). We aim to investigate (1) the impact of risk factors on PPG, (2) how AF-related hemodynamic changes are reflected in PPG, and
(3) how ablation treatment for AF affects hemodynamics.

Background:
PPG is a technique that uses light to detect volumetric changes in the peripheral vasculature. It is widely available in wearables and provides a more continuous signal than electrocardiography (ECG). In research, PPG has been used to detect AF with high accuracy comparable to ECG. PPG is less well characterized than ECG, and it is unknown how ageing, hypertension, diabetes, and other risk factors as well as hemodynamics relating to AF are reflected in the PPG. This project will generate important basic knowledge on the clinical use of PPG and at the same time investigate the hemodynamics of AF, the most common arrhythmia worldwide.

Methods:
We will develop deep neural networks (DNN) for detecting hemodynamical patterns related to AF based on PPG recordings and characteristics from three independent cohorts comprising >6500 patients. Specifically, we will apply a DNN to (1) use PPG signals to distinguish patients with a risk factor (e.g. diabetes) from a patient without, (2) investigate how the hemodynamic changes before, during and after AF in PPG signals, and (3) distinguish between a patient’s hemodynamical pattern before and after they have received ablation therapy with PPG signals. To allow for linkage between the PPG signal and the outcome, we will specifically develop and apply explainable AI (xAI) methods for PPG analysis. xAI allows for a visual interpretation ofthe otherwise hidden decision-making of the DNN and graphically depicts the linkage of the
signal to the outcome. xAI has previously been used with ECG analysis and in this project, we will develop the method for use with PPG signals for characterisation of hemodynamics associated with the risk factors, paroxysmal AF, and AF management.

Perspectives

This project will provide a novel understanding of PPG necessary for future clinical use and investigate unknown mechanisms of AF. Firstly, we will characterize the effect of prevalent risk factors on the PPG with huge implications for PPG algorithm development. We will also determine to what degree PPG may be used as a gatekeeper for further diagnostic work-up and reduce the number of unnecessary tests for the benefit of patients and society. Secondly, we will generate important knowledge on AF mechanisms and on how hemodynamics are reflected in PPG signals and our findings will be part of the scientific foundation necessary for the use of PPG in healthcare, whether driven by industry or academia. Finally, this project will help gain mechanistic information on ablation as a treatment for AF and might eventually help inform personalized treatment.