PPG-to-ECG Signal Translation for Continuous Atrial Fibrillation Detection via Attention-based Deep State-Space Modeling

TL;DR

This paper introduces an attention-based deep state-space model that translates PPG signals into ECG waveforms, enabling more accurate atrial fibrillation detection from wearable device data.

Contribution

The proposed ADSSM model is subject-independent, noise-robust, and data-efficient, improving PPG-to-ECG translation for cardiovascular diagnosis.

Findings

Achieved PR-AUC of 0.986 in AFib detection using translated ECG signals.

Model is robust to noise and effective with limited data.

Facilitates early cardiovascular disease diagnosis from wearable devices.

Abstract

Photoplethysmography (PPG) is a cost-effective and non-invasive technique that utilizes optical methods to measure cardiac physiology. PPG has become increasingly popular in health monitoring and is used in various commercial and clinical wearable devices. Compared to electrocardiography (ECG), PPG does not provide substantial clinical diagnostic value, despite the strong correlation between the two. Here, we propose a subject-independent attention-based deep state-space model (ADSSM) to translate PPG signals to corresponding ECG waveforms. The model is not only robust to noise but also data-efficient by incorporating probabilistic prior knowledge. To evaluate our approach, 55 subjects' data from the MIMIC-III database were used in their original form, and then modified with noise, mimicking real-world scenarios. Our approach was proven effective as evidenced by the PR-AUC of 0.986 achieved when inputting the translated ECG signals into an existing atrial fibrillation (AFib) detector. ADSSM enables the integration of ECG's extensive knowledge base and PPG's continuous measurement for early diagnosis of cardiovascular disease.