  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.
