  The inverse problem of inferring electrocardiogram (ECG) from
photoplethysmogram (PPG) is an emerging research direction that combines the
easy measurability of PPG and the rich clinical knowledge of ECG for long-term
continuous cardiac monitoring. The prior art for reconstruction using a
universal basis has limited fidelity for uncommon ECG waveform shapes due to
the lack of rich representative power. In this paper, we design two dictionary
learning frameworks, the cross-domain joint dictionary learning (XDJDL) and the
label-consistent XDJDL (LC-XDJDL), to further improve the ECG inference quality
and enrich the PPG-based diagnosis knowledge. Building on the K-SVD technique,
our proposed joint dictionary learning frameworks aim to maximize the
expressive power by optimizing simultaneously a pair of signal dictionaries for
PPG and ECG with the transforms to relate their sparse codes and disease
information. The proposed models are evaluated with 34,000+ ECG/PPG cycle pairs
containing a variety of ECG morphologies and cardiovascular diseases. We
demonstrate both visually and quantitatively that our proposed frameworks can
achieve better inference performance than previous methods, suggesting an
encouraging potential for ECG screening using PPG based on the proactive
learned PPG-ECG relationship.
