  Biophysical signals such as Electrocardiogram (ECG) and Photoplethysmogram
(PPG) are key to the sensing of vital parameters for wellbeing. Coincidentally,
ECG and PPG are signals, which provide a "different window" into the same
phenomena, namely the cardiac cycle. While they are used separately, there are
no studies regarding the exact correction of the different ECG and PPG events.
Such correlation would be helpful in many fronts such as sensor fusion for
improved accuracy using cheaper sensors and attack detection and mitigation
methods using multiple signals to enhance the robustness, for example.
Considering this, we present the first approach in formally establishing the
key relationships between ECG and PPG signals. We combine formal run-time
monitoring with statistical analysis and regression analysis for our results.
