Runtime Monitoring and Statistical Approaches for Correlation Analysis of ECG and PPG

TL;DR

This paper introduces a novel approach combining runtime monitoring and statistical analysis to establish correlations between ECG and PPG signals, aiming to improve sensor fusion and attack detection in vital sign monitoring.

Contribution

It is the first to formally establish relationships between ECG and PPG signals using a combination of runtime monitoring and statistical methods.

Findings

Identified key correlations between ECG and PPG signals.

Demonstrated improved accuracy in sensor fusion.

Enhanced robustness against signal attacks.

Abstract

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.