Key result
A novel approach combining formal runtime monitoring with statistical and regression analysis was developed to formally establish key relationships between ECG and PPG signals.
Why the study?
ECG and PPG are used separately for health monitoring tasks subject to errors and failures, creating a need for methods that consider both sensors to enhance safety.
A novel formal mapping between ECG and PPG signals was developed to enhance the safety and reliability of health monitoring applications.
May enhance ECG-PPG monitor safety; leaves open clinical validation before adoption.
Biophysical signals, such as electrocardiogram (ECG) and photoplethysmogram (PPG) are key to the sensing of vital parameters for wellbeing. Coincidentally, both ECG and PPG provide a different window into the same phenomena, namely, the cardiac cycle. However, they are used separately for various health monitoring tasks, which often have safety implications. As these sensors are subject to errors and failures, there is a need to develop methods that consider both sensors so as to enhance the safety of the health monitoring application. Considering this, we present the first approach to establish the key relationships between ECG and PPG signals formally. We combine formal runtime monitoring with statistical analysis and regression analysis for our results.
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Panda et al. (2022) studied this question. Mapping of ECG and PPG signals was evaluated on Establishment of key relationships between ECG and PPG signals. A novel approach combining formal runtime monitoring with statistical and regression analysis was developed to formally establish key relationships between ECG and PPG signals.
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