ECG-derived Blood Pressure Classification using Complexity Analysis-based Machine Learning

The recent advancement on wearable physiological sensors supports the development of real-time diagnosis in preventive medicine that demands various signal processing techniques to enable the extraction of the vital signs (e.g., blood pressure). Blood pressure estimation from physiological sensors data is challenging task that usually is solved by a combination of multiple signals. In this paper we present a novel complexity analysis-based machine-learning perspective on the problem of blood pressure class estimation only from ECG signals. We show that high classification accuracy of 96.68% can be achieved by extracting information via complexity analysis on the ECG signal followed by applying a stack of machine-learning classifiers. In addition, the proposed stacking approach is compared to a traditional machine-learning approaches and feature analysis is performed to determine the influence of the different features on the classification accuracy. The experimental data was gathered by daily monitoring of 20 subjects with two different ECG sensors.