Multi-level information fusion for learning a blood pressure predictive model using sensor data

The availability of commercial wearable bio-sensors provides an opportunity for developing smart phone applications for real-time diagnosis that can be used to improve the health of the user. We propose a multi-level information fusion approach for learning a predictive model for blood pressure (BP) using electrocardiogram (ECG) sensor data. The approach fuses the information on five different levels: i) data collection, where data from multiple ECG sensors is collected; ii) feature extraction, where features are extracted from the collected data by different preprocessing methods; iii) information fusion, fusing the evaluation information from different classifiers; iv) information fusion using the information from multi-target regression models for each BP class; and v) information fusion using the information from multi-target regression models from all configurations as a single model. This is used for predicting the blood pressure values (systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP)). Evaluating the methodology by using a separate test set indicates that the multi-level information fusion provides promising results, which are acceptable and comparable to the state-of-the-art results obtained for blood pressure prediction.