Hypertension (the ‘silent killer’) is one of the main risk factors for cardiovascular diseases (CVDs), main cause of death worldwide. Its continuous monitoring can offer a valid tool for patients care, as blood pressure (BP) is a significant indicator of health and putting it together with other parameters, such as heart and breath rates, could strongly improve prevention of CVDs. In this work we investigate the cuff-less estimation of continuous BP through pulse transit time (PTT) and heart rate (HR) using regression techniques. Our approach is intended as the first step towards continuous BP estimation with a low error according to AAMI guidelines. The novelties introduced in this work are represented by the implementation of pre-processing and by the innovative method for features research and features processing to continuously monitor blood pressure in a non-invasive way. In fact, invasive methods are the only reliable methods for continuous monitoring, while non-invasive techniques recover the values in a discreet way. This approach can be considered the first step for the integration of this type of algorithms on wearable devices, in particular on the devices developed for SINTEC project.
To test the code related to the SHIMMER database, an example is provide. Files are organized according to the following description:
- SINTEC_Proj: .py file containing the Python code.
- Files: zip folder containing:
- Prova_Sofia: a .csv file with the timestamp values (1st column) and the control SBP (2nd column) and DBP (3rd column) values;
- Prova_Sofia_Session1_Shimmer_6C0E_Calibrated_SD: a .mat file with the selected PPG signal;
- Prova_Sofia_Session1_Shimmer_9404_Calibrated_SD: a .mat file with the selected ECG signal.