Cuff-less blood pressure estimation using Kalman filter on android platform

The continuous monitoring of blood pressure (BP) has been found to significantly predict the risk of severe cardiovascular disease. Pulse arrival time (PAT), generally extracted from synchronized photoplethysmogram (PPG) and electrocardiogram (ECG) signals, is widely adopted in noninvasive blood pressure studies. However, motion artifact and physical activities introduce different levels of noise to the ECG and PPG signals in wearable devices, resulting in large fluctuations in PAT-based BP estimation, which may confuse and mislead users. We explored the potential of Kalman filter to enhance the stability of continuous BP estimation. We developed an Android application collecting data from the wearable device via Bluetooth technology, two Kalman filters were designed and implemented to evaluate the systolic blood pressure (SBP) and pulse pressure (PP) separately, whose gains were adjusted automatically by signal quality indicators. Validation experiments were performed on 6 volunteers to ensure the effectiveness of Kalman filters, and the preliminary results compared with a standard commercial sphygmomanometer showed that our approach can achieve higher stability than the method without Kalman filtering.