Wavelet-based Adaptive Boosting Method for Cuffless Blood Pressure Estimation on PYNQ-Z2

• Published in: International Conference on Signal Processing and Communications pp. 1 - 5
• Main Authors: Kumar, Vinit, Bharadwaj, Govindarajula V S Sai, Jayarajan, Jayesh, Gadani, Maulesh N, Sharma, Payal, Muduli, Priya Ranjan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2024
• Subjects: Blood pressure; Boosting; Cuffless BP estimation; Estimation; Feature extraction; Machine learning; Photoplethysmogram; PYNQ Board; Signal processing; Time-frequency analysis; Valves; Wavelets
• ISSN: 2474-915X
• DOI: 10.1109/SPCOM60851.2024.10631602

Hypertension or high blood pressure is a significant global health issue. Having high blood pressure is a big risk for conditions like coronary heart disease, including ischemic and hemorrhagic stroke. In general, the measurement of blood pressure is performed using a sphygmomanometer. However, this technique has several limitations in continuous and long-term monitoring due to bulky electronic devices with pneumatic systems (pump, valve, battery) to inflate and deflate the cuff. Cuffless blood pressure estimation has recently emerged as a good alternative to overcome these limitations. This paper proposes a machine learning-based approach using wavelet-based time-frequency features and adaptive boosting regression for cuffless blood pressure estimation from photoplethysmogram signals. The efficacy of the proposed approach is evaluated using various parameters concerning different state-of-the-art approaches. The proposed approach is found to perform better than various state-of-the-art methods. Furthermore, the proposed approach is implemented on the Xilinx PYNQ-Z2 board to validate the hardware compatibility.