Key result
Wavelet-based machine learning outperforms state-of-the-art methods for cuffless blood pressure estimation.
Why the study?
Traditional sphygmomanometers have limitations in continuous and long-term monitoring due to bulky pneumatic systems, making cuffless blood pressure estimation an important alternative.
A novel wavelet-based adaptive boosting algorithm improves cuffless blood pressure estimation from photoplethysmogram signals and demonstrates hardware compatibility.
May advance cuffless BP estimation; leaves open prospective clinical validation before adoption.
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.
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Kumar et al. (2024) studied Hypertension. Wavelet-based adaptive boosting method vs. State-of-the-art approaches was evaluated on Cuffless blood pressure estimation efficacy. A machine learning approach using wavelet-based time-frequency features and adaptive boosting regression performed better than state-of-the-art methods for cuffless blood pressure estimation.
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