Non-invasive continuous blood pressure measurement using flexible sensing and intelligent estimation algorithms is evolving to address current limitations in measurement consistency and adaptability.
This review summarizes the evolution, current challenges, and future directions of non-invasive continuous blood pressure measurement using flexible sensors and intelligent algorithms.
Accurate and continuous, non-invasive blood pressure (BP) monitoring plays a vital role in the long-term management of cardiovascular diseases. Advances in wearable and flexible sensing technologies have facilitated the transition of non-invasive BP monitoring from clinical settings to ambulatory home environments. However, the measurement consistency and algorithm adaptability of existing devices have not yet reached the level required for routine clinical practice. To address these limitations, comprehensive innovations have been made in material development, sensor design, and algorithm optimization. This review examines the evolution of non-invasive continuous BP measurement, highlighting cutting-edge advances in flexible electronic devices and BP estimation algorithms. First, we introduce measurement principles, sensing devices and limitations of traditional non-invasive BP measurement, including arterial tonometry, arterial volume clamp, and ultrasound-based methods. Subsequently, we review the pulse wave analysis-based BP estimation methods from two perspectives: flexible sensors based on optical, mechanical, and electrical principles, and estimation models that use physiological features or raw waveforms as input. Finally, we conclude the existing challenges and future development directions of flexible electronic technology and intelligent estimation algorithms for non-invasive continuous BP measurement.
Shen et al. (Fri,) conducted a review in cardiovascular diseases. Non-invasive continuous blood pressure measurement was evaluated. Non-invasive continuous blood pressure measurement using flexible sensing and intelligent estimation algorithms is evolving to address current limitations in measurement consistency and adaptability.
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