The Recent Advancements to Measure the Blood Pressure Using Photoplethysmography, Electrocardiogram, and Microchannel

Abstract

Uncontrolled blood pressure poses significant health risks, making accurate measurement essential in healthcare. Conventional blood pressure measurement methods, typically using inflatable cuffs, can cause patient discomfort, tissue damage, and are unsuitable for long-term monitoring. Consequently, researchers are exploring noninvasive, cuffless methods that provide continuous and accurate blood pressure assessment. This article presents a comprehensive review of sensors and estimation models used in cuffless blood pressure monitors, with a focus on enhancing accuracy and minimizing calibration requirements. A literature search was conducted using Google Scholar and reputable journals, including IEEE, Frontiers, and MDPI, resulting in the selection of 35 relevant studies. The review examines innovative techniques based on electrical, mechanical, and optical sensors. Particular attention is given to photoplethysmography (PPG), electrocardiography (ECG), and bioimpedance (Bio-Z), which, when combined with advanced signal analysis and deep learning models, show promising results. PPG enables blood volume measurement at accessible sites like the fingertip or wrist, leveraging parameters such as pulse transit time. ECG, which directly reflects heart activity, is also widely used for blood pressure estimation. Recent advancements in machine learning have improved accuracy, with models such as HGCTNet (a hybrid CNN-Transformer architecture) achieving an error margin of 0.9 ± 6.5 mmHg for diastolic and 0.7 ± 8.3 mmHg for systolic blood pressures. Despite the potential, challenges remain, including the need for continuous calibration of PPG-based systems. Ongoing research aims to address these limitations by improving signal quality and developing robust algorithms. The demonstrated accuracy and reduced calibration requirements suggest that cuffless blood pressure monitoring technologies may soon become viable for widespread clinical and home use.

Keywords

blood pressure / medical sensor / monitoring / machine learning / PPG

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