Key result
A flexible optoelectronic patch capturing a hybrid EC-PPG signal achieved accurate arterial blood pressure estimations with mean errors of 1.69 mmHg for systolic and 0.89 mmHg for diastolic pressure.
Why the study?
Hemodynamics is fundamental for assessing cardiovascular health, disease states, and therapeutic outcomes, motivating the development of flexible optoelectronics for blood pressure monitoring.
Does a flexible optoelectronic patch capturing hybrid EC-PPG signals improve the accuracy of arterial blood pressure estimation compared to traditional individual physiological signals?
Does a flexible optoelectronic patch capturing hybrid EC-PPG signals improve the accuracy of arterial blood pressure estimation compared to traditional individual physiological signals?
A novel flexible optoelectronic patch capturing hybrid ECG and PPG signals provides highly accurate, AI-driven arterial blood pressure estimations.
Should not yet change cuff-based BP practice; leaves open larger trials confirming hybrid EC-PPG accuracy in patients.
Hemodynamics , the study of blood flow and circulatory forces, is fundamental for assessing cardiovascular health, disease states, and therapeutic outcomes. Here, we introduce a skin-conformable organic optoelectronic thin-film patch (ePatch) designed to simultaneously capture electrocardiography (ECG) and photoplethysmography (PPG) signals as a single hybrid signal, named the electrocardio-photoplethysmogram (EC-PPG). The ePatch integrates an organic electrochemical transistor (OECT), an organic photodiode , surface-mounted light-emitting diodes, and electrochemical electrodes on a flexible, skin-conforming substrate. By training deep learning models on the hybrid EC-PPG data, we achieved accurate arterial blood pressure estimations, with mean errors of just 1.69 mmHg for systolic and 0.89 mmHg for diastolic blood pressure, outperforming traditional predictions that rely on individual physiological signals as inputs. Our findings underscore the potential of EC-PPG as a compound hemodynamic signal for AI-driven vital sign monitoring and integrated, solution-processable soft electronics for clinical and point-of-care applications.
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Zhong et al. (2025) studied Blood pressure monitoring. Skin-conformable organic optoelectronic thin-film patch (ePatch) with deep learning vs. Traditional predictions relying on individual physiological signals was evaluated on Arterial blood pressure estimation accuracy (mean error) (Mean error 1.69 mmHg (systolic) and 0.89 mmHg (diastolic)). A flexible optoelectronic patch capturing a hybrid EC-PPG signal achieved accurate arterial blood pressure estimations with mean errors of 1.69 mmHg for systolic and 0.89 mmHg for diastolic pressure.
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