TY - JOUR
T1 - Monitoring blood pressure through a single hybrid hemodynamic signal with a flexible optoelectronic patch
AU - Zhong, Yizhou
AU - Zhang, Yongcao
AU - Pu, Jing
AU - Wustoni, Shofarul
AU - Uribe, Johana
AU - Lopez-Larrea, Naroa
AU - Marks, Adam
AU - McCulloch, Iain
AU - Mecerreyes, David
AU - Baran, Derya
AU - Inal, Sahika
N1 - Publisher Copyright:
© 2025 The Authors
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - DTI-3: Develop
KW - electrocardiography
KW - electrophysiology
KW - flexible electronics
KW - OECT
KW - OPD
KW - organic electrochemical transistor
KW - photoplethysmography
KW - wearable electronics
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U2 - 10.1016/j.device.2025.100778
DO - 10.1016/j.device.2025.100778
M3 - Article
AN - SCOPUS:105003804230
SN - 2666-9986
JO - Device
JF - Device
M1 - 100778
ER -