Cuff-free blood pressure continuous monitoring method based on ICG, PPG and ECG multi-mode physiological signals

By constructing a cuffless blood pressure prediction model based on multimodal physiological signal feature matrices of ICG, PPG, and ECG and a multi-head attention mechanism, the accuracy and applicability issues of cuffless blood pressure monitoring were solved, achieving high-precision continuous blood pressure monitoring and meeting clinical application needs.

CN121015153APending Publication Date: 2025-11-28CHONGQING UNIV
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Patent Information

Application Number
CN202511423943.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-30
Publication Date
2025-11-28

AI Technical Summary

Technical Problem

Existing technologies are insufficient for cuffless continuous blood pressure monitoring, resulting in unstable accuracy, poor individual applicability, and an inability to meet clinical application needs.

Method used

A cuffless blood pressure continuous monitoring method based on ICG, PPG and ECG multimodal physiological signals was adopted. By constructing a cuffless blood pressure prediction model with multimodal feature matrix and multi-head attention mechanism, and using multimodal physiological signals for deep learning training, continuous monitoring of cuffless blood pressure was achieved.

Benefits of technology

It improves the accuracy and individual applicability of cuffless blood pressure monitoring, meets the needs for continuous, non-invasive, and high-precision blood pressure monitoring, breaks through the bottlenecks in accuracy and applicability of traditional methods, and has significant clinical application value.

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Abstract

The invention relates to the technical field of blood pressure measurement, in particular to a cuff-free blood pressure continuous monitoring method based on ICG, PPG and ECG multi-mode physiological signals. According to the method, deep mining and fusion are carried out based on multi-modal physiological signals such as a chest impedance signal ICG, a photoelectric volume pulse wave signal PPG and an electrocardiosignal ECG of a tested object synchronously collected by multi-modal hemodynamics monitoring equipment, and a richer multi-modal feature matrix related to blood pressure changes is constructed; after deep learning fusion training is carried out on the constructed cuff-free blood pressure prediction model, prediction output of a cuff-free blood pressure prediction result can be directly carried out according to ICG, PPG and ECG multi-mode physiological signals of a to-be-detected tested object by means of the cuff-free blood pressure prediction model, the generalization ability and monitoring precision of continuous monitoring of the cuff-free blood pressure can be effectively improved, and the accuracy of continuous monitoring of the cuff-free blood pressure can be improved. The continuous, non-invasive and high-precision blood pressure monitoring requirements are met, and the clinical application value is more remarkable.
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