Electrocardio abnormality early warning method and system based on AI intelligent ring

By integrating sensors into the smart ring to acquire wearing status information, filtering and analyzing ECG signal segments, and combining them with the user's historical data to assess the level of abnormal risk, the problem of insufficient accuracy and high false alarm rate of ECG abnormality warning in smart rings is solved, and efficient ECG abnormality warning is achieved.

CN122350722APending Publication Date: 2026-07-10SHENZHEN FANGYUANBAO INFORMATION TECH SERVICE CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHENZHEN FANGYUANBAO INFORMATION TECH SERVICE CO LTD
Filing Date
2026-04-14
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Existing ECG abnormality early warning technologies based on smart rings suffer from insufficient accuracy and high false alarm rates when there are frequent changes in wearing status, large fluctuations in signal quality, and individual differences.

Method used

By integrating multiple sensors into a smart ring to acquire wearing status information, filtering ECG signal segments that meet the reliability criteria, analyzing trend characteristics by combining the user's historical ECG data, assessing the level of abnormal risk based on risk evolution rules, and generating early warnings.

Benefits of technology

It improves the accuracy and reliability of ECG abnormality early warning, reduces the false alarm rate, and realizes the graded identification and early warning of ECG abnormality risks.

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Abstract

Embodiments of the present application can be applied to the technical field of smart rings, and specifically provide an electrocardio abnormality early warning method and system based on an AI smart ring, which comprises: acquiring an electrocardio signal collected by a smart ring in a wearing state, screening an electrocardio signal segment meeting a preset credible condition, analyzing a time period, acquiring a plurality of characteristic parameters of a corresponding time period of a same user in a historical period by using the time period, comparing the electrocardio signal segment with the plurality of characteristic parameters to extract a trend characteristic, evaluating an abnormality risk level of the electrocardio signal segment based on a preset risk evolution rule and the trend characteristic, and generating corresponding electrocardio abnormality early warning information and outputting the information to a terminal when the abnormality risk level meets a preset early warning condition. Through the above scheme, hierarchical identification and early warning of electrocardio abnormality risks are realized, the accuracy, continuity and early warning reliability of electrocardio abnormality monitoring are effectively improved, and the problems of insufficient electrocardio abnormality early warning accuracy and high false alarm rate are overcome.
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