View source for 17757671. Electrocardiosignal Prediction Method and Apparatus, Terminal, and Storage Medium simplified abstract (HUAWEI TECHNOLOGIES CO., LTD.)

=Electrocardiosignal Prediction Method and Apparatus, Terminal, and Storage Medium=


==Organization Name==

[[:Category:HUAWEI TECHNOLOGIES CO., LTD.|HUAWEI TECHNOLOGIES CO., LTD.]]


[[Category:HUAWEI TECHNOLOGIES CO., LTD.]]

==Inventor(s)==

[[:Category:Luping Li of Beijing (CN)|Luping Li of Beijing (CN)]][[Category:Luping Li of Beijing (CN)]]

[[:Category:Maolin Chen of Beijing (CN)|Maolin Chen of Beijing (CN)]][[Category:Maolin Chen of Beijing (CN)]]

[[:Category:Yujia Han of Nanjing (CN)|Yujia Han of Nanjing (CN)]][[Category:Yujia Han of Nanjing (CN)]]

[[:Category:Miao Jia of Beijing (CN)|Miao Jia of Beijing (CN)]][[Category:Miao Jia of Beijing (CN)]]

[[:Category:Guangming Guo of Shenzhen (CN)|Guangming Guo of Shenzhen (CN)]][[Category:Guangming Guo of Shenzhen (CN)]]

==Electrocardiosignal Prediction Method and Apparatus, Terminal, and Storage Medium - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17757671 titled 'Electrocardiosignal Prediction Method and Apparatus, Terminal, and Storage Medium

==Simplified Explanation==

The patent application describes a method for predicting the occurrence of atrial fibrillation (AF) attacks in a target user based on their electrocardiosignal. Here is a simplified explanation of the abstract:

* The method involves obtaining the electrocardiosignal of a target user.
* The electrocardiosignal is then imported into a pre-trained atrial fibrillation signal classification model.
* The model determines the signal type of the electrocardiosignal, indicating whether it is indicative of atrial fibrillation or not.
* Based on the signal type, the method calculates a risk level of an atrial fibrillation occurrence.
* This risk level is used to predict whether the target user is likely to have an atrial fibrillation attack.

Potential applications of this technology:

* Medical diagnosis: The method can be used by healthcare professionals to predict the occurrence of atrial fibrillation attacks in patients, allowing for early intervention and treatment.
* Remote monitoring: The method can be implemented in wearable devices or remote monitoring systems to continuously monitor the electrocardiosignal of individuals at risk of atrial fibrillation, providing real-time alerts and improving patient care.

Problems solved by this technology:

* Early detection: By analyzing the electrocardiosignal, the method can identify the risk of atrial fibrillation attacks before they occur, enabling timely medical intervention and potentially preventing serious complications.
* Non-invasive monitoring: The method eliminates the need for invasive procedures or continuous hospitalization, allowing for convenient and cost-effective monitoring of atrial fibrillation.

Benefits of this technology:

* Improved patient care: By predicting atrial fibrillation attacks, healthcare providers can proactively manage patients' conditions, reducing the risk of complications and improving overall patient outcomes.
* Cost-effective monitoring: The method enables remote monitoring of atrial fibrillation, reducing the need for frequent hospital visits and lowering healthcare costs.
* Enhanced quality of life: Early detection and intervention can help individuals with atrial fibrillation lead a better quality of life by minimizing the impact of the condition on their daily activities.


==Original Abstract Submitted==

A method includes: obtaining an electrocardiosignal of a target user; importing the electrocardiosignal into a preset atrial fibrillation signal classification model, to obtain a signal type, of the electrocardiosignal, output by the atrial fibrillation signal classification model, where the atrial fibrillation signal classification model is obtained through training with an atrial fibrillation patient being a model training sample; and calculating, based on the signal type of the electrocardiosignal, a risk level of an atrial fibrillation occurrence, to predict whether the target user is to have an atrial fibrillation attack.



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