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Heartbeat anomaly recognition algorithm based on deep learning technology

A technology of deep learning and abnormal identification, applied in the fields of informatics, medical science, medical informatics, etc., can solve the problems of irregular patient matching, high accuracy, harsh stability, weak ECG signal, etc., and achieve simple medical rules, Effects with a wide range of applications and high accuracy

Pending Publication Date: 2019-06-14
成都蓝景信息技术有限公司
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On the other hand, this patent is aimed at patch-type long-range ECG equipment, which has problems such as weak ECG signals, l

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  • Heartbeat anomaly recognition algorithm based on deep learning technology
  • Heartbeat anomaly recognition algorithm based on deep learning technology

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[0019] The following will be combined with Figure 1-Figure 2 The present invention is described in detail, and the technical solutions in the embodiments of the present invention are clearly and completely described. Apparently, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0020] The present invention provides a heartbeat abnormality recognition algorithm based on deep learning technology through improvement; this patent models the heartbeat recognition model as a sequence labeling problem, that is, the input is a series of heartbeat corresponding ECG signal segments, and the output is The type corresponding to each heartbeat, such as figure 1 Shown; Among them, the waveform represents the ECG signal, and the ...

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Abstract

The invention discloses a heartbeat anomaly recognition algorithm based on a deep learning technology. The heartbeat anomaly recognition algorithm models a heartbeat recognition model as a sequence labeling problem, that is, the input is a series of electrocardiosignal segments corresponding to the heartbeat, and the output is the type corresponding to each heartbeat. In view of the characteristics of the heartbeat recognition task, the heartbeat anomaly recognition algorithm divides the recognition process into two phases: the characteristic extraction phase and the classification phase. Besides, the heartbeat anomaly recognition algorithm is directed to a patch-type long-range electrocardiogram device which has problems such as weak electrocardiosignal, large interference, irregular patient matching mode and the like, and is more demanding on the accuracy and stability of CAD (computer aided diagnosis). The heartbeat anomaly recognition algorithm based on a deep learning technology aims to achieve high accuracy and high robustness in heartbeat anomaly recognition in electrocardiosignal with poor signal quality and high interference by using the artificial intelligence technology,so as to greatly reduce the subsequent labor cost and shorten the cycle of generating a final conclusion.

Description

technical field [0001] The present invention relates to an abnormal heartbeat recognition algorithm, specifically, an abnormal heartbeat recognition algorithm based on deep learning technology. Background technique [0002] According to statistics, about 540,000 people die of sudden cardiac death in my country every year, and nearly 90% of sudden deaths are caused by arrhythmia. It can be seen that arrhythmia is very harmful to the human body. Electrocardiogram is one of the most commonly used clinical examination methods in the detection of arrhythmia, which has the characteristics of high accuracy, affordable price, and no side effects. The common electrocardiogram analysis system usually first obtains preliminary results with the help of computer aided diagnosis (CAD), and then the electrocardiologist merges and corrects to draw the final conclusion. In CAD, heartbeat (one cardiac cycle or one heartbeat) analysis is the basis for arrhythmia analysis. The accuracy of he...

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Application Information

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IPC IPC(8): G16H50/20A61B5/00A61B5/0402
Inventor 韩伟
Owner 成都蓝景信息技术有限公司
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