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Myocardial ischemia auxiliary detecting method based on deterministic learning theory

A technology that determines the learning theory and assists in detection. It is applied in the field of medical detection and can solve problems such as difficult analysis and limited application of VCG.

Active Publication Date: 2014-02-05
SOUTH CHINA UNIV OF TECH
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  • Description
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Problems solved by technology

Using ECG and VCG to diagnose myocardial ischemia can effectively improve the diagnostic rate, but in clinical practice, VCG is not widely used due to the relatively difficult analysis.

Method used

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  • Myocardial ischemia auxiliary detecting method based on deterministic learning theory
  • Myocardial ischemia auxiliary detecting method based on deterministic learning theory
  • Myocardial ischemia auxiliary detecting method based on deterministic learning theory

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Embodiment

[0047] The specific embodiment of the present invention selects the normal data P117 and myocardial infarction data P072 of the standard ECG database PTB (Physikalisch-Technische Bundesanstalt) database to illustrate the effectiveness of the method of the present invention for detecting myocardial ischemia-related diseases.

[0048] The steps of adopting the myocardial ischemia auxiliary detection method based on deterministic learning theory are as follows:

[0049] (1) ST-T ring data acquisition:

[0050] The numerical data of 12-lead electrocardiogram ECG were obtained by using the ECG data acquisition equipment: Ⅰ, Ⅱ, Ⅲ, aVR, aVL, aVF, V1, V2, V3, V4, V5 and V6. Due to the influence of interference and the weak characteristics of the ECG signal itself, the ECG signal is often buried in the noise, which can easily cause information loss or false information. Therefore, the signal should be preprocessed to reduce noise and enhance information. In this embodiment, two basic ...

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Abstract

The invention discloses a myocardial ischemia auxiliary detecting method based on a deterministic learning theory. The method includes the following steps that data is pre-processed to obtain electrocardiogram ST-T loop data; a deterministic learning algorithm is adopted to conduct local correct modeling on inner system dynamics of an ST-T loop, modeled system dynamics are displayed in a three-dimensional visualization mode to obtain CDVG; according to a CDVG form, and by combining high risk factors and clinic information, auxiliary detecting results are obtained. The method is characterized in that the CDVG is obtained and analyzed based on the deterministic learning algorithm. The method is applicable to myocardial ischemia detecting when an electrocardiogram is not obviously changed, can prompt cardiovascular diseases effectively, can be used for conducting evaluation of a cardiovascular disease treatment effect and has the advantages of being simple in detecting process, economical and noninvasive and reducing burdens of patients and the like.

Description

technical field [0001] The invention belongs to the technical field of medical detection, and in particular relates to an auxiliary detection method for myocardial ischemia based on deterministic learning theory. Extraction and detection methods. Background technique [0002] For a long time, cardiovascular disease has been recognized as one of the most serious diseases that endanger human life and health. Among them, the morbidity and mortality of myocardial infarction (MI) due to myocardial ischemia rank first among all kinds of diseases. Because some patients with myocardial ischemia have no obvious clinical symptoms or mild symptoms in the early stage of onset, the condition is easily overlooked. If a more sensitive detection method for myocardial ischemia is used in daily health checks, it is possible to pay close attention to its aura symptoms, and detect myocardial ischemia before any significant changes in the electrocardiogram, so that such patients can be diagnose...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0452
Inventor 王聪胡俊敏董训德欧陕兴
Owner SOUTH CHINA UNIV OF TECH
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