Arrhythmia prediction method

A prediction method and technology for abnormal heart rhythm, applied in diagnostic recording/measurement, medical science, diagnosis, etc., can solve problems such as misjudgment prone to occur

Pending Publication Date: 2020-06-26
广州天嵌计算机科技有限公司
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AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the technical problem that the above-mentioned single model is prone to misjudgment of ECG data, and to provide a method for predicting abnormal heart rate, using a variety of different mo

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Embodiment 1

[0029] Such as figure 1 As shown, the abnormal cardiac rhythm prediction method of the present invention comprises the following steps:

[0030] Step S100: Pass the ECG dataset through multiple CNN models to generate multiple feature vectors, and stack the multiple feature vectors to form an input vector.

[0031] Among them, the ECG data set is to obtain the electrical activity data generated by the human heart in real time. Specifically, the heart rate of the human body can be obtained through existing technologies such as smart bracelets or medical instruments to form an ECG data set. As an embodiment, the ECG data set is obtained by medical The instrument obtains an ECG, analyzes and obtains a medical ECG sample of the ECG. In order to further improve the accuracy of ECG abnormality prediction and classification, it is necessary to preprocess the ECG data set, specifically to enhance the medical ECG samples. The original medical ECG samples form new samples, and the new ...

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Abstract

The invention discloses an arrhythmia prediction method. The prediction method includes the following steps: generating, by an ECG data set, multiple feature vectors respectively through multiple CNNmodels, and stacking the multiple feature vectors to form input vectors; training the input vectors through multiple RNN models to obtain multiple bottom layer classifiers; and adopting a fully connected neural network to fuse the multiple bottom layer classifiers to predict abnormal ECG events. The multiple CNN models are adopted to extract the feature vectors of ECG data so as to form the inputvectors; the RNN is adopted to extract the timing information in the input vectors so as to form the bottom layer classifiers having the abilities of predicting abnormal ECG; and the bottom layer classifiers predict the ECG data again to obtain prediction results, and the prediction results can be fused to form the optimal prediction result by adopting the fully connected neural network so as to further make the prediction results more accurate.

Description

technical field [0001] The invention belongs to the technical field of cardiac electrophysiological analysis, and in particular relates to a method for predicting abnormal heart rhythm. Background technique [0002] Arrhythmia is an important group of diseases in cardiovascular diseases. It can occur alone or be accompanied by other cardiovascular diseases. It can suddenly attack and cause sudden death, and it can also continue to involve the heart and cause its failure. Therefore, accurate diagnosis and timely treatment of heart rhythm are effective measures to deal with cardiovascular diseases. Currently, an electrocardiogram (ECG) is used to detect and diagnose heart rhythm by recording the electrical activity of the heart every cardiac cycle from the body surface using an electrocardiograph. However, the doctor's analysis and judgment of the ECG is easily affected by subjective factors such as the doctor's professional ability or experience. At the same time, a large nu...

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Application Information

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IPC IPC(8): A61B5/0402A61B5/00G06K9/62G06N3/04
CPCA61B5/7267A61B5/7235A61B5/316A61B5/318G06N3/045G06F18/24G06F18/254G06F18/214
Inventor 黄健戴俊秀
Owner 广州天嵌计算机科技有限公司
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