Arrhythmia prediction method

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...

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 models to predict the abnormal situation of ECG data, and using a fully connected neural network model Combine various prediction results to generate the best prediction results, so that the prediction results can more truly reflect the ECG data

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