Electrocardio diagnosis method based on combination of convolutional neural network and recurrent neural network

Abstract

The invention relates to the technical field of electrocardio signal processing, in particular to an electrocardio diagnosis method based on the combination of a convolutional neural network and a recurrent neural network. The method comprises the steps: S1, collecting electrocardiosignal training data, and attaching labels to the electrocardiosignal training data for data preprocessing; S2, performing data enhancement on the preprocessed training data; S3, constructing a joint neural network model, and training the joint neural network model by using the enhanced training data to obtain a training model; S4, acquiring a target electrocardiosignal, inputting the target electrocardiosignal into the training model for calculation, and outputting a probability value; S5, performing positive and negative example judgment according to the output probability value to obtain a classification judgment result. According to the method, the electrocardiogram diagnosis efficiency and accuracy canbe effectively improved, the provided training model can cover various complex electrocardiogram characteristics, and transfer learning of data is facilitated.

Description

technical field [0001] The invention relates to the technical field of electrocardiographic signal processing, in particular to an electrocardiographic diagnosis method based on the combination of a convolutional neural network and a cyclic neural network. Background technique [0002] Electrocardiogram examination is a routine inspection item in hospitals. Electrocardiogram is the most basic basis for doctors to judge a patient's heart condition. Electrocardiogram signals are electrical signals converted from non-stationary periodic biological signals caused by heart activity, and contain a large amount of complex heart activity information. At present, there are still some problems in the field of ECG diagnosis: 1. The training cycle of diagnostic doctors is long and the cost is high, and there is still a large gap for high-end talents; 2. The individual subjectivity of doctors varies greatly, and it is difficult to achieve a completely unified diagnostic standard ;3. Doct...

AI Technical Summary

Problems solved by technology

At present, there are still some problems in the field of ECG diagnosis: 1. The training cycle of diagnostic doctors is long and the cost is high, and there is still a large gap for high-end talents; 2. The individual subjectivity of doctors varies greatly, and it is difficult to achieve a completely unified diagnostic standard ;3. Doctors only observe with the naked eye, a lot of underlying information is invisible, and the information utilization rate is low; 4. There are too many similar diseases, and there is a lot of repetitive labor in the process of viewing pictures; 5. It takes a long time for doctors to read pictures, at least a few minutes to several hours

[0003] Using the traditional digital signal processing method, for a specific disease ECG, first manually extract features, and then make a judgment based on the threshold. This method requires a lot of medical and signal processing experience, and it is not universal. After changing the disease, Difficult to have a higher accuracy rate, or even work at all

The use of machine learning models or statistical learning models has achieved automation to a certain extent and has a certain degree of versatility. Compared with the first method, the accuracy rate has also been greatly improved, but the model still has insufficient expressive ability. problems, cannot cover a variety of complex situations, and is difficult to perform transfer learning

Images