Atrial fibrillation signal recognition method, device and equipment

Abstract

The invention provides atrial fibrillation signal recognition method, device and equipment, wherein the method comprises: acquiring an electrocardiogram signal to be recognized; inputting the electrocardiogram signal to an atrial fibrillation signal recognition model pre-established, and putting results of atrial fibrillation signal recognition, wherein the atrial fibrillation signal recognition model is established by acquiring a set amount of electrocardiogram sample signals and corresponding identifier information that includes normal signal and atrial fibrillation signal identifier information, using SMOTE (synthetic minority over-sampling technique) to balance atrial fibrillation signals according to the amount of normal signals, establishing a network structure of multiple convolutional neural networks each of which is provided with a specific receptive field for recognizing the atrial fibrillation signal of corresponding granular size, inputting the normal signals and the balanced atrial fibrillation signals to the network structure, and training. The atrial fibrillation signal recognition method, device and equipment can provide improved reliability and accuracy of atrial fibrillation signal recognition.

Description

technical field [0001] The present disclosure relates to the technical field of electrocardiogram monitoring, and in particular to a method, device and equipment for identifying an atrial fibrillation signal. Background technique [0002] Atrial fibrillation is the most common persistent chronic cardiac arrhythmia, often arising from disordered atrial activity and irregular atrial compression. The atrial fibrillation signal in the ECG signal can be identified by detecting the variability of the P wave or the RR interval, but the reliability and accuracy of these methods are difficult to meet the needs of clinical diagnosis, resulting in not being used in medical diagnosis or wearable monitoring equipment. widely used. [0003] With the rapid development of artificial intelligence, there is also a method of using machine learning to identify atrial fibrillation signals. However, this identification method requires a long ECG signal and has high requirements for data processi...

AI Technical Summary

Problems solved by technology

The atrial fibrillation signal in the ECG signal can be identified by detecting the variability of the P wave or the RR interval, but the reliability and accuracy of these methods are difficult to meet the needs of clinical diagnosis, resulting in not being used in medical diagnosis or wearable monitoring equipment. widely used

[0003] With the rapid development of artificial intelligence, there is also a method of using machine le

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