Method for realizing automatic recognition of atrial fibrillation on mini dynamic electrocardiogram monitoring equipment

Abstract

The invention discloses a method for realizing automatic recognition of atrial fibrillation on mini dynamic electrocardiogram monitoring equipment. The method includes: calling an MIT-BIH arrhythmia database, an MIT-BIH normal sinus rhythm database and a long-time atrial fibrillation database as training samples, and introducing an artificial neural network for learning training; setting a weight value for each layer of the artificial neural network, and inputting the training data samples to repeatedly and iteratively correct the weight value of each layer until training errors are less than a certain specified value, through the means, a weight value matrix capable of judging occurrence of atrial fibrillation can be found; utilizing the weight value matrix, and adding the same into an original artificial neural network to build a new artificial neural network; using collected electrocardiosignals of a target human body, processing the electrocardiosignals of the human body, acquiring a target human body feature vector X, and performing prediction operation according to the target human body feature vector X and the new artificial neural network.

Description

technical field [0001] The invention relates to detection of atrial fibrillation, in particular to a method for realizing automatic identification of atrial fibrillation on a miniature dynamic electrocardiogram monitoring device. Background technique [0002] Atrial fibrillation (abbreviated as atrial fibrillation, Auricular Fibrillation, AF) is the most common sustained cardiac arrhythmia. The incidence of atrial fibrillation continues to increase with age, reaching 10% of people over the age of 75. In atrial fibrillation, the frequency of atrial excitement reaches 300-600 beats / min, and the heartbeat frequency is often fast and irregular, sometimes up to 100-160 beats / min, which is not only much faster than normal heartbeat, but also absolutely irregular. When atrial fibrillation occurs, the atrium loses effective systolic function, and the blood easily stagnates in the atrium to form a thrombus. After the thrombus falls off, it can travel with the blood to all parts of t...

AI Technical Summary

Problems solved by technology

[0012] The present invention aims to overcome the shortcomings of severe time lag effect and low detection accuracy due to technical limitations in dynamic electrocardiogram equipment and technology in the detection of atrial fibrillation, to achieve continuous detection for a long time, and real-time, high-precision detection of atrial fibrillation. Automatic detection provides timely reference for the diagnosis and treatment of patients with atrial fibrillation, so as to reduce the possibility of high-risk events such as stroke and heart failure caused by atrial fibrillation. Through real-time and continuous ECG signal monitoring and artificial intelligence machine learning algorithms , make predictions and judgments on atrial fibrillation, create conditions for timely medical intervention, and possibly save patients' lives

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