Arhythmia testing method for aiming at electrocardiogram data by means of binary neural network

Abstract

The invention discloses an arhythmia testing method for aiming at electrocardiogram data by means of a binary neural network. The method comprises the steps of firstly acquiring training model data, and preprocessing the data before training; constructing a set of full-precision convolutional network model, and inputting data for training, adjusting a network parameter for obtaining a relatively high effect; referring to the obtained full-precision models for constructing a binary network model, inputting the data for training, performing fine adjustment on a model parameter, and utilizing a Stop-BN training method in training for improving a training effect; using the trained full-precision model as a teacher model and using the untrained binary model as a student model, and performing distillation training on the student model by means of the teacher model, thereby obtaining a training effect better than that in directly training the binary network. The method performs discriminationon atrial fibrillation and can effectively reduce operation memory and operation time. The trained network model realizes relatively high effect for reducing precision loss caused by binarization.

Description

technical field [0001] The invention belongs to the field of deep learning, and is an arrhythmia detection application based on a convolutional neural network, which performs binary compression on its parameters and is applied to electrocardiogram data. Background technique [0002] Atrial fibrillation, the most common irregular heartbeat, occurs with very rapid and irregular contractions of the atria and carries a high risk of death, stroke, heart failure or coronary artery disease. According to statistics, the incidence of atrial fibrillation in the population is generally 1% to 2%. At present, the general diagnosis scheme is that the patient collects the electrocardiogram through the corresponding equipment, and then submits it to the doctor for diagnosis. This method obviously consumes a lot of manpower and material resources, and the efficiency is low. Therefore, it is necessary to construct an algorithm that can perform efficient and accurate diagnosis based on ECG da...

AI Technical Summary

Problems solved by technology

But there are also many problems. The current neural network model often has a large number of parameters, and the calculation cost is huge. However, there is a great demand for the arrhythmia detection algorithm to run on embedded devices or mobile devices.

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