Cardiac arrythmias classification algorithm based on convolutional neural network

A convolutional neural network and classification algorithm technology, applied in biological neural network models, neural architecture, medical science, etc., can solve the problems of complex network and time-consuming, and achieve the effect of simple classification process
CN110313894AInactive Publication Date: 2019-10-11SICHUAN UNIV
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Patent Information

Authority / Receiving Office
CN ยท China
Patent Type
Applications(China)
Current Assignee / Owner
SICHUAN UNIV
Publication Date
2019-10-11
Estimated Expiration
Not applicable ยท inactive patent

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Abstract

The invention discloses a cardiac arrythmias classification algorithm based on a convolutional neural network. The cardiac arrythmias classification algorithm comprises a small-scale Deep-LeNet network suitable for a sparse image of electrocardiosignals. According to the characteristics of a small convolutional core, the time consumed for network classification is shorter, and the accuracy is improved. Then, the convolutional neural network is put forward. The width of the network can be increased, the adaptability of the network to the scale is increased, the network is more suitable for recognizing the sparse image. Under the condition of quite slightly increasing the network time consumption, the classification accuracy of the network can be greatly increased; due to the integrated recognition and classification process, the cardiac arrythmias classification algorithm can be better used for family-practice-level diagnosis and has great significance in accurate recognition of cardiacarrythmias.
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Description

technical field

[0001] The invention belongs to the field of biomedical signal recognition, and relates to a ECG signal image classification technology, in particular to an ECG signal image feature extraction and classification technology based on a convolutional neural network, and specifically relates to building an ECG signal image classification technology. Convolutional neural network. Background technique

[0002] The ECG contains a wealth of pathological information about cardiac activity, the most important analysis of which is the classification of heart beats, which is very important for detecting arrhythmias. Since it is very time-consuming and impractical to diagnose arrhythmia by artificially analyzing long-term ECG signals, it is worth studying to use automatic algorithms to assist in the diagnosis of arrhythmia.

[0003] Convolutional Neural Networks (CNN) are a new form of deep learning in which the network structure consists of many hidden layers and parame...

Claims

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