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Convolutional-neural-network-based first lead electrocardiogram heartbeat classification method

A technology of convolutional neural network and classification method, which is applied in the field of first-lead electrocardiogram heart beat classification, can solve the problem of insufficient feature extraction, etc., and achieve the effect of fast classification speed, simplified convolution operation, and small amount of parameters

Inactive Publication Date: 2018-04-20
TIANJIN UNIV
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Problems solved by technology

[0009] The purpose of the present invention is to solve the problem of insufficient feature extraction in the existing electrocardiogram heart beat classification, and to provide a first lead electrocardiogram heart beat classification method based on convolutional neural network for the special first lead electrocardiogram signal

Method used

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  • Convolutional-neural-network-based first lead electrocardiogram heartbeat classification method

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Embodiment Construction

[0019] The key point of this method is how to design a convolutional neural network for the one-dimensional signal of the first lead ECG. In CNN, the convolutional layer extracts features by convolving the input image or the feature map generated by the intermediate layer. For a traditional convolutional layer, suppose X ∈ R H×W×D Represents the input three-dimensional image or feature map, where H and W represent the height and width of the feature map, respectively, and D represents the number of feature maps, also known as the number of channels. with ω ∈ R h×w×D×D' Represents the parameters of the convolution kernel, where h×w represents the size of the convolution window, D refers to the number of input channels, and D' is the number of channels generated by the current convolution layer. The output neuron s after the convolution operation is a scalar, and the calculation formula is:

[0020]

[0021] In the formula, ω k ∈ R h×w×D Indicates the size of the convolu...

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Abstract

The invention relates to a convolutional-neural-network-based first lead electrocardiogram heartbeat classification method. The method comprises: S1, preparing trained first lead electrocardiogram heartbeat data and a corresponding class label; S2, designing a convolutional neural network structure for the first lead electrocardiogram heartbeat by using a one-dimensional convolutional neural network and setting the number of convolutional layers as well as the number and sizes of characteristic maps of each convolutional layer; S3, carrying out forward calculation; to be specific, inputting normalized 196-length first lead heartbeat training data into the CNN, the data are processed by all intermediate layers successively, and extracting features of the intermediate layers; S4, carrying out error back propagation; to be specific, calculating classified losses and carrying out back propagation of the loses based on a chain rule; S5, updating a weight by using a gradient descent method;and S6, carrying out iterative training.

Description

technical field [0001] The invention relates to the fields of single-lead electrocardiogram, heartbeat classification and deep learning, and in particular to a method for first-lead electrocardiogram heartbeat classification. Background technique [0002] Electrocardiogram (ECG) reflects the changes of human heart potential. It has been invented and applied for more than 100 years. Due to its advantages of reliable diagnosis, simple method and non-invasive acquisition, it has gradually become the clinical diagnosis method for cardiovascular diseases. One of the most widely used and commonly used technical means in Traditional ECG analysis is done manually. Doctors observe the patient's ECG signal with the naked eye, and then make a final diagnosis based on relevant rules and personal experience. However, due to the huge amount of ECG signal data and the limited number of medical staff, this manual method will appear powerless. Omissions or false detections can easily occur...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06V2201/03G06N3/045G06F18/24
Inventor 李潇何宇清
Owner TIANJIN UNIV
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