Electrocardiosignal processing method

An ECG signal and processing method technology, applied in medical science, sensors, diagnostic recording/measurement, etc., can solve problems such as complex training, poor ECG disease detection effect, and huge network structure.

Active Publication Date: 2015-04-22
SHENZHEN POLYTECHNIC
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Problems solved by technology

[0012] The technical problem to be solved by the present invention is that, aiming at the problems of the above-mentioned prior art method that the central electrical signal disease detection effect is not good a

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  • Electrocardiosignal processing method
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  • Electrocardiosignal processing method

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Embodiment Construction

[0064] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, the specific implementation manners of the present invention will now be described in detail with reference to the accompanying drawings.

[0065] The invention provides a method for processing electrocardiographic signals, the purpose of which is to collect the original electrocardiographic signals and directly use them as the input data of the deep learning ANN network, and automatically extract the important information through the multi-hidden layer 2 structure, thereby obtaining Better detection effect. This method automatically abstracts the base features at each level from the original data, which is one of the main advantages of deep learning ANN. And this method also avoids the situation that the default feature extraction is deviated due to the inaccurate positioning of the basic waveform.

[0066] Such as figure 2 as shown, figure 2 A flow c...

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Abstract

The invention discloses an electrocardiosignal processing method. The electrocardiosignal processing method includes the following steps that A, an original electrocardiosignal set theta is collected and preprocessed to construct an input electrocardiosignal signal theta'; B, a sample F' is extracted, a hidden layer is trained, and a self-learning set phi is output; C, a label vector G of the self-learning set phi is detected, and a node is output; D, an input layer, the hidden layer and an output layer of an artificial neural network are stacked, so that a complete electrocardiosignal disease monitoring artificial neural network is established. Thus, basic waveform positioning is not needed, dependence on artificially selected characteristics is avoided, the requirement for priori knowledge is low, important characteristics in data can be automatically and abstractly input, and the dimensionality of an input electrocardiosignal sample is reduced through PCA whitening dimensionality reduction applied during preprocessing.

Description

technical field [0001] The invention relates to the technical field of electrocardiographic signals, in particular to a method for processing electrocardiographic signals. Background technique [0002] Electrocardiography (ECG) is a time-varying potential signal collected through body surface electrodes when the human heart performs physiological activities, which contains rich biological information. As one of the earliest data indicators to be studied and applied in clinical medicine, ECG signals have important scientific research value and practical significance, and can effectively test and predict various diseases related to the heart. In recent years, more and more attention has been paid . But on the other hand, the ECG signal has the characteristics of high sample dimension, relatively small sample size, and nonlinear relationship in feature space, so its analysis is difficult. Currently, machine learning algorithms are generally used for processing. [0003] Such...

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Application Information

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IPC IPC(8): A61B5/0402
CPCA61B5/349
Inventor 曾启明赵杰宋荣
Owner SHENZHEN POLYTECHNIC
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