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Electrocardiosignal R wave recognition method based on empirical wavelet transform (EWT) and structural feature extraction

A technology of ECG signal and structural features, applied in the field of signal processing, can solve the problems of large amount of calculation, high consumption of memory resources, difficult real-time detection, etc., and achieve the effect of fast calculation speed and high recognition effect

Active Publication Date: 2020-11-20
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

However, ANN requires a large amount of prior information of ECG signals in the training phase, which has a large amount of computation and high memory resource consumption, making it difficult to use for real-time detection[2][7]

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  • Electrocardiosignal R wave recognition method based on empirical wavelet transform (EWT) and structural feature extraction
  • Electrocardiosignal R wave recognition method based on empirical wavelet transform (EWT) and structural feature extraction
  • Electrocardiosignal R wave recognition method based on empirical wavelet transform (EWT) and structural feature extraction

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Embodiment Construction

[0041] Embodiments of the present invention are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation modes, and various modifications or changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the diagrams provided in the following embodiments are only schematically illustrating the basic concept of the present invention, and the following embodiments and the features in the embodiments can be combined with each other in the case of no conflict.

[0042] Wherein, the accompanying drawings are for illustrative purposes only, and represent only schematic diagrams, rather than physical drawings, and should...

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Abstract

The invention relates to an electrocardiosignal R wave recognition method based on empirical wavelet transform (EWT) and structural feature extraction, and belongs to the field of signal processing. Firstly, empirical wavelet transform is used for carrying out self-adaptive segmentation on an electrocardiosignal frequency spectrum, and a proper wavelet filter bank is constructed on a segmentationinterval to extract modal components with tight support. Then spectral analysis is carried out on each extracted modal component, to find out a high-frequency component corresponding to a R wave and carry out structural analysis on the high-frequency component, and thus accurate positioning of the R wave is achieved. Simulation results show that by using the algorithm, the average sensitivity of Rwave recognition of electrocardiosignals with noise can reach 99.93%, the average positive accuracy rate of the same can reach 99.99%, and the average accuracy rate of the same can reach 99.92%; andthe method has a good recognition effect and is of great significance to real-time monitoring of the electrocardiosignals.

Description

technical field [0001] The invention belongs to the field of signal processing, and relates to an electrocardiographic signal R-wave recognition method based on EWT and structural feature extraction. Background technique [0002] As the manifestation of cardiac electrical activity, ECG signal reflects the running state of the heart in real time, and is an important basis for clinical diagnosis of cardiovascular diseases. A standard cycle of ECG signals consists of P waves, QRS complexes, and T waves. Different waveforms correspond to different electrical activities of the heart [1]. The QRS complex is the most distinctive wave group in the ECG signal, among which the peak value of the R wave is the most prominent, and it is an important reference for determining other waves, segments, and intervals of the ECG signal[2]. Therefore, accurate detection of R waves is an important prerequisite for automatic ECG analysis. [0003] For the identification of the R wave in the ECG ...

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Application Information

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IPC IPC(8): A61B5/0456A61B5/352
CPCA61B5/7257A61B5/726A61B5/7225A61B5/7203
Inventor 李国权李必禄徐勇军李国军林金朝庞宇
Owner CHONGQING UNIV OF POSTS & TELECOMM
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