Electrocardio signal identification method and system based on multi-feature sparse representation

A technology for sparsely representing coefficients and ECG signals. It is used in medical science, sensors, diagnostic recording/measurement, etc. Effect

Active Publication Date: 2020-02-14
HEZE UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Because there is a lot of data noise in the ECG signal, a f

Method used

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  • Electrocardio signal identification method and system based on multi-feature sparse representation
  • Electrocardio signal identification method and system based on multi-feature sparse representation
  • Electrocardio signal identification method and system based on multi-feature sparse representation

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Experimental program
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Embodiment 1

[0032] Embodiment 1, this embodiment provides an ECG signal identification method based on multi-feature sparse representation;

[0033] Such as figure 1 As shown, the ECG signal identification method based on multi-feature sparse representation includes:

[0034] S1: Obtain the ECG signal to be identified; perform noise elimination processing on the ECG signal to be identified;

[0035] S2: Carry out equal length processing on the ECG signal to be identified with the noise eliminated, and obtain several single-period ECG signals;

[0036] S3: performing multiple feature extractions on each single-period ECG signal;

[0037] S4: Input all the extracted features into the cross direction multiplier algorithm, solve multi-feature sparse representation coefficients, and finally obtain the optimal coefficient matrix;

[0038] S5: Input the optimal coefficient matrix into the pre-trained classifier, and output the identification result.

[0039] As one or more embodiments, in th...

Embodiment 2

[0088] Embodiment two, present embodiment also provides the ECG signal identification system based on multi-feature sparse representation;

[0089] ECG signal identification system based on multi-feature sparse representation, including:

[0090] A preprocessing module, which is configured to: obtain the ECG signal to be identified; perform noise removal processing on the ECG signal to be identified;

[0091] The segmentation module is configured to: perform equal length processing on the electrocardiographic signal to be identified with the noise eliminated to obtain several single-period electrocardiographic signals;

[0092] A multi-feature extraction module, which is configured to: perform multiple feature extraction on each single-period ECG signal;

[0093] The sparse representation module is configured to: input all the extracted features into the cross direction multiplier algorithm, solve multi-feature sparse representation coefficients, and finally obtain the optima...

Embodiment 3

[0095] Embodiment 3. This embodiment also provides an electronic device, including a memory, a processor, and computer instructions stored in the memory and run on the processor. When the computer instructions are executed by the processor, the computer instructions in Embodiment 1 are completed. steps of the method described above.

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Abstract

The invention discloses an electrocardio signal identification method and system based on multi-feature sparse representation. The method comprises the following steps: acquiring an electrocardio signal to be identified; performing noise cancellation processing on the electrocardio signal to be identified; performing cadence processing on the noise cancelled electrocardio signal to be identified to obtain a plurality of single-period electrocardio signals; performing multi-feature extraction on each single-period electrocardio signal; inputting all features obtained in extraction into an alternating direction method of multipliers, performing coefficient solving with multi-feature sparse representation to finally obtain an optimized coefficient matrix; and inputting the optimized coefficient matrix into a pre-trained classifier, and outputting an identification result.

Description

technical field [0001] The present disclosure relates to the technical field of ECG signal recognition, in particular to a method and system for ECG signal identification based on multi-feature sparse representation. Background technique [0002] The statements in this section merely mention background art related to the present disclosure and do not necessarily constitute prior art. [0003] Recently, as a new biometric identification technology, ECG has become a research hotspot due to its liveliness and difficulty of being imitated. There are many existing ECG signal identification methods, such as methods based on principal component analysis, linear discriminant analysis, fiducial point analysis, no fiducial point analysis, and deep neural network. [0004] In the process of realizing the present disclosure, the inventors found that the following technical problems existed in the prior art: [0005] At present, the existing ECG signal identification method only utiliz...

Claims

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Application Information

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IPC IPC(8): A61B5/0402A61B5/0452
CPCA61B5/7203A61B5/7235A61B5/318A61B5/349
Inventor 黄复贤黄玉文于继江刘春英
Owner HEZE UNIV
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