Electrocardiogram-signal identity recognition method based on NMF, evaluation method and device

Abstract

The invention discloses an electrocardiogram-signal identity recognition method based on NMF, an evaluation method and a device. The electrocardiogram-signal identity recognition method based on the NMF includes the steps that electrocardiogram signals are received, serve as sample points and are preprocessed; according to the electrocardiogram signal structure, a QRS wave section is subjected tobidirectional particle size scanning, the obtained QRS wave section, a wave section before QRS and a wave section after the QRS are integrated, and the preprocessed electrocardiogram signals are reconstructed; the reconstructed electrocardiogram signals are subjected to non-negative matrix factorization, and a coefficient matrix is obtained; a sub-component of the coefficient matrix is selected with the sub-component analysis method as a final characteristic value matrix of the electrocardiogram signals; electrocardiogram-signal identity recognition is carried out through the characteristic value matrix. Through the characteristic value matrix, a homologous similarity matrix and a heterogenous similarity matrix are calculated through mahalanobis distance, the error recognition rate, the false reject rate and the equal error rate are calculated through the homologous similarity matrix and the heterogenous similarity matrix, and the recognition effect on electrocardiogram-signal identityrecognition is subjected to measurement evaluation.

Description

technical field [0001] The disclosure belongs to the technical field of electrocardiographic signal identification, and relates to a method and device for identification and evaluation of electrocardiographic signals based on NMF. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] With the advancement and development of science and technology, due to the uniqueness of ECG signals, high security, and strong collectability, ECG signals can be used to identify identities. However, there are the following difficulties when using ECG signals for identification: [0004] First, in the acquisition process, it will be affected by external factors and cause a certain degree of distortion; [0005] Second, the ECG signal is also easily affected by the psychological and physiological activities of the human body; [0006] Therefore, the collected EC...

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Problems solved by technology

[0004] First, in the acquisition process, it will be affected by external factors and cause a certain degree of distortion;

[0005] Second, the ECG signal is also easily affected by the psychological and physiological activities of the human body;

[0008] In wavelet decomposition, it is difficult to select wavelet basis and the decomposition result is not unique;

[0009] In the empirical mode decomposition, since the ECG signal is a non-stationary signal, when the signal has large fluctuations, pulse interference, or large noise influence, it is easy to produce modal aliasing, which is not conducive to the analysis of the ECG signal. identify

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