Method for generating noise model of mobile electrocardiogram signal

Abstract

The invention discloses a method for generating a noise model of a mobile electrocardiogram signal. The method comprises the following steps: using a time varying coefficient to replace an autoregressive coefficient in a traditional AR model sequence, forming a multistage time varying coefficient AR model, then a multistage time varying coefficient AR model sequence is obtained, wherein the sequence comprises an unknown time varying coefficient and an order; according to the obtained multistage time varying coefficient AR model sequence, using past values of the time varying coefficient and sequence to obtain an expression of a forecasting sequence; using a method of a basis function to estimate a time varying coefficient of the multistage time varying coefficient AR model sequence; based on a forgetting factor method, determining an order of the multistage time varying coefficient AR model sequence of a non-stationary random signal; and according to the time varying coefficient and order, finally obtaining the time varying coefficient to replace the traditional AR model sequence. According to the method for generating the noise model of the mobile electrocardiogram signal provided by the invention, compared with the methods like parameter estimation that a hypothetical sequence is stable in a short time, the invention directly uses the time varying AR model to simulate an ECG noise source model, which is more reasonable and small in structural error, thus a result is more accurate.

Description

technical field [0001] The invention relates to the field of signal processing, in particular to a method for generating a noise model of a mobile ECG signal. Background technique [0002] The noise model of the ECG signal can be used to construct the real ECG signal. In the research of ECG signal, it is often necessary to construct a simulation model of ECG. Usually the simulation of the ECG signal is to add various noise signals in the pure ECG signal. The more realistic the simulated model can simulate the real ECG signal, the more accurate the corresponding research results will be. [0003] Real ECG signals usually contain various noises such as baseline drift, power frequency, myoelectric interference, and electrode movement, so it is required to incorporate various noises into the simulated ECG signal to achieve the goal of being close to the real ECG. Although there are already some models to simulate noise, there are not many models that can simulate it accuratel...

AI Technical Summary

Problems solved by technology

This short duration is suitable for most phenomena, but for ECG signals, the modeling time using this method is too long for such phenomena, so the desired results cannot be achieved

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