Pulse regression model-based electrocardiography data correction method and system

Abstract

The invention discloses a pulse regression model-based electrocardiography data correction method and system. The method includes the steps of: constructing a regression model by using an artificial neural network; performing training on the regression model by utilizing acquired pulse signal data and acquired electrocardiography data to obtain trained regression model; serving the pulse signal data as a network input of the trained regression model; serving an output as estimated electrocardiography data; and then correcting the electrocardiography data according to a value difference between the acquired electrocardiography data and the estimated electrocardiography data. The arithmetic speed of the method is far above that of the conventional methods.

Description

technical field [0001] The present invention relates to the field of electrocardiographic signals, in particular to a method and system for correcting electrocardiographic signal data based on a pulse regression model. Background technique [0002] The existing electrocardiography (ECG) data analysis algorithm mainly includes two steps: first, detect and locate the basic waveform in the original signal, and extract its characteristic information; then, use machine learning algorithm to classify / Regression analysis, predicting its target state. [0003] like figure 1 As shown, the ECG signal in a normal cardiac cycle can be regarded as composed of four basic waveforms: P wave, QRS wave group, T wave and U wave. The feature extraction process is to calculate the included preset index information by measuring the four basic waveforms of the input signal. Existing algorithms generally use peak detection, wavelet analysis, etc. to measure the basic waveforms, and the extracte...

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Problems solved by technology

[0010] In view of the above-mentioned deficiencies in the prior art, the object of the present invention is to provide an electrocardiographic signal feature selection method and system based on the Memetic algorithm, aiming to solve the problems of poor learning effect, incomplete feature extraction, and low efficiency in existing feature extraction and selection methods. The problem of low accuracy

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