Method and device for extracting feature point of electrocardiosignal waveform

Abstract

The invention discloses a method for extracting the feature point of an electrocardiosignal waveform. The method comprises the steps of determining positions of a QRS wave point, a P wave point and a T wave point, wherein the position of the QRS wave point is determined by processing an electrocardiosignal by stationary wavelet transform, determining the QRS wave represented optimal target layer and T and P wave represented optimal target layers, finding out a maximum value and minimum value pair of the corresponding target layer, removing unqualified maximum value and minimum value pairs, carrying out error inspection and miss inspection on the position of the R wave point to obtain the final position of the R wave point, determining positions of the Q wave point and the S wave point, and determining positions of the P wave point and the T wave point according to the QRS wave determined by the T and P wave represented optimal target layers. According to the method, stationary wavelet transform is used in position determination of the QRS wave point, the T wave point and the P wave point, and can be used for effectively avoiding resolution damage when the dimension is increased in comparison with discrete wavelet transform, movement is not deformed, and the problem in the prior art can be effectively solved.

Description

technical field [0001] The invention relates to the field of medical technology, in particular to a method and device for extracting feature points of electrocardiographic signal waveforms. Background technique [0002] There are many kinds of wearable devices used to monitor human health in the prior art. Generally, the physiological information data of the human body is collected through sensors, and then these physiological data are analyzed to achieve the purpose of detecting the state of the human body. Among them, how to accurately extract the characteristic points (PQRST) in the ECG signal during the processing of the ECG signal is particularly important to realize the automatic judgment of the state, in which the P wave represents the fluctuation of the left and right new chambers, the QRS wave represents the contraction of the ventricle, and the T wave Represents ventricular repolarization. [0003] The traditional ECG signal feature point extraction method mainly ...

AI Technical Summary

Problems solved by technology

[0004] DWT processing method, in the JPEG image, the discrete cosine transform compresses the image into small blocks of 8*8. This algorithm considers discarding frequency information to achieve compression, because the higher the compression rate, the more frequency information is discarded. In extreme cases, the JPEG image only retains the basic information that reflects the appearance of the image, and the fine image details are lost, resulting in the loss of resolution of the DWT processing method when the scale becomes larger, and there is no movement or deformation

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