ECG signal processing method based on two-level neural network with unbalanced training

A neural network and signal processing technology, applied in the field of signal processing, can solve problems such as high processing power consumption, manpower and time consumption, and increased computational complexity, so as to eliminate baseline drift and power frequency interference, ensure recognition accuracy, and save The effect of processing power consumption

CN109259756AActive Publication Date: 2019-01-25周军
8 Cites 8 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2019-01-25

Smart Images

  • Figure 1
    Figure 1
  • Figure 2
    Figure 2
  • Figure 3
    Figure 3
Patent Text Reader

Abstract

The invention discloses an ECG signal processing method based on an unbalanced training two-level neural network, which comprises the following steps: preprocessing: collecting ECG signal, adopting filter to eliminate baseline drift and power frequency interference, searching an R peak of the waveform of the ECG signal,and segmenting the beat of the ECG signal; signal recognition: ECG signals after heart beat segmentation are recognized by two-level neural network with unbalanced training, and abnormal ECG signals and normal ECG signals are obtained, wherein unbalanced training needs to be combined with two-level neural network; and compression processing: the ECG signal is subjected to adaptive compression based on intelligent diagnosis.
Need to check novelty before this filing date? Find Prior Art

Description

technical field

[0001] The invention relates to the technical field of signal processing, in particular to an ECG signal processing method based on a two-level neural network of unbalanced training. Background technique

[0002] Heart disease is one of the main diseases that threaten human life. For a long time, the research on heart disease has been an important topic in the medical field. Human electrocardiogram, as a comprehensive expression of heart electrical activity on the body surface, contains rich physiological and pathological information reflecting heart rhythm and its electrical conduction, so electrocardiogram is often used to analyze and judge various arrhythmias, and can also be used to diagnose myocardial damage It is of great reference value in guiding the treatment and rehabilitation of heart diseases, and it is also one of the most accurate methods for analyzing and identifying various arrhythmias.

[0003] As an important routine examination method in c...

Examples

Embodiment

[0041] like Figure 1 to Figure 3 As shown, this embodiment provides an ECG signal processing method based on a two-level neural network based on unbalanced training, which can not only ensure the accuracy of recognition, but also reduce the calculation workload, and ensure that the abnormal ECG signal remains complete and true. It also reduces processing energy consumption. Specifically, the following steps are included:

[0042] The first step, preprocessing: collect ECG signals, and use filters to eliminate baseline drift and power frequency interference; find the R peak of the waveform of the ECG signal, and perform cardiac beat segmentation of the ECG signal. For example, first, the collected ECG signal is sequentially filtered with a 0.5 Hz low-pass filter and a 50 Hz high-pass filter to eliminate baseline drift and power frequency interference in the waveform of the ECG signal. Then, the modulus maximum method based on wavelet transform is used to find the R-peak of t...