An intelligent electrocardiogram classification method

A classification method and electrocardiogram technology, applied in medical science, sensors, diagnostic recording/measurement, etc., can solve problems such as noise interference and lack of real electrocardiogram data, and achieve the effect of improving speed and accuracy

Inactive Publication Date: 2019-01-11
HANGZHOU DIANZI UNIV
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Problems solved by technology

However, the shallowness of features, lack of real ECG data, noise interference, and the diversity of abnormal ECG have resulted in no significant

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  • An intelligent electrocardiogram classification method
  • An intelligent electrocardiogram classification method

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Embodiment Construction

[0022] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0023] figure 1 It is a schematic flowchart of an implementation method of intelligent electrocardiogram classification in the present invention. Such as figure 1 In the process shown, firstly, the waveform data of lead II ECG and the existing classification labels are obtained, and the unified ECG waveform data is 30 seconds, and the data samples whose length is longer than 30 seconds are collected for interception, and those less than 30 seconds are filled with zeros. The actual electrocardiogram belongs to the label of the neural network training data. Since the labels of the actual electrocardiogram are not uniform, ECG experts need to integrate and unify again. The electrocardiogram is inevitably interfered by external factors in the...

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Abstract

The invention discloses an intelligent electrocardiogram classification method. The invention performs 3 Hz and 4 Hz sampling on the raw data of I lead ECG waveform sampling points with a sampling rate of 500 Hz in 30 seconds, and performs 3 Hz sampling on the raw data of the I lead ECG waveform sampling points. 45 Hz band-pass filtering, R-peak recognition algorithm based on wavelet transform isused to extract R-peak position, extracting a heart beat template of 195 sampling points based on the R peak position, and then extracting the maximum amplitude of the ECG waveform, Minimum, Mean, Logarithmic entropy, amplitude and position of PQRS complex and other traditional ECG signal characteristics, using the original ECG waveform, corresponding classification labeling, gradient descent training deep residual network superposition long-short time memory network to extract the deep-level features of ECG waveform, and finally input all the extracted features into the stochastic forest model for classification and diagnosis. The invention can largely remove noise interference, reduce the dependence on artificial feature identification, and greatly improve the speed and accuracy of electrocardiogram classification.

Description

technical field [0001] The invention belongs to the technical field of intelligent electrocardiogram classification, and in particular relates to an intelligent electrocardiogram classification method based on machine learning and signal processing. Background technique [0002] With the continuous improvement of living standards, people pay more and more attention to the health of the heart, and the electrocardiogram has become an important reference for knowing the state of the heart because it contains a lot of information, is non-invasive, and is cheap. needs are becoming more and more urgent. The traditional electrocardiogram classification is analyzed by the naked eye, which relies heavily on the existing practical electrocardiogram classification experience. In the early days, it also tried to identify the characteristic waveforms by using traditional signal processing methods, and then performed auxiliary analysis through statistical analysis. However, some important...

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Application Information

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IPC IPC(8): A61B5/0402A61B5/00
CPCA61B5/7203A61B5/7225A61B5/725A61B5/7264A61B5/316A61B5/318
Inventor 韦张跃昊洪慧钱升谊
Owner HANGZHOU DIANZI UNIV
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