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A ECG classification method based on fuzzy inference combined with weighted similarity measures for non-therapeutic purposes

A technology of fuzzy reasoning and similarity measurement, applied in reasoning methods, medical automated diagnosis, computer-aided medical procedures, etc., can solve problems such as high classification error rate and inability to build ECG knowledge base, so as to narrow the matching range and solve construction problems , the effect of reducing the probability of misclassification

Inactive Publication Date: 2018-03-27
HARBIN UNIV OF SCI & TECH
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Problems solved by technology

[0003] In order to solve the problem that the existing fuzzy reasoning classification method cannot construct the electrocardiogram knowledge base, thus ignoring the influence of different combinations of electrocardiogram knowledge and different band forms on the classification, the problem of high classification error rate is solved, and the existing fuzzy reasoning classification method The method does not filter the attribute concept and directly uses the comparison of its membership value to classify, which leads to the problem of high classification error rate

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  • A ECG classification method based on fuzzy inference combined with weighted similarity measures for non-therapeutic purposes
  • A ECG classification method based on fuzzy inference combined with weighted similarity measures for non-therapeutic purposes
  • A ECG classification method based on fuzzy inference combined with weighted similarity measures for non-therapeutic purposes

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specific Embodiment approach 1

[0020] Specific implementation mode one: combine figure 1 Description of this embodiment, a method for ECG classification based on fuzzy reasoning combined with weighted similarity measure for non-therapeutic purposes, including the following steps:

[0021] Step 1: Preprocessing the known type of ECG signal f(n), including two parts, using the mathematical morphology method to remove the baseline drift, using the wavelet threshold method to remove high-frequency noise, and using the preprocessed ECG signal with F (n) means;

[0022] figure 2 The processing results of removing baseline drift are given, where figure 2 (a) represents the original ECG signal, figure 2 (b) represents the ECG signal after removing the baseline drift. image 3 The processing results of removing high-frequency noise are given, where image 3 (a) represents the original ECG signal, image 3 (b) represents the ECG signal after removing the baseline drift.

[0023] Step 2: Carry out waveform d...

specific Embodiment approach 2

[0030]Specific embodiment two: in step two described in the present embodiment, carry out the waveform detection of QRS band to the electrocardiogram signal F (n) of known type and the realization process of extracting corresponding feature parameter is:

[0031] Step 2.1.1, utilize binary spline wavelet transform to decompose and filter electrocardiographic signal F (n) by Mallat algorithm, eliminate interference;

[0032] Step 2.1.2, using the relationship between wavelet transform and signal singularity, in 2 3 The QRS band is detected under the scale to obtain the start and end points of the QRS band. If the number of detected sampling points in the QRS band is greater than 36, it is judged that the QRS band is wide, and if it is less than or equal to 36, it is judged that the QRS band is normal;

[0033] Step 2.1.3, obtain the number of heart beats with wide QRS bands in all ECG signals F(n) in the known type i, and count the number of heart beats with wide QRS bands, and...

specific Embodiment approach 3

[0036] Specific embodiment three: in step 2 of this embodiment, the implementation process of performing T wave waveform detection and extracting corresponding characteristic parameters to the known type of electrocardiographic signal F(n) is:

[0037] Step 2.2.1, utilize the method of wavelet extremum point to detect T wave, obtain the starting and ending point of T wave; By analyzing wavelet transform in 2 1 -2 5 The wavelet coefficients on the scale can be known: scale 2 4 and 2 5 contains most of the energy of the T wave, but scale 2 5 Contains part of the baseline drift noise; so 2 4 scale wavelet transform, looking for extreme points on this scale, and searching for the positive modulus maximum value P with the largest amplitude between the start and end points of the T wave max and negative modulus maximum P min , set the amplitude thresholds of the positive and negative modulus maxima to be P max / 6 and P min / 6, satisfying P i max / 6 and P i >P min The modul...

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Abstract

An electrocardiogram classification method based on fuzzy reasoning combined with weighted similarity measure relates to an electrocardiogram classification method. In order to solve the problem that the existing fuzzy reasoning classification method cannot construct the electrocardiogram knowledge base, thus ignoring the influence of different combinations of electrocardiogram knowledge and different band forms on the classification, the problem of high classification error rate is solved, and the existing fuzzy reasoning classification method The method directly uses the comparison of membership degree values ​​to classify attribute concepts without screening, which leads to the problem of high classification error rate. The present invention first preprocesses the ECG signal, then extracts the characteristic parameters of each band, and constructs the classification characteristic attribute value vector Yi=[yi1 yi2 yi3 yi4 yi5] and the characteristic attribute value vector X=[x1 x2 x3 x4 x5 ], and create ECG ontology ecg.owl according to ECG knowledge; construct fuzzy concept lattice, convert fuzzy attributes into specific membership value, and effectively screen the membership value; then use weighted classification algorithm for final classification. The invention is applicable to the classification of electrocardiographic signals.

Description

technical field [0001] The invention relates to an electrocardiogram classification method. Background technique [0002] The traditional classification of ECG signals is often implemented by an expert system. The advantage of this method is that it is convenient and fast. However, for the classification of ECG signals, due to its complexity and changeability, it is difficult to accurately describe the complex relationship between the phenomenon and the cause. For many-to-many or one-to-many relationships, it becomes very difficult to extract rules, and the extracted rules will not be very accurate, and fuzzy theory can well make up for this deficiency. Chen Xiaoli used fuzzy theory combined with neural network to obtain the membership degree of abnormal heartbeat and completed the extraction of fuzzy rules, and then carried out fuzzy reasoning to realize classification; Wang Dening used database to build fuzzy knowledge base, and then combined fuzzy reasoning machine to rea...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G16H50/20G06N5/04
Inventor 宋立新王宇虹王乾
Owner HARBIN UNIV OF SCI & TECH
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