An electrocardiosignal classification method based on optimal feature space similarity mining label correlation

An optimal feature, ECG signal technology, applied in the fields of medical science, instrument, character and pattern recognition, etc., can solve the problems of poor accuracy, building models, ignoring label correlation, etc., to achieve accurate label correlation and improve accuracy. degree of effect

Inactive Publication Date: 2021-03-09
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0006] Algorithm adaptation strategies and problem conversion strategies largely ignore the correlation between labels, and do not use the relationship between labels to build models, and there is such a relationship between ECG disorders, so these methods cannot be used very well. Using ECG to determine the condition, the accuracy of prediction is poor

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  • An electrocardiosignal classification method based on optimal feature space similarity mining label correlation
  • An electrocardiosignal classification method based on optimal feature space similarity mining label correlation
  • An electrocardiosignal classification method based on optimal feature space similarity mining label correlation

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specific Embodiment

[0054] Concrete embodiment: as shown in table 1, table 1: training set data

[0055]

[0056]

[0057] As shown in Table 1, given a training set of 10 samples, in the feature space, P wave amplitude, T wave amplitude, and QRS complex amplitude are continuous features (unit: mv), P wave double peaks, T wave low Ping is a discrete feature, 0 means there is no such feature, 1 means it has this feature, for the convenience of description, the above features are named as f 1 , f 2 , f 3 , f 4 , f 5 ;The label space is composed of four labels: left atrial hypertrophy, sinus arrhythmia, inferior wall myocardial infarction, and premature atrial contraction. 0 indicates that the sample does not contain this label, and 1 indicates that the sample contains this label. For the convenience of description, Name the above labels in turn as L 1 , L 2 , L 3 , L 4 .

[0058] Step (1), using the optimal feature space similarity algorithm to correct the tag correlations mined by th...

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Abstract

The invention discloses an electrocardiosignal classification method based on optimal feature space similarity mining label correlation, and belongs to the field of intelligent diagnosis of electrocardio diseases. According to the invention, on the basis of a trained single label classifier, label correlation mined by utilizing an optimal feature space similarity algorithm to correct association rules is proposed, and a final prediction label is determined according to the corrected label correlation; the method comprises the following specific steps: 1, obtaining a main label and a candidateauxiliary label set by utilizing a classification result of a classifier; and 2, mining label correlation by adopting an association rule, correcting the label correlation by utilizing an optimal feature space similarity algorithm provided by the invention, and filtering the candidate sub-label set through the corrected label correlation to determine a final prediction label. According to the invention, the relation between the labels is corrected by calculating the optimal feature space similarity between the different labels, more accurate label correlation is obtained, and the precision ofelectrocardiosignal classification is improved.

Description

technical field [0001] The invention belongs to the field of intelligent diagnosis of electrocardiographic disorders, and in particular relates to a machine learning-based multi-label disease determination method, in particular to a method for classifying electrocardiographic signals based on optimal feature space similarity mining for correlation between tags. Background technique [0002] In recent years, multi-label learning has gradually become one of the hot research issues in the field of machine learning. Unlike traditional single-label classification where each sample belongs to only one class label, each sample in multi-label classification belongs to multiple class labels. The multi-label problem is defined as: Let X=R d Represents the d-dimensional sample space, L={l 1 , l 2 ,... l n} represents a label space containing n labels, D={(x i ,Y i )|1≤i≤m,x i ∈X,Y i ∈L} represents a training set containing m samples, where x i =[x i1 ,x i2 ,...,x id ] repre...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62A61B5/346
CPCA61B5/7267G06F18/24G06F18/214
Inventor 韩京宇王成张伟钱龙赵静
Owner NANJING UNIV OF POSTS & TELECOMM
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