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A Disease-Related ECG Feature Selection Method

A feature selection method and electrocardiogram technology, applied in the directions of instruments, character and pattern recognition, computer components, etc., can solve the problems of ventricular abnormality, between ventricular abnormality and normal, and inability to distinguish clearly, and improve the sensitivity. Effect

Active Publication Date: 2017-05-03
SUZHOU INST OF NANO TECH & NANO BIONICS CHINESE ACEDEMY OF SCI
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Problems solved by technology

Due to the wide variety of ECGs, in order to unify and standardize the evaluation criteria of the ECG automatic recognition system, the American Association for the Advancement of Medical Instrumentation (AAMI) divides the ECG categories into five categories: (1) N, normal ECG and Conduction block ECG; (S) S, supraventricular abnormality; (3) V, ventricular abnormality; (4) F, between ventricular abnormality and normal; (5) Q, indistinguishable

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  • A Disease-Related ECG Feature Selection Method
  • A Disease-Related ECG Feature Selection Method
  • A Disease-Related ECG Feature Selection Method

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Embodiment 1

[0051] Taking the arrhythmia database of MIT-BIH as an example, the effectiveness of this method is verified. The entire data set has 48 pieces of Holter two-lead ECG data of about 30 minutes, of which 4 records are ECGs with pacemakers placed, which need to be processed separately, and the remaining 44 records are used for automatic classification tests of ECGs.

[0052] According to the AAMI evaluation standard, the above two-lead ECG data were divided into four classification systems for NSVF, as shown in Table 1:

[0053] Table 1 shows the number of beats in the MIT-BIH database

[0054]

[0055] DS1: 101, 106, 108, 109, 112, 114, 115, 116, 118, 119, 122, 124, 201, 203, 205, 207, 208, 209, 215, 220, 223, 230;

[0056] DS2: 100, 103, 105, 111, 113, 117, 121, 123, 200, 202, 210, 212, 213, 214, 219, 221, 222, 228, 231, 232, 233, 234

[0057] The 44 records are equally divided into DS1 and DS2. Among them, DS1 is used to train the classification model, and DS2 is used as ...

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Abstract

The electrocardiogram feature selection method provided by the present invention divides the electrocardiogram into four types of NSVF classification systems, and decomposes the four types of NSVF classification systems into six binary classifiers of NvS, NvV, NvF, SvV, SvF, and VvF. In the classifier, each feature is sorted according to the score to form a candidate feature set, and then the optimal feature subset is selected from each binary classifier, and the ECG sample to be tested is predicted based on the optimal feature subset to obtain the ECG to be tested kind of category. The electrocardiogram feature selection method provided by the present invention forms a feature subset after sorting the feature scores from high to low, and selects the optimal feature subset from each binary classifier, and uses the optimal feature subset to predict the ECG sample to be tested, The category of the electrocardiogram to be tested is obtained, and the prediction accuracy is improved.

Description

【Technical field】 [0001] The invention relates to the technical field of electrocardiogram signal detection, in particular to a disease-related electrocardiogram feature selection method. 【Background technique】 [0002] Electrocardiogram examination is an effective method for diagnosing arrhythmia and myocardial ischemia. This method has the advantages of non-invasiveness and low cost. It has a large business volume in hospitals, especially in institutions such as physical examination centers and remote consultation centers. Doctors need to interpret a large number of electrocardiograms every day. In order to reduce the workload of doctors, computer-aided electrocardiogram automatic classification and recognition systems have been paid more and more attention in recent years. [0003] A complete ECG automatic classification and recognition system usually includes the following processes: data acquisition, data preprocessing, feature extraction, and classifier training / predic...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
Inventor 张战成董军
Owner SUZHOU INST OF NANO TECH & NANO BIONICS CHINESE ACEDEMY OF SCI
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