Feature selection-based arrhythmia classification method

Abstract

The invention relates to a feature selection-based arrhythmia classification method. The method comprises the following steps: preprocessing an ECG (electrocardiograph) signal; extracting a morphological feature and a time-frequency feature according to a detected position R, and constructing an original feature vector; calculating feature weights, namely calculating the weight of each feature in the original feature vector by using a Relief algorithm; instructing a population to be initialized according to the feature weights, respectively performing selection, crossover and mutation operation according to a selection probability, a crossover probability and a mutation probability according to the adaptation degree of an individual to obtain a next generation, repeating the operation until the maximum number of iteration times termination condition is met, and then outputting the individual with the highest adaptation degree as an optimal feature; and enabling a plurality of di-classifiers to form an identification classifier through a multi-classification strategy to realize identification of various arrhythmias. According to the feature selection-based arrhythmia classification method, the dimensions of the features can be reduced and the accuracy of identification of the various arrhythmias can be improved.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and particularly relates to the task of extracting electrocardiogram signal features and combining feature selection to solve various arrhythmia recognition tasks. Background technique [0002] The long-term existence of arrhythmia will cause serious heart disease. Due to the high mortality rate from heart disease, the detection of cardiac arrhythmias has become very important. Experts detect arrhythmias by analyzing ECG, but analyzing ECG records for a long time is time-consuming and boring, and it is difficult to accurately distinguish the small morphological changes of ECG signals through human eyes, which may cause experts to lose or mistake important information during diagnosis. Therefore, an efficient and accurate computer-aided diagnosis system is needed to assist doctors in detecting arrhythmia. The arrhythmia classification method includes four steps: preprocessing, feature extracti...

AI Technical Summary

Problems solved by technology

[0005] The features extracted by the time-frequency method are often of high dimensionality, which not only increases the amount of calculation, but also has a negative impact on the classification performance.

The meth

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