Electrocardiogram classifying method based on fuzzy inference and weighted similarity measurement

Abstract

The invention provides an electrocardiogram classifying method based on fuzzy inference and weighted similarity measurement and relates to an electrocardiogram classification method. The electrocardiogram classifying method aims to solve the problem that by the adoption of an existing fuzzy inference classification method, due to the fact that an electrocardiogram knowledge base cannot be established, the influence of electrocardiogram knowledge and different combinations of different wave band forms on classification is omitted, and as a result, the error rate of classification is high and to solve the problem that by the adoption of the existing fuzzy inference classification method, due to the facts that attribute concepts are not screened and comparison of membership degrees of the attribute concepts is directly used for classification, the error rate of classification is high. The electrocardiogram classifying method comprises the following steps that, firstly, electrocardiosignals are preprocessed; secondly, feature parameter extraction is conducted on all wave bands; thirdly, a classification feature attribute value vector Yi=[yi1, yi2, yi3, yi4 and yi5] and a to-be-detected feature attribute value vector X=[x1, x2, x4, x4 and x5] are established, and an ECG body ecg.owl is established according to electrocardiogram knowledge; fuzzy concept lattices are established; fuzzy attributes are converted into specific membership degree values, and effective screening is conducted on the specific membership degree values; final classification is conducted through a weighted classification method. The electrocardiogram classifying method is suitable for electrocardiosignal classification.

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...

Images