Classifier fusion and diagnosis rule based premature ventricular contraction (PVC) identification system and method

Abstract

The invention discloses a classifier fusion and diagnosis rule based PVC identification system and method. The system comprises a classification unit, a fusion unit and a discrimination unit; the classification unit comprises an LCNN classification module and an RNN classification module which process electrocardiogram data independently, the LCNN classification module includes m first classifiersof different structures, and outputs m first classification results at least, and the RNN classification module includes n second classifiers of different structures, and outputs n second classification results at least; the fusion unit carries out fusion decision on the first and second classification results according to a fusion decision rule to obtain a fusion result; and the discrimination unit discriminates non PVC data from PVC data of the fusion unit according to PVC pathologic characteristics, and obtains a PVC identification result. The classification results of the LCNN and RNN classifiers are fused, the PVC pathologic characteristics are combined, a machine learning and disease diagnosis rule combined method is used, and the integral classification performance and accuracy ofPVC identification are improved.

Description

technical field [0001] The invention relates to a ventricular premature beat recognition system, in particular to a ventricular premature beat recognition system and a recognition method based on classifier fusion and diagnosis rules, belonging to the technical field of medical electronics. Background technique [0002] The computer-aided diagnosis of premature ventricular contraction (PVC) has very important clinical significance. It can relieve doctors from massive ECG analysis, reduce the workload of doctors, and improve the efficiency of doctors' diagnosis. At present, there are mainly two types of computer-aided recognition systems for premature ventricular contractions: one is to use a certain classifier for recognition; the other is to use rule reasoning to distinguish based on the pathological characteristics of PVC. The former category usually ignores the doctor's diagnostic thinking process; while the latter category considers the doctor's diagnostic thinking proce...

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Problems solved by technology

The former category usually ignores the doctor's diagnostic thinking process; while the latter category considers the doctor's diagnostic thinking process, but it needs to extract some feature points of PVC in advance, such as R wave, QRS wave boundary points, etc., and how to accurately extract these Feature points are also a problem that researchers need to focus on

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