Identification method and system of characteristic waveform of electrocardiogram

Abstract

The invention discloses identification method and system of a characteristic waveform of an electrocardiogram. The identification system comprises at least two neural network modules and a fuzzy logic mode identification module, wherein a characteristic waveform identification algorithm of each neural network module is different from all characteristic waveform identification algorithms of other neural network modules, the neural network modules operate an input electrocardiogram signal according to the characteristic waveform identification algorithms and output by identifying the characteristic waveforms from operated signals; the fuzzy logic mode identification module is used for receiving the characteristic waveforms output by each neural network module and setting medical history weight values according to the characteristic waveforms, and the fuzzy logic mode identification module carries out weighted operation on the priority levels of the characteristic waveforms and then correspondingly outputs the characteristic waveforms and the weighted priority levels of the characteristic waveforms. In the invention, neural networks with different characteristic waveform identification algorithms are adopted to identify the characteristic waveforms, thereby extending the characteristic waveforms capable of being identified by a single neural network and achieving the purpose of more accurate analysis and classification of the electrocardiogram signals.

Description

technical field [0001] The invention relates to the field of medical devices, in particular to a method and system for assisting in identifying characteristic waveforms of electrocardiograms. Background technique [0002] Pattern recognition technology is widely used in the medical field. For example, pattern recognition is performed on electrocardiogram signals, and typical characteristic waveforms are analyzed and classified, so that doctors can judge the patient's physical condition based on the analyzed and classified characteristic waveforms. [0003] The main methods of pattern recognition are: 1. Statistical pattern recognition; 2. Syntactic pattern recognition; 3. Fuzzy pattern recognition; 4. Logical reasoning method; 5. Neural network method. Among them, statistical pattern recognition is difficult to extract features of patterns with complex structures, it cannot reflect the structural characteristics of patterns, it is difficult to describe the nature of patterns...

AI Technical Summary

Problems solved by technology

However, in the prior art, the neural network method still has some problems, and its main disadvantage is that there are few models and not enough patterns (i.e. characteristic waveforms) that can be recognized. Therefore, the neural network method of the prior art is analyzing the electrocardiogram signal. Some characteristic waveforms may not be recognized during classification, resulting in low accuracy of ECG signal analysis

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