Electrocardiograph detection method based on quantum simple recursion neural network

Abstract

The invention relates to an intelligent method for testing cardiograms by basing on quanta a simple recursion neural network, which comprises the steps that: the cardiac electric signals are sent to a self-adaptation power frequency disturbance-restriction module for restricting the disturbance of the power frequency; the two output cardiac electric signals in the previous step are sent to a blind baseline drift disturbance-restriction module so as to remove the breath baseline drift disturbance; the output cardiac electric signals in the second step are sent to the quanta simple recursion neural network for carrying out cardiac electric intellectual test and output. The self-adaptation power frequency disturbance-restriction module comprises two same groups of self-adaptation power frequency disturbance-restriction units, and carries out power frequency disturbance-restriction on the two collected groups of cardiac electric signals simultaneously and respectively. The self-adaptation power frequency disturbance-restriction module can automatically restrict power frequency disturbance by adopting the cardiac electric power frequency disturbance-restriction method basing on QR decomposing recursive least squares (RLS) algorithm self-adaptation trap technology. The invention can not only realize cardiac electric intellectualization test, but also remove the power frequency and the breath baseline drift disturbance in the effective frequency band of cardiac electric signals, and solve the difficulty that digital filtration method eliminates the disturbance of aliasing in spectrum.

Description

technical field [0001] The invention relates to an electrocardiogram intelligent detection method. In particular, it relates to an intelligent electrocardiogram detection method based on quantum simple recursive neural network, which effectively solves the problem of clearing spectrum aliasing interference by digital filtering method and improves the correct detection rate of electrocardiogram. Background technique [0002] In recent years, cardiovascular disease is the first killer affecting human health, and cardiovascular disease has become a frequently-occurring and common disease in our country. According to the survey data released by the Ministry of Health, the prevalence of heart disease in my country has been very high in recent years and has been increasing year by year, exceeding 3%. The age of onset of coronary heart disease and myocardial infarction tends to be younger. It is not uncommon to have myocardial infarction and stroke around the age of 10. Therefore,...

AI Technical Summary

Problems solved by technology

However, at present, the automatic diagnosis of ECG has not been widely used in clinical practice. The main reason is that the recognition of ECG waveform is not accurate and the misdiagnosis rate is high. Therefore, it is necessary to explore new detection methods to improve the effect of automatic diagnosis of ECG and expand its application range.

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