Clinical examination method of electrocardio intelligent analyzing system

Abstract

The invention discloses a clinical examination method of an electrocardio intelligent analyzing system. The method comprises the following steps that electrocardio signals are input and preprocessed, wherein after the electrocardio signals are input into the electrocardio intelligent analyzing system, morphology filtering and a self-adaptive threshold value are utilized for removing interference noise to the electrocardio signals; waveform detection and feature extraction are carried out, wherein a morphological analysis method is utilized for detecting and recognizing a QRS wave group, an ST wave band and a P wave band, and electrocardio parameters are output; diagnosis classification and result output are carried out, wherein a clinical database carries out diagnosis classification, and the clinical detection result and the clinical database reference result are compared to obtain the diagnosis result. The clinical examination method of the electrocardio intelligent analyzing system can effectively remove noise interference, achieve automatic diagnosing, and help people to find lesion from electrocardiogram examination in time.

Description

technical field [0001] The invention relates to the field of cardiovascular disease treatment, in particular to a clinical testing method of an electrocardiographic intelligent analysis system. Background technique [0002] With the continuous improvement of people's living standards in our country and the gradual aging of the population, cardiovascular disease is gradually becoming a major killer that threatens human health. The heart is the power source of blood circulation, like a never-ending engine, accompanied by human life. Organic or functional lesions of the heart will bring great pain to patients and their families. As the four routine examinations of the human body, the electrocardiogram is accepted by many people for its convenience and ability to detect functional lesions. However, the manual analysis of ECG not only requires a large workload of professionally trained physicians, but also is relatively subjective. Therefore, it is urgently needed to automatic...

AI Technical Summary

Problems solved by technology

[0003] 1. The ECG signal itself is a very weak signal, which is inevitably affected by various interference factors during the acquisition process. It is impossible to completely filter out these interferences using any filtering method, so that the accuracy of analysis and diagnosis decreases.

[0004] 2. Due to the complexity of the ECG signal itself and the diversity of pathological signals, feature extraction is very difficult

Therefore, more than 90% of the errors in automatic ECG analysis are caused by waveform detection and recognition, especially the detection of P waves has not been really resolved

[0005] 3. The ECG signal itself is a human physiological signal with relatively large individual differences. No matter what detection and analysis method is used, it is difficult to guarantee that it can be suitable for all situations.

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