Training data processing, ECG waveform detection method and electronic equipment

Abstract

The invention discloses a method for processing training data, detecting an electrocardiogram waveform, and electronic equipment. The method for processing training data includes: obtaining electrocardiographic data; calculating the apex of the first wave band in the electrocardiographic data; generating A candidate frame corresponding to the second band in the ECG data, the second band including at least one band in the ECG data; using the band anchor frame and the candidate frame to generate training data, the band anchor frame is used to indicate the The real band corresponding to the above candidate frame.

Description

technical field [0001] The invention relates to the technical field of medical electronics, in particular to a method for processing training data, detecting an electrocardiogram waveform, and electronic equipment. Background technique [0002] Electrocardiographic data such as electrocardiographic signals are mainly used to reflect the electrical excitation process of the heart, and are an important clinical means for doctors to perform heart examination and diagnosis. The ECG signal itself is highly complex, and people of different races, genders, and ages have great differences in various pathological conditions. In actual clinical diagnosis, it is usually assisted by machine detection results, and doctors make judgments and identifications based on their own clinical experience. In this case, due to the lack of professional knowledge and experience of doctors, they often rely too much on the test results given by the machine, which limits the accuracy and easily leads t...

AI Technical Summary

Problems solved by technology

In this case, due to the lack of professional knowledge and experience of doctors, they often rely too much on the test results given by the machine, which has a certain limit on the accuracy rate and easily leads to misjudgment of abnormal ECG.

[0003] With the widespread application of artificial intelligence, the use of artificial intelligence models for P wave, QRS wave, and T wave detection can improve the accuracy of ECG detection, but artificial intelligence such as deep learning requires a large amount of labeled data. To mark each band at the level, it is necessary to accurately mark the position of each band such as P wave, QRS wave, T wave, etc. For some bands such as P and QRS waves, due to their short duration, mark them accurately The location is more difficult, and some offsets may occur during labeling, resulting in errors in labeling data, which in turn leads to deviations in artificial intelligence detection results

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