Electrocardio normality/abnormality big-data processing method and device

Abstract

The invention discloses an electrocardio normality / abnormality big-data processing method and device, and the method employs a normal electrocardio database, and comprises the following steps: S1, partitioning to-be-classified electrocardio data according to cardiac beat, and then carrying out the normalization processing of length and amplitude, thereby forming a plurality of pieces of beat-wave-shaped data; S2, extracting the index data of to-be-classified electrocardio data; S3, determining a confidence interval according to the index data stored in the database, comparing the index data of the extracted to-be-classified electrocardio data with the confidence interval, and outputting a comparison result; S4, calculating the similarity of the plurality of pieces of beat-wave-shaped data obtained through the partitioning of the to-be-classified electrocardio data with waveform data, corresponding to cardiac beat, in the electrocardio data stored in the database, comparing the similarity with a similarity threshold value, and outputting the comparison results. The device comprises the normal electrocardio database, and a plurality of modules which are used for achieving the above steps. The device can achieve the reliable classification and screening of the electrocardiogram which is not diagnosed, and avoids false negative.

Description

technical field [0001] The invention relates to the fields of pattern recognition, big data analysis, and medical signal processing, in particular to a method and device for processing positive / abnormal big data of electrocardiogram. Background technique [0002] Electrocardiogram is a commonly used clinical examination. By recording the electrical activity of the heart in each cycle, it can help diagnose arrhythmia, cardiac ischemia, myocardial infarction, atrioventricular hypertrophy, conduction block, premature beats and other abnormalities. It can also be used to judge electrolytes and drug effects on the heart. [0003] At present, the popularity of ECG screening is largely limited by the number of cardiologists who can understand ECG. So many research institutions are devoted to the research and development of ECG automatic diagnosis system. At home and abroad, a variety of research methods have been used to realize the automatic classification of ECG by computer, su...

AI Technical Summary

Problems solved by technology

There is not yet a more mature method to support ECG screening by identifying whether the ECG is abnormal or not

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