Abnormal blood pressure data processing method based on latest historical values

Abstract

The invention provides an abnormal blood pressure data processing method based on recent historical values. The method includes: acquiring blood pressure measurement data, selecting latest historical data as sample values, calculating an arithmetic average of the sample values, calculating a data residual error and a standard deviation, and judging whether abnormal data exits or not. Compared with other abnormal data processing methods, the method has the advantages that according to comparison of the residual error with the standard deviation, abnormal data in the blood pressure measurement data can be detected more quickly and more accurately and thus blood press data processing accuracy is guaranteed.

Description

technical field [0001] The invention relates to the technical field of medical data processing, and more specifically, to a method for processing abnormal blood pressure data based on recent historical values. Background technique [0002] As a disease with high morbidity, high complications and high disability rate, hypertension has always been an issue of concern to people. Nowadays, with the rapid development of computer technology, communication technology and medical technology, many people have blood pressure monitors at home, especially some elderly people, which can help people with high blood pressure to monitor their physical conditions at any time, prevent or detect diseases in time. Therefore, unlike the previous one-to-many measurement between blood pressure monitors and hypertensive patients, the current one-to-one measurement can obtain more valuable data, and in-depth analysis of the measurement data can obtain more and more accurate current health status inf...

AI Technical Summary

Problems solved by technology

[0003] At present, there have been many related research methods and guidelines on the identification of abnormal data, such as statistical-based methods, distance-based methods, density-based methods, cluster-based methods, etc., but most of them are strictly And accurate, but the blood pressure index is an uncertain data, it may increase or decrease significantly with some external factors, such as drugs, measurement time, mood swings, etc., and unreasonable abnormal data processing methods may treat normal values ​​as error due to outlier processing

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