Method and apparatus for detecting, and analysing heart rate variation predication degree index

Abstract

A method and instrument for detecting and analyzing the predication degree index of cardiacrhythm variability features that the predication degree is used as the index of detecting and analyzing cardiac rhythm variability. Said method includes such steps as using dynamic electrocardiograph to record cardioelectric signals, automatically recognizing dynamic cardioelectric RR gap, forming time sequence RRi, and calculating non-linear predication degree PI.

Description

technical field [0001] The invention relates to a detection and analysis method and a measuring instrument for the heart rate variability prediction index of a dynamic electrocardiogram. Background technique [0002] The clinical index of heart rate variability is a quantitative and reproducible index. It is not only a quantitative index for the degree of sinus arrhythmia, but also an important quantitative index for evaluating autonomic nerve regulation function. The correct measurement of various indicators of heart rate variability has guiding significance for both physiological and pathological research, as well as clinical diagnosis and prevention. In particular, it has important clinical application value for analyzing and observing the regulatory function of sympathetic nerve and vagus nerve, defining the risk factors for the prognosis of patients with myocardial infarction, and diagnosing diabetes, paroxysmal arrhythmia, hypertension, and sleep apnea syndrome. . ...

AI Technical Summary

Problems solved by technology

For the further clinical application of the current time-domain and frequency-domain linear methods, the main crux is that it is difficult to accurately characterize and analyze heart rate changes-this is affected by hemodynamics, electrophysiology and hormone changes, autonomic nervous system and central nervous system. very complex process of factors affecting

Therefore, the current linear method is too rough and sensitive to external noise interference, and its mathematical model does not conform to the basic fact that the heart rate control system itself is a nonlinear system.

For the current nonlinear scatter diagram analysis method, it mainly relies on the naked eye observation and the analysis of some simple numerical quantitative indicators, which is far from enough, and it is not very consistent with the basic facts of the high-dimensional nonlinear system of heart rate change.

Therefore, no matter the current method, it has its significant limitations, which hinders the clinical application and promotion of heart rate variability indicators.

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