Heart rate variability signal analysis method based on extreme value energy decomposition method

Abstract

The invention discloses a heart rate variability signal analysis method based on an extreme value energy decomposition method. The heart rate variability signal analysis method comprises the followingsteps: acquiring an ECG (electrocardiograph) signal at an unknown state at a provided moment and a provided sampling frequency, and performing denoising so as to obtain an RRI (interbeat interval) signal x(t); by taking the RRI signal x(t) as an original signal, decomposing the original signal x(t) into n extreme value intrinsic mode function components and one margin, and judging whether the RRIsignal is an abnormal heart rate variability signal or not according to the n extreme value intrinsic mode function components, wherein the n extreme value intrinsic mode function components obtainedby decomposing the original signal x(t) represent components of the original signals at different frequency bands. The RRI signal is analyzed by using the extreme value energy decomposition method, the original signal is decomposed into multiple components, that is, extreme value component functions, the energy of each component is calculated, and thus energy distribution of the RRI signal can beobtained.

Description

technical field [0001] The invention relates to an electrocardiogram signal analysis, in particular to a heart rate variability signal analysis method based on an extreme value energy decomposition method. Background technique [0002] Physiological signals are generated by the interaction of multiple systems in a living body, and the time and intensity of different systems are different, resulting in the complexity of physiological signals in time and space. Heart rate variability (HRV) refers to the measurement of the variation in time between consecutive cardiac cycles, to be precise, it should be the variation in the measurement of the difference between consecutive normal P-P intervals. However, because the P wave is not as obvious as the R wave or the top of the P wave is sometimes broad and blunt, the research on the heart rate variability signal is usually replaced by the R-R interval signal (RRI) which is equal to the P-P interval. Studies have shown that HRV can b...

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However, PSD is not a data-driven approach, and the segmentation in the frequency domain is rough, resulting in missing details and inflexible segmentation.

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