The present application belongs to the technical field of
human heart rate variability
signal recognition, and particularly relates to a
heart rate variability
signal denoising method based on
Markov chain. The present application firstly uses
Markov chain prediction method to analyze and calculate
sample sequence, obtains the relationship between
sample sequence length (N) and classification number (k) and prediction accuracy (q), then compromises the accuracy of prediction state and the accuracy of data according to the requirements of actual application, so as to determine the values of N and k, and uses the two parameters in the
human heart rate variability
signal (HRV) detection
system, so as to improve the anti-interference performance and detection efficiency of the
system. The present application belongs to the statistical method, breaks through the limitations of the existing detection
system, such as weak anti-interference ability, inflexible parameter setting and low real-time performance, and can improve the work efficiency of the user; not only expands the denoising way of the
heart rate variability signal detection system, but also enriches the application field of
Markov chain.