System for the analysis of the daily heart rhythm autonomic nervous system balance

• US 10,342,474 B2
• Filed: 04/13/2015
• Issued: 07/09/2019
• Est. Priority Date: 10/27/2014
• Status: Active Grant

• Current Assignee: UAB Metapro Holding
• Original Assignee: UAB Metapro Holding
• Inventors: Stankus, Albinas
• Primary Examiner(s): Schaetzle, Kennedy
• Application Number:
  US15/521,279
• Publication Number:
  US 20170311867A1
• Time in Patent Office: 1,548 Days
• Field of Search: None
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/352 Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
• CPC Class Codes: A61B 5/4035 Evaluating the autonomic nervous system
• CPC Class Codes: A61B 5/4857 Indicating the phase of biorhythm clocks or watches with indicators for biological cycles G04B19/264
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/743 Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

The present invention collects data on circadian intervals between two cardiac contractions (RR intervals) and, on the basis of the collected data on circadian RR intervals, provides a system for analysis of the human circadian heart rhythm autonomic nervous system balance, and analysis of heart rate (HR). The invention focuses on HR fluctuations that are divided into three groups: slow fluctuations of the circadian period, higher frequencies compared with circadian, and chaotic. The division into parts is executed by applying adequate empiric and mathematical methods to determine a series of envelope curves, closely and individually related to the principal circadian series, followed by their separation from the input HR series, and then applying HR variability (HRV) methods to increase accuracy of the analysis of HRV results and add new HR regulation criteria to the analysis outcomes.

Claims

1. An analysis system for analyzing a balance of a circadian heart rhythm autonomic nervous system wherein the analysis system increases accuracy of the analysis of HRV results and adds new HR regulation criteria to the analysis outcomes, wherein the system comprises:

a RR interval collection block;

tools for artifact and arrhythmias removal from RR interval series;

an identifier for circadian period and RR interval series; and

a data analyzer finding sympathetic and parasympathetic nervous system balance, wherein the tools are configured to perform the following after artifact and arrhythmias removal from RR interval series;

1) identify a chaotic part of the RR interval series and analysis of the chaotic part;

2) separate the chaotic part from the RR interval series and aquire a clean circadian RR series;

3) aquire a normalized RR interval series using interpolation, resampling and normalization;

4) analyze the heart rate bimodal distribution and modes of the normalized RR interval series;

5) identify a heart rate circadian period in the normalized RR data series;

6) analyze the heart rhythm variable part SNS and PNS regulation indicators in the normalized RR data series; and

7) generate a report on obtained results in graph and tabular form.

2. The analysis system according to claim 1, wherein the tools for artifact and arrhythmias removal from RR series performs chaotic series separation from RR series and obtains clean circadian RR interval series using a wavelet transform method.

3. The analysis system according to claim 1, wherein to obtain a normalized data series, interpolation of circadian RR interval series is performed using cubic Hermite interpolation.

4. The analysis system according to claim 1, wherein the following are performed by the heart rhythm bimodal distribution and analysis of the modes detected in the normalized RR data series block:

calculate an input histogram sequence out of normalized RR data series and histogram graphic presentation;

cluster a normalized RR data series into two RR clusters (modes) of which the RR cluster 1 (first mode) involves low heart rhythm frequencies and RR cluster 2 (second mode) involves high heart rhythm frequencies;

calculate statistical characteristics of the first mode series and second mode series;

calculate probability density functions for both modes and their histograms;

summate probability density histograms and their graphic presentation;

determine reliability of the compliance of the distributions between summary histogram and input histogram using chi-square criterion and calculation of the summary histogram for both modes;

determine a divergence coefficient in the summary histogram of both modes;

determine a skewness coefficient of the heart rate and skewness coefficient of the total time in the input histogram;

present statistical calculations in tabular form;

present circadian time distribution of the RR interval within both modes in graphic form;

present the normalized time course of the circadian tonic sympatho-vagal balance in graphic form;

present the values of results of summary calculations of the values of RR interval analysis in tabular form.

5. The analysis system according to claim 4, wherein the heart rhythm bimodal distribution and analysis of the modes detected in normalized RR data series by a block is clustering normalized RR data series into two groups using a k-means clustering method.

6. The analysis system according to claim 1, wherein the block for heart rhythm circadian period identification within RR data series:

isolates the heart rhythm circadian period performing continual empirical mode decomposition involving detection of interim modes and their gradual elimination from every residual RR interval series;

obtains the heart rhythm circadian period series and analyzes the circadian period structure.

7. The analysis system according to claim 6, wherein the block for heart rhythm circadian period identification within RR data series analyzes the circadian period structure using a cosinor calculation method and includes statistical evaluation of parameters obtained, presenting them in tabular and graph forms.

8. The analysis system according to claim 1, wherein the block for the analysis of the heart rhythm variable part SNS and PNS regulation criteria performs the following:

separates the RR variable part which is achieved by subtraction of the heart rhythm circadian period from normalized RR data sequence;

filters the variable heart rhythm series when three RR interval time series of different frequencies are separated;

calculates the dispersion and mean standard deviations and of three RR intervals time series;

evaluates heart rhythm variability;

presents the evaluation of the heart rhythm variability in graphic and tabular forms.

9. The analysis system according to claim 8, wherein the variable heart rhythm series filter employs an infinite impulse response three-filter restriction method determining very low frequency (VLF), low frequency (LF) and high frequency (HF) RR interval time series.

10. The analysis system according to claim 9, wherein the block for the analysis of the heart rhythm variable part SNS and PNS regulation criteria performs the following evaluation of heart rhythm variability:

for the very low frequency filter determining passband edge frequency equal to 0.035 Hz and stopband edge frequency equal to 0.04 Hz;

for low frequency band filter determining stopband edge frequency at low frequency side equal to 0.035 Hz, and at high frequency side equal to 0.0155 Hz, passband edge frequency at low frequency side equal to 0.04 Hz, and at high frequency side equal to 0.15 Hz;

for the high frequency band filter determining stopband edge frequency at low frequency side equal to 0.145 Hz, and at high frequency side equal to 0.45 Hz, passband edge frequency at low frequency side equal to 0.15 Hz, and at high frequency side equal to 0.40 Hz.

11. The analysis system according to claim 10, wherein, in the block for the analysis of the heart rhythm variable part SNS and PNS regulation criteria, evaluation of the heart rhythm variability is performed by calculation of the dispersion ratio for every time frame within low frequency and high frequency bands presenting phase balance of SNS and PNS changes.

12. The analysis system according to claim 10, wherein the block for the analysis of the heart rhythm variable part SNS and PNS regulation criteria performs the following evaluation of heart rhythm variability:

for the very low frequency filter from 0 Hz to 0.04 Hz, determining passband edge frequency equal to 0.035 Hz and stopband edge frequency equal to 0.04 Hz;

for low frequency band filter 0.04-0.15 Hz determining stopband edge frequency at low frequency side equal to 0.035 Hz, and at high frequency side equal to 0.0155 Hz, passband edge frequency at low frequency side equal to 0.04 Hz, and at high frequency side equal to 0.15 Hz;

for the high frequency band filter 0.15-0.40 Hz determining stopband edge frequency at low frequency side equal to 0.145 Hz, and at high frequency side equal to 0.45 Hz, passband edge frequency at low frequency side equal to 0.15 Hz, and at high frequency side equal to 0.40 Hz.