Method for quantitative assessment of the autonomic nervous system based on heart rate variability analysis

• US 20090005696A1
• Filed: 06/29/2007
• Published: 01/01/2009
• Est. Priority Date: 06/29/2007
• Status: Active Grant

• Current Assignee: Intelwave LLC
• Original Assignee: Intelwave LLC
• Inventors: Riftine, Alexander
• Granted Patent: US 7,826,892 B2
• Time in Patent Office: Days
• US Class Current: 600/515
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/0245 by using sensing means generating electric signals, i.e. ECG signals
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/347 Detecting the frequency distribution of signals
• 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 5/7257 using Fourier transforms

Abstract

A method is provided for characterizing autonomic nervous system activity of a patient based on heart rate variability analysis using electrocardiographic data from the patient. In addition, a computer-readable medium is provided tangibly embodying a program of instructions executable by a computer to perform a method for characterizing autonomic nervous system activity of a patient based on heart rate variability analysis using electrocardiographic data from the patient. Also, a system is provided for characterizing autonomic nervous system activity of a patient comprising a processor and a machine-readable medium tangibly embodying a program of instructions executable by the processor.

Claims

1. A method for characterizing autonomic nervous system activity of a patient comprising:

(a) acquiring electrocardiographic data from the patient;

(b) determining from the electrocardiographic data obtained in step (a) a plurality of time intervals between successive R-waves;

(c) analyzing the plurality of time intervals by frequency domain analysis so as to quantitate the following parameters;

i) power of high frequency (P_HF);

ii) power of low frequency 1 (P_LF1);

iii)

iv) power of low frequency 2 (P_LF2);

v) difference between power of low frequency 1 and high frequency (P_LF1−

P_HF);

vi) maximum amplitude of spectral function in a low frequency band (S_LFmax);

vii) frequency at a point of maximum amplitude of spectral function in the low frequency band (F_LFmax);

viii) maximum amplitude of spectral function in a high frequency band (S_HFmax);

ix) frequency at a point of maximum amplitude of spectral function in the high frequency band (F_HFmax);

x) ratio of power of high frequency to power of low frequency 1, (P_HF/P_LF1);

xi) ratio of power of high frequency to power of low frequency (sum of powers of low frequency 1 and low frequency 2), (P_HF/(P_LF1+P_LF2));

xii) heart rate (HR);

and, if a spectral peak falls in the range of a middle frequency band,

xiii) (S_MF) maximum amplitude of spectral function in a middle frequency band; and

(d) applying an clusterization algorithm to the parameter values quantitated in step

(c) so as to determine two further parameters, one of which correlates with sympathetic nervous system activity and the other of which correlates with parasympathetic nervous system activity, so as to thereby characterize the autonomic nervous system activity of the patient.

2. The method of claim 1, wherein the high frequency is 0.15 to 0.5 Hz.

3. The method of claim 1, wherein the low frequency 1 is 0.07 to 0.15 Hz.

4. The method of claim 1, wherein the low frequency 2 is 0.033 to 0.07 Hz.

5. The method of claim 1, wherein parasympathetic nervous system activity is determined by the parameters:

i) P_HF;

ii) P_LF1;

iii) P_LF2; and

iv) P_LF1−

P_HF.

6. The method of claim 5, wherein the value of P_HF, P_LF1 or P_LF2 is one of 30 values.

7. The method of claim 6, wherein each of the 30 values is an integer representing the sub-range of the power spectrum falling between each of the following numbers, in msec²:

99999, 12000, 11000, 9000, 7800, 6400, 5200, 4200, 3200, 2500, 2100, 1700, 1300, 1000, 900, 820, 730, 6400 570, 490, 420, 360, 310, 255, 200, 145, 95, 55, 25, 15 and 0 for P_HF;

99999, 9000, 7800, 6400, 5200, 4200, 3400, 3000, 2600, 2200, 1850, 1550, 1300, 1100, 1000, 900, 810, 730, 640, 570, 490, 420, 350, 300, 250, 200, 150, 120, 70, 25 and 0 for P_LF1; and

99999, 9000, 7800, 6400, 5200, 4200, 3400, 3000, 2600, 2200, 1850, 1550, 1300, 1100, 1000, 900, 810, 730, 640, 570, 490, 420, 350, 300, 250, 200, 150, 120, 70, 25 and 0 for P_LF2.

8. The method of claim 1, wherein sympathetic nervous system activity is determined by the parameters:

i) P_LF1;

ii) P_LF2;

iii) S_LFmax;

iv) F_LFmax;

v) S_HFmax;

vi) F_HFmax;

vii) S_MF (if present);

viii) P_HF/P_LF1;

ix) P_HF/(P_LF1+P_LF2); and

x) HR.

9. The method of claim 1, further comprising replacing an artifactual parameter value with an interpolated parameter value prior to step (c).

10. The method of claim 1, wherein in step (c) the frequency domain analysis comprises reducing the value of a power in a lower frequency subrange if the total power in the subrange of 0.5 to 1.0 Hz exceeds 3000 ms².

11. The method of claim 1, wherein prior to step (b) the data are subjected to a data preparation algorithm.

12. The method of claim 1, wherein in step (c) the frequency domain analysis is performed via a power spectral analysis algorithm.

14. A system for characterizing autonomic nervous system activity of a patient comprising:

i) a processor; and

ii) a machine-readable medium tangibly embodying a program of instructions executable by the processor to perform the method of claim 1.

15. The system of claim 14, further comprising a display means to present results to a user thereof via a computer interface.

13. A computer-readable medium tangibly embodying a program of instructions executable by a computer to perform a method for characterizing autonomic nervous system activity of a patient which method comprises:

(a) acquiring electrocardiographic data from the patient;

(b) determining from the electrocardiographic data obtained in step (a) a plurality of time intervals between successive R-waves;

(c) analyzing the plurality of time intervals by frequency domain analysis so as to quantitate the following parameters;

i) power of high frequency (P_HF);

ii) power of low frequency 1 (P_LF1);

iii) power of low frequency 2 (P_LF2);

iv) difference between power of low frequency 1 and high frequency (P_LF1−

P_HF);

v) maximum amplitude of spectral function in a low frequency band (S_LFmax);

vi) frequency at a point of maximum amplitude of spectral function in the low frequency band (F_LFmax);

vii) maximum amplitude of spectral function in a high frequency band (S_HFmax);

viii) frequency at a point of maximum amplitude of spectral function in the high frequency band (F_HFmax);

ix) ratio of power of high frequency to power of low frequency 1, (P_HF/P_LF1);

x) ratio of power of high frequency to power of low frequency (sum of powers of low frequency 1 and low frequency 2), (P_HF/(P_LF1+P_LF2));

xi) heart rate (HR);

xii) and, if a spectral peak falls in the range of a middle frequency band,

xiii) (S_MF) maximum amplitude of spectral function in a middle frequency band; and

(d) applying a clusterization algorithm to the parameter values quantitated in step (c) so as to determine two further parameters, one of which correlates with sympathetic nervous system activity and the other of which correlates with parasympathetic nervous system activity.

16. A method for analyzing electrocardiographic data comprising:

(a) analyzing a plurality of time intervals between successive R-waves of the electrocardiographic data by frequency domain analysis so as to quantitate the following parameters;

i) power of high frequency (P_HF);

ii) power of low frequency 1 (P_LF1);

iii) power of low frequency 2 (P_LF2);

iv) difference between power of low frequency 1 and high frequency (P_LF1−

P_HF);

v) maximum amplitude of spectral function in a low frequency band (S_LFmax);

vi) frequency at a point of maximum amplitude of spectral function in the low frequency band (F_LFmax);

vii) maximum amplitude of spectral function in a high frequency band (S_HFmax);

viii) frequency at a point of maximum amplitude of spectral function in the high frequency band (F_HFmax);

ix) ratio of power of high frequency to power of low frequency 1, (P_HF/P_LF1);

x) ratio of power of high frequency to power of low frequency (sum of powers of low frequency 1 and low frequency 2), (P_HF/(P_LF1+P_LF2));

xi) heart rate (HR);

xii) and, if a spectral peak falls in the range of a middle frequency band,

xiii) (S_MF) maximum amplitude of spectral function in a middle frequency band; and

(b) applying a clusterization algorithm to the parameter values quantitated in step (a) so as to determine two further parameters, one of which correlates with sympathetic nervous system activity and the other of which correlates with parasympathetic nervous system activity.