Classification of heart rate variability patterns in diabetics using cepstral analysis

• US 6,390,986 B1
• Filed: 01/31/2000
• Issued: 05/21/2002
• Est. Priority Date: 05/27/1999
• Status: Expired due to Fees

• Current Assignee: Rutgers University
• Original Assignee: Rutgers University
• Inventors: Curcie, David J., Craelius, William
• Primary Examiner(s): Nasser, Robert L.
• Application Number:
  US09/494,572
• Time in Patent Office: 841 Days
• Field of Search: 600/500, 600/485, 600/300
• US Class Current: 600/485
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/4035 Evaluating the autonomic nervous system
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

An encoding and classification method is provided which is capable of comprehensively featuring relevant spectral information for the early detection of diabetes-induced cardiac autonomic neuropathy using the LP-cepstral discriminant classifier for quick, noninvasive assessment (screening) of neuropathy in diabetics. Patients are tested in the supine position thus avoiding the stresses to the patient that are associated with tilt table testing.

Claims

1. A method of screening for neuropathy in diabetics using cepstral analysis of heart rate comprising the steps of:

generating electrical signals representing an individual'"'"'s instantaneous heart rate while the individual is at rest;

cepstral encoding said electrical signals to generate heart rate cepstral vectors for an individual;

generating a classifier of heart rate cepstral vectors from a patient population having early diabetic autonomic neuropathy;

comparing said heart rate cepstral vectors for an individual within said classifier; and

using the results of said comparison to indicate the presence or absence of autonomic neuropathy in an individual.

2. The method of claim 1 wherein heart rate information is obtained from an individual in a supine position.

3. The method of claim 2 wherein cepstral encoding is linear predictor-derived.

4. The method of claim 3 wherein said heart rate cepstral vectors are presented in 12-dimensions using linear prediction-derived cepstral coefficients.

5. The method of claim 4 wherein instantaneous heart rate and said classifier from a patient population are recorded while patients are in a supine position.

6. The method of claim 5 wherein the AN status of the patient population is known prior to obtaining classifier, and the status is determined by testing the patient population on a tilt table.

7. A method of screening for neuropathy in diabetics using cepstral analysis of heart rate comprising the steps of:

generating electrical signals indicative of heart rate in patients having diabetic autonomic neuropathy;

generating electrical signals indicative of heart rate of an individual while the individual is at rest for diagnoses of autonomic neuropathy in the individual;

encoding said electrical signals to produce heart rate cepstral vectors; and

comparing said heart rate cepstral vectors for an individual with said heart rate cepstral vectors for patients to determine the presence or absence of autonomic neuropathy in an individual.

8. The method of claim 7 wherein said electrical signals indicative of heart rate are generated with patients and an individual in supine positions.

9. The method of claim 8 wherein patients are first screened for AN on a tilt table.

10. The method of claim 9 wherein the step of establishing said electrical signals indicative of heart rate in persons having autonomic diabetic neuropathy is determined by establishing two diabetic groups based on autonomic status.

11. The method of claim 10 further including the step of using discriminant analysis to calculate a discriminant function to separate the groups.

12. The method of claim 8 wherein said encoding is linear predictor-derived cepstral encoding.

13. The method of claim 8 further including the step of calculating time domain and spectral heart rate variability indices for patients and an individual.

14. The method of claim 13 wherein said indices include heart rate mean, variance, spectral energies in the very low frequency, low frequency, high frequency, and total power bands.

15. The method of claim 14 wherein said very low frequency band is within the range of 0.003-0.04 Hz.

16. The method of claim 14 wherein said low frequency band is within the range of 0.04-15 Hz.

17. The method of claim 14 wherein said high frequency band is within the range of 0.15-0.4 Hz.

18. The method of claim 14 wherein said total power band is within the range of 0.03-0.4 Hz.

19. The method of claim 13 further including the step of calculating low frequency/high frequency ratio.

20. The method of claim 19 further including the step of calculating the ratio of low frequency in tilt to high frequency in supine to generate an index of autonomic balance.

21. A method of screening for neuropathy in diabetics using cepstral analysis of heart rate comprising the steps of:

sampling a patient'"'"'s heart rate while the patient is at rest;

generating electrical signals representing the patient'"'"'s instantaneous heart rate;

generating at least one cepstral vector from the electrical signals, said cepstral vector being representative of all power bands of the electrical signals;

generating a classifier of heart rate cepstral vectors from a patient population having early diabetic autonomic neuropathy;

comparing the at least one cepstral vector of the patient to one or more cepstral vectors within the classifier; and

using the results of the comparison to indicate the presence or absence of autonomic neuropathy in the patient.

22. The method of claim 21, wherein the step of generating the classifier comprises applying linear discriminant analysis to the patient population to calculate the classifier.