Biomarkers for determining susceptibility to SUDEP

• US 10,327,661 B1
• Filed: 07/21/2015
• Issued: 06/25/2019
• Est. Priority Date: 07/22/2014
• Status: Active Grant

• Current Assignee: Louisiana Tech Research Corporation
• Original Assignee: Louisiana Tech Research Corporation
• Inventors: Iasemidis, Leonidas D., Vlachos, Ioannis, Glasscock, Albert Edward
• Primary Examiner(s): Agahi, Puya
• Application Number:
  US14/804,658
• Time in Patent Office: 1,435 Days
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• 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 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/374 Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
• CPC Class Codes: A61B 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

A method for determining an increased risk of death of a patient includes receiving ECG data of the patient generated during a first time period; receiving EEG data of the patient generated during the first time period; composing a feature of the ECG data and a feature of the EEG data over a common time frame and determining a statistical measure of association between the ECG data and the EEG data; and determining whether the degree of association exceeds a predetermined threshold, thereby indicating whether an increased risk is present.

Claims

1. A method for determining an increased risk of death of a patient comprising the steps of:

a. receiving electrocardiogram (ECG) data of the patient generated during a first time period;

b. receiving electroencephalogram (EEG) data of the patient generated during the first time period;

c. composing a feature of the ECG data and a feature of the EEG data over a common time frame, wherein the feature composed from the EEG or ECG data is at least one from the group consisting of mean, standard deviation, Kurtosis, skewness, autoregressive modeling coefficients, Vector autoregressive modeling coefficients, Barlow parameters, Teager energy, Hjorth parameters, Pearson product-moment correlation coefficient, Rank correlation, spectral band power at specific frequencies/frequency bands, normalized spectral band power, spectral edge frequency, median frequency, coherence, directed transfer function, partial coherence, directed coherence, generalized partial directed coherence, Shannon entropy, approximate entropy, sample entropy, spectral entropy, mutual information, transfer entropy, Lyapunov exponents, correlation dimension, recurrence quantification analysis, dynamical entrainment, non-linear interdependence, detrended fluctuation analysis, Hurst exponent, Lempel-Ziv complexity, phase synchronization, phase-amplitude coupling, bispectrum, and bicoherence;

d. determining a statistical measure of association between features of the ECG data and the EEG data;

e. determining whether the statistical measure of association exceeds a predetermined threshold, thereby indicating whether the increased risk of death is present;

f. in response to an indication of the increased risk of death, either (i) delivering a series of electrical signals to therapeutically counter-act brain electrical impulses related to seizures;

or (ii) administering a therapeutic drug to the patient; and

g. wherein the patient suffers from at least one condition from the group consisting of epileptic seizures, status epilepticus, sudden infant death syndrome, sudden unexpected death in epilepsy, and sudden arrythmic death syndrome.

2. The method of claim 1, wherein the first time period is a time period when the patient is not in a seizure state.

3. The method of claim 1, wherein the feature of the EEG data is a coherence between brain recording sites in at least one of the bands from the group consisting of the δ

-band, θ

-band, α

-band, β

-band, or the γ

-band.

4. The method of claim 3, wherein the feature of the ECG data is at least one of a median (M), an Inter Quartile Range (IQR), or an entropy (ENT) of either R peak amplitudes or length of RR intervals in QRS complexes of the ECG data.

5. The method of claim 4, wherein the feature of the EEG data and the feature of the ECG data are at least one of the pairs C(δ

)-M_Rpeak;

C(δ

)-IQR_Rpeak;

C(δ

)-ENT_RR;

C(δ

)-M_RR;

or C(β

)-M_RR.

6. The method of claim 1, wherein the statistical measure of association is a phi coefficient between the feature of the EEG data and the feature of the ECG data.

7. The method of claim 6, wherein the statistical measure of association threshold is 0.1.

8. The method of claim 1, further comprising the steps of (i) receiving respiratory data of the patient generated during the first time period;

(ii) composing a feature of the respiratory data; and

(iii) determining a statistical measure of association between the respiratory data and the EEG data and/or the ECG data.

9. The method of claim 1, further comprising the step of, in response to an indication of the increased risk of death, either (i) delivering a series of electrical signals to therapeutically counter-act brain electrical impulses related to seizures;

or (ii) administering a therapeutic drug to the patient.

10. A method for treating a patient determined to have an increased risk of death comprising the steps of:

a. receiving electrocardiogram (ECG) data and electroencephalogram (EEG) data from the patient generated during a first time period when the patient is in an interictal state;

b. composing a feature of the ECG data and a feature of the EEG data over a common time frame from the first time period and determining a statistical measure of association between the features of ECG data and the EEG data;

c. determining whether the degree of the statistical measure of association exceeds a predetermined threshold, thereby indicating whether the increased risk of death is present; and

d. in response to an indication of the increased risk of death, either (i) delivering a series of electromagnetic signals to therapeutically counter-act brain electrical impulses related to seizures;

or (ii) administering a therapeutic drug to the patient; and

e. wherein the patient suffers from at least one condition from the group consisting of epileptic seizures, status epilepticus, sudden infant death syndrome, sudden unexpected death in epilepsy, and sudden arrythmic death syndrome.

11. The method of claim 10, wherein the feature composed from the EEG or ECG data is at least one from the group consisting of mean, standard deviation, Kurtosis, skewness, autoregressive modeling coefficients, Vector autoregressive modeling coefficients, Barlow parameters, Teager energy, Hjorth parameters, Pearson product-moment correlation coefficient, Rank correlation, spectral band power at specific frequencies/frequency bands, normalized spectral band power, spectral edge frequency, median frequency, coherence, directed transfer function, partial coherence, directed coherence, generalized partial directed coherence, Shannon entropy, approximate entropy, sample entropy, spectral entropy, mutual information, transfer entropy, Lyapunov exponents, correlation dimension, recurrence quantification analysis, dynamical entrainment, non-linear interdependence, detrended fluctuation analysis, Hurst exponent, Lempel-Ziv complexity, phase synchronization, phase-amplitude coupling, bispectrum, and bicoherence.

12. The method of claim 10, wherein the feature of the EEG data is a coherence between brain recording sites in at least one of the bands from the group consisting of the δ

-band, θ

-band, α

-band, β

-band, or the γ

-band.

13. The method of claim 12, wherein the feature of the ECG data is at least one of a median (M), an Inter Quartile Range (IQR), or an entropy (ENT) of either R peak amplitudes or length of RR intervals in QRS complexes of the ECG data.

14. The method of claim 13, wherein the feature of the EEG data and the feature of the ECG data are at least one of the pairs C(δ

)-M_Rpeak;

C(δ

)-IQR_Rpeak;

C(δ

)-ENT_RR;

C(δ

)-M_RR;

or C(β

)-M_RR.

15. The method of claim 10, wherein the statistical measure of association is a phi coefficient between the feature of the EEG data and the feature of the ECG data.

16. The method of claim 15, wherein the statistical measure of association threshold is 0.1.