Epileptic seizure prediction by non-linear methods

• US 5,857,978 A
• Filed: 03/20/1996
• Issued: 01/12/1999
• Est. Priority Date: 03/20/1996
• Status: Expired due to Fees

• Current Assignee: Lockheed Martin Energy Systems, Inc. (AES Corporation)
• Original Assignee: Lockheed Martin Energy Systems, Inc. (AES Corporation)
• Inventors: Hively, Lee M., Daw, C. Stuart, Clapp, Ned E., Lawkins, William F.
• Primary Examiner(s): Bahr, Jennifer
• Assistant Examiner(s): CARTER, RYAN CHRISTOPHER
• Application Number:
  US08/619,030
• Time in Patent Office: 1,028 Days
• Field of Search: 128/731-733, 600/544-546
• US Class Current: 600/544
• 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

Abstract

Methods and apparatus for automatically predicting epileptic seizures monitor and analyze brain wave (EEG or MEG) signals. Steps include: acquiring the brain wave data from the patient; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis tools; obtaining time serial trends in the nonlinear measures; comparison of the trend to known seizure predictors; and providing notification that a seizure is forthcoming.

Claims

1. A method for automatically predicting an epileptic seizure in a patient comprising the steps of:

(a) providing at least one channel of a patient'"'"'s raw brain wave data, called e-data, selected from the group consisting of electroencephalogram data and magnetoencephalogram data;

(b) separating the e-data into artifact data, called f-data, and artifact-free data, called g-data, while preventing phase distortions in the data;

(c) processing g-data through a low-pass filter to produce a low-pass-filtered version of g-data, called h-data;

(d) applying at least one measure selected from the group consisting of linear statistical measures minimum and maximum, standard deviation, absolute minimum deviation, skewedness, and kurtosis, and nonlinear measures time steps per cycle, Kolmogorov entropy, first minimum in mutual information function, and correlation dimension to at least one type of data selected from the group consisting of e-data, f-data, g-data, and h-data to provide at least one time serial sequence of nonlinear measures, from which at least one indicative trend selected from the group consisting of abrupt increases, abrupt decreases, peaks, valleys, and combinations thereof is determined;

(e) comparing at least one indicative trend with at least one known seizure predictor; and

(f) determining from said comparison whether an epileptic seizure is oncoming in the patient.

2. The method as described in claim 1 wherein said at least one time serial sequence of linear and nonlinear measures is selected from the group consisting of:

the standard deviation of the correlation dimension for e-data;

the standard deviation of the correlation dimension for f-data;

the standard deviation of the correlation dimension for g-data;

the standard deviation of the correlation dimension for h-data;

the Kolmogorov entropy for f-data;

the Kolmogorov entropy for h-data;

the first minimum in the Mutual Information Function for e-data;

the first minimum in the Mutual Information Function for g-data;

the average Kolmogorov entropy for e-data;

the skewedness for f-data;

the kurtosis for f-data, and combinations thereof.

3. The method as described in claim 1 wherein said at least one seizure predictor is selected from the group consisting of:

(a) a long-time, large value of the standard deviation of the skewedness of e-data;

(b) a long-time, large value of the standard deviation of the skewedness of f-data;

(c) quasi-periodic maxima and minima in the average Kolmogorov entropy of e-data;

(d) quasi-periodic maxima and minima in the average Kolmogorov entropy of g-data;

(e) quasi-periodic maxima and minima in the average Kolmogorov entropy of h-data;

(f) quasi-periodic maxima and minima in the average correlation dimension of e-data;

(g) quasi-periodic maxima and minima in the average correlation dimension of g-data;

(h) quasi-periodic maxima and minima in the average correlation dimension of h-data;

(i) quasi-periodic maxima and minima in the average number of time steps per cycle of e-data;

(j) quasi-periodic maxima and minima in the average number of time steps per cycle of g-data;

(k) quasi-periodic maxima and minima in the average number of time steps per cycle of h-data;

(l) coincidence of the minima and maxima selected from the group consisting of items c through k of this list and combinations thereof;

(m) a valley in the average of the minimum in the mutual information function in g-data;

(n) a peak in the standard deviation of the minimum in the mutual information function in g-data;

(o) coincidence of the extrema selected from the group consisting of items m and n of this list and combinations thereof with at least one of extrema times selected from the group consisting of items c through k of this list and combinations thereof;

(p) large decreases and increases in the standard deviation of the correlation dimension of e-data;

(q) large decreases and increases in the standard deviation of the correlation dimension of f-data;

(r) large decreases and increases in the standard deviation of the correlation dimension of g-data;

(s) large decreases and increases in the standard deviation of the correlation dimension of h-data;

(t) large, aperiodic variations in Kolmogorov entropy of f-data, including some values close to zero for >

10 seconds;

(u) large, aperiodic variations in Kolmogorov entropy of h-data, including some values close to zero for >

10 seconds;

(v) the average of the first minimum of the mutual information function of e-data gradually decreases below a critical value;

(w) the average of the first minimum of the mutual information function of g-data gradually decreases below a critical value;

(x) quasi-periodic maxima and minima in the average of the first minimum of the mutual information function of f-data;

(y) aperiodic maxima and minima in the average e Kolmogorov entropy of e-data;

(z) oscillations in Kolmogorov entropy of e-data of increasing or decreasing magnitude, about the average Kolmogorov entropy of item y;

(aa) small variation ratio in average skewedness (Δ

_t /Δ

_n) of g-data;

(ab) small variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of skewedness of f-data;

(ac) small variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of skewedness of g-data;

(ad) small variation ratio in average kurtosis (Δ

_t /Δ

_n) of e-data;

(ae) small variation ratio in average kurtosis (Δ

_t /Δ

_n) of g-data;

(af) small variation ratio in average kurtosis (Δ

_t /Δ

_n) of h-data;

(ag) small variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of kurtosis of e-data;

(ah) small variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of kurtosis of g-data;

(ai) small variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of kurtosis of h-data;

(aj) small variation ratio in average number of timesteps per cycle (Δ

_t /Δ

_n) of g-data;

(ak) small variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of number of timesteps per cycle of g-data;

(al) small variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of number of timesteps per cycle of h-data;

(am) small variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of Kolmogorov entropy of f-data;

(an) small variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of Kolmogorov entropy of h-data;

(ao) small variation ratio in the average of the first minimum in mutual information function (Δ

_t /Δ

_n) of f-data;

(ap) small variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of first minimum in mutual information function of f-data;

(aq) small variation ratio in average correlation dimension (Δ

_t /Δ

_n) of g-data;

(ar) large variation ratio in average (Δ

_t /Δ

_n) of the standard deviation of e-data;

(as) large variation ratio in average (Δ

_t /Δ

_n) of the standard deviation of f-data;

(at) large variation ratio in average (Δ

_t /Δ

_n) of the standard deviation of g-data;

(au) large variation ratio in average (Δ

_t /Δ

_n) of the standard deviation of h-data;

(av) large variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of the standard deviation of e-data;

(aw) large variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of the standard deviation of f-data;

(ax) large variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of the standard deviation of h-data;

(ay) large variation ratio in the average of the absolute average deviation (Δ

_t /Δ

_n) of e-data;

(az) large variation ratio in the average of the absolute average deviation (Δ

_t /Δ

_n) of f-data;

(ba) large variation ratio in the average of the absolute average deviation (Δ

_t /Δ

_n) of g-data;

(bb) large variation ratio in the average of the absolute average deviation (Δ

_t /Δ

_n) of h-data;

(bc) large variation ratio in the standard deviation of the absolute average deviation (Δ

σ

_t /Δ

σ

_n) of e-data;

(bd) large variation ratio in the standard deviation of the absolute average deviation (Δ

σ

_t /Δ

σ

_n) of f-data;

(be) large variation ratio in the standard deviation of the absolute average deviation (Δ

σ

_t /Δ

σ

_n) of h-data;

(bf) large variation ratio in average skewedness (Δ

_t /Δ

_n) of e-data;

(bg) large variation ratio in average skewedness (Δ

_t /Δ

_n) of f-data;

(bh) large variation ratio in average skewedness (Δ

_t /Δ

_n) of g-data;

(bi) large variation ratio in average skewedness (Δ

_t /Δ

_n) of h-data;

(bj) large variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of skewedness of e-data;

(bk) large variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of skewedness of f-data;

(bl) large variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of skewedness of g-data;

(bm) large variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of skewedness of h-data;

(bn) large variation ratio in average kurtosis (Δ

_t /Δ

_n) of e-data;

(bo) large variation ratio in average kurtosis (Δ

_t /Δ

_n) of f-data;

(bp) large variation ratio in average kurtosis (Δ

_t /Δ

_n) of g-data;

(bq) large variation ratio in average kurtosis (Δ

_t /Δ

_n) of h-data;

(br) large variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of kurtosis of e-data;

(bs) large variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of kurtosis of f-data;

(bt) large variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of kurtosis of g-data;

(bu) large variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of kurtosis of h-data;

(bv) small variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of Kolmogorov entropy of g-data;

(bw) large variation ratio in the average of the first minimum in mutual information function (Δ

_t /Δ

_n) of f-data;

(bx) large variation ratio in the average of the first minimum in mutual information function (Δ

_t /Δ

_n) of g-data;

(by) large variation ratio in the average of the first minimum in mutual information function (Δ

_t /Δ

_n) of h-data;

(bz) large variation ratio in average correlation dimension (Δ

_t /Δ

_n) of h-data;

(ca) large variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of correlation dimension of e-data;

(cb) large variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of correlation dimension of h-data; and

(cc) combinations thereof.

4. The method as described in claim 1 wherein the artifact data is separated from the raw data by use of a zero-phase filter.

5. The method as described in claim 1 wherein said low-pass filter comprises a standard low-pass filter selected from the group consisting of second-order, third-order and fourth-order low-pass filters at frequencies between about 35 Hz and about 60 hz.

6. The method as described in claim 5 wherein said low-pass filter comprises a standard fourth-order low-pass filter at 50 Hz.

7. Apparatus for automatically predicting an epileptic seizure in a patient comprising:

(a) data provision means for providing at least one channel of raw brain wave data, called e-data, selected from the group consisting of electroencephalogram data and magnetoencephalogram data;

(b) separation means for separating e-data into artifact data, called f-data, and artifact-free data, called g-data, while preventing phase distortions in the data, communicably connected to said data provision means;

(c) low-pass filter means for filtering g-data to produce a low-pass filtered version of g-data, called h-data, communicably connected to said separation means;

(d) application means for applying at least one measure selected from the group of consisting of linear statistical measures minimum and maximum, standard deviation, absolute minimum deviation, skewedness, and kurtosis, and nonlinear measures time steps per cycle, Kolmogorov entropy, first minimum in mutual information function, and correlation dimension to at least one type of data selected from the group consisting of e-data, f-data, g-data, and h-data to provide at least one time serial sequence of nonlinear measures, from which at least one indicative trend selected from the group consisting of abrupt increases, abrupt decreases, peaks, valleys, and combinations thereof is determined, communicably connected to said low-pass filter means;

(e) comparison means for comparing at least one indicative trend with known seizure predictors, connected to said application means; and

,

(f) determination means for determining from the comparison whether an epileptic seizure is oncoming in the patient, communicably connected to said comparison means.

8. The apparatus as described in claim 7 wherein said at least one time serial sequence of linear and nonlinear measures is selected from the group consisting of:

the standard deviation of the correlation dimension for e-data;

the standard deviation of the correlation dimension for f-data;

the standard deviation of the correlation dimension for g-data;

the standard deviation of the correlation dimension for h-data;

the Kolmogorov entropy for f-data;

the Kolmogorov entropy for h-data;

the first minimum in the Mutual Information Function for e-data;

the first minimum in the Mutual Information Function for g-data;

the average Kolmogorov entropy for e-data;

the skewedness for f-data;

the kurtosis for f-data, and combinations thereof.

9. The apparatus as described in claim 7 wherein said at least one seizure predictor is selected from the group consisting of:

(a) a long-time, large value of the standard deviation of the skewedness of e-data;

(b) a long-time, large value of the standard deviation of the skewedness of f-data;

(c) quasi-periodic maxima and minima in the average Kolmogorov entropy of e-data;

(d) quasi-periodic maxima and minima in the average Kolmogorov entropy of g-data;

(e) quasi-periodic maxima and minima in the average Kolmogorov entropy of h-data;

(f) quasi-periodic maxima and minima in the average correlation dimension of e-data;

(g) quasi-periodic maxima and minima in the average correlation dimension of g-data;

(h) quasi-periodic maxima and minima in the average correlation dimension of h-data;

(i) quasi-periodic maxima and minima in the average number of time steps per cycle of e-data;

(j) quasi-periodic maxima and minima in the average number of time steps per cycle of g-data;

(k) quasi-periodic maxima and minima in the average number of time steps per cycle of h-data;

(l) coincidence of the minima and maxima selected from the group consisting of items c through k of this list and combinations thereof;

(m) a valley in the average of the minimum in the mutual information function in g-data;

(n) a peak in the standard deviation of the minimum in the mutual information function in g-data;

(o) coincidence of the extrema selected from the group consisting of items m and n of this list and combinations thereof with at least one of extrema times selected from the group consisting of items c through k of this list and combinations thereof;

(p) large decreases and increases in the standard deviation of the correlation dimension of e-data;

(q) large decreases and increases in the standard deviation of the correlation dimension of f-data;

(r) large decreases and increases in the standard deviation of the correlation dimension of g-data;

(s) large decreases and increases in the standard deviation of the correlation dimension of h-data;

(t) large, aperiodic variations in Kolmogorov entropy of f-data, including some values close to zero for >

10 seconds;

(u) large, aperiodic variations in Kolmogorov entropy of h-data, including some values close to zero for >

10 seconds;

(v) the average of the first minimum of the mutual information function of e-data gradually decreases below a critical value;

(w) the average of the first minimum of the mutual information function of g-data gradually decreases below a critical value;

(x) quasi-periodic maxima and minima in the average of the first minimum of the mutual information function of f-data;

(y) aperiodic maxima and minima in the average Kolmogorov entropy of e-data;

(z) oscillations in Kolmogorov entropy of e-data of increasing or decreasing magnitude, about the average Kolmogorov entropy of item y;

(aa) small variation ratio in average skewedness (Δ

_t /Δ

_n) of g-data;

(ab) small variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of skewedness of f-data;

(ac) small variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of skewedness of g-data;

(ad) small variation ratio in average kurtosis (Δ

_t /Δ

_n) of e-data;

(ae) small variation ratio in average kurtosis (Δ

_t /Δ

_n) of g-data;

(af) small variation ratio in average kurtosis (Δ

_t /Δ

_n) of h-data;

(ag) small variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of kurtosis of e-data;

(ah) small variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of kurtosis of g-data;

(ai) small variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of kurtosis of h-data;

(aj) small variation ratio in average number of timesteps per cycle (Δ

_t /Δ

_n) of g-data;

(ak) small variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of number of timesteps per cycle of g-data;

(al) small variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of number of timesteps per cycle of h-data;

(am) small variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of Kolmogorov entropy of f-data;

(an) small variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of Kolmogorov entropy of h-data;

(ao) small variation ratio in the average of the first minimum in mutual information function (Δ

_t /Δ

_n) of f-data;

(ap) small variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of first minimum in mutual information function of f-data;

(aq) small variation ratio in average correlation dimension (Δ

_t /Δ

_n) of g-data;

(ar) large variation ratio in average (Δ

_t /Δ

_n) of the standard deviation of e-data;

(as) large variation ratio in average (Δ

_t /Δ

_n) of the standard deviation of f-data;

(at) large variation ratio in average (Δ

_t /Δ

_n) of the standard deviation of g-data;

(au) large variation ratio in average (Δ

_t /Δ

_n) of the standard deviation of h-data;

(av) large variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of the standard deviation of e-data;

(aw) large variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of the standard deviation of f-data;

(ax) large variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of the standard deviation of h-data;

(ay) large variation ratio in the average of the absolute average deviation (Δ

_t /Δ

_n) of e-data;

(az) large variation ratio in the average of the absolute average deviation (Δ

_t /Δ

_n) of f-data;

(ba) large variation ratio in the average of the absolute average deviation (Δ

_t /Δ

_n) of g-data;

(bb) large variation ratio in the average of the absolute average deviation (Δ

_t /Δ

_n) of h-data;

(bc) large variation ratio in the standard deviation of the absolute average deviation (Δ

σ

_t /Δ

σ

_n) of e-data;

(bd) large variation ratio in the standard deviation of the absolute average deviation (Δ

σ

_t /Δ

σ

_n) of f-data;

(be) large variation ratio in the standard deviation of the absolute average deviation (Δ

σ

_t /Δ

σ

_n) of h-data;

(bf) large variation ratio in average skewedness (Δ

_t /Δ

_n) of e-data;

(bg) large variation ratio in average skewedness (Δ

_t /Δ

_n) of f-data;

(bh) large variation ratio in average skewedness (Δ

_t /Δ

_n) of g-data;

(bi) large variation ratio in average skewedness (Δ

_t /Δ

_n) of h-data;

(bj) large variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of skewedness of e-data;

(bk) large variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of skewedness of f-data;

(bl) large variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of skewedness of g-data;

(bm) large variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of skewedness of h-data;

(bn) large variation ratio in average kurtosis (Δ

_t /Δ

_n) of e-data;

(bo) large variation ratio in average kurtosis (Δ

_t /Δ

_n) of f-data;

(bp) large variation ratio in average kurtosis (Δ

_t /Δ

_n) of g-data;

(bq) large variation ratio in average kurtosis (Δ

_t /Δ

_n) of h-data;

(br) large variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of kurtosis of e-data;

(bs) large variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of kurtosis of f-data;

(bt) large variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of kurtosis of g-data;

(bu) large variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of kurtosis of h-data;

(bv) small variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of Kolmogorov entropy of g-data;

(bw) large variation ratio in the average of the first minimum in mutual information function (Δ

_t /Δ

_n) of f-data;

(bx) large variation ratio in the average of the first minimum in mutual information function (Δ

_t /Δ

_n) of g-data;

(by) large variation ratio in the average of the first minimum in mutual information function (Δ

_t /Δ

_n) of h-data;

(bz) large variation ratio in average correlation dimension (Δ

_t /Δ

_n) of h-data;

(ca) large variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of correlation dimension of e-data;

(cb) large variation ratio in standard deviation (Δ

σ

_t /Δ

σ

_n) of correlation dimension of h-data; and

(cc) combinations thereof.

10. The apparatus as described in claim 7 wherein said separation means comprises a zero-phase filter.

11. The apparatus as described in claim 10 wherein said zero-phase filter is embodied in a programmed integrated-circuit semiconductor chip.

12. The apparatus as described in claim 7 wherein said low-pass filter means comprises a standard low-pass filter selected from the group consisting of second-order, third-order, and fourth-order low-pass filters at frequencies between about 35 Hz and about 60 Hz.

13. The apparatus as described in claim 7 wherein said low-pass filter comprises a standard fourth-order low-pass filter at about 50 Hz.

14. The apparatus as described in claim 7 further comprising notification means for providing notification that a seizure is oncoming in the patient, the notification means being communicably connected to said determination means.