Methods for improved forewarning of critical events across multiple data channels

• US 20040059761A1
• Filed: 09/22/2003
• Published: 03/25/2004
• Est. Priority Date: 07/12/2002
• Status: Active Grant

• Current Assignee: UT-Battelle LLC
• Original Assignee: UT-Battelle LLC
• Inventors: Hively, Lee M.
• Granted Patent: US 7,209,861 B2
• Time in Patent Office: Days
• US Class Current: 708/160
• CPC Class Codes: A61B 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy
• CPC Class Codes: A61B 5/412 Detecting or monitoring sepsis
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• 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: G06F 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 2218/02 Preprocessing
• CPC Class Codes: G06F 2218/12 Classification; Matching
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

This disclosed invention concerns improvements in forewarning of critical events via phase-space dissimilarity analysis of data from mechanical devices, electrical devices, biomedical data, and other physical processes. First, a single channel of process-indicative data is selected that can be used in place of multiple data channels without sacrificing consistent forewarning of critical events. Second, the method discards data of inadequate quality via statistical analysis of the raw data, because the analysis of poor quality data always yields inferior results. Third, two separate filtering operations are used in sequence to remove both high-frequency and low-frequency artifacts using a zero-phase quadratic filter. Fourth, the method constructs phase-space dissimilarity measures (PSDM) by combining of multi-channel time-serial data into a multi-channel time-delay phase-space reconstruction. Fifth, the method uses a composite measure of dissimilarity (C_i) to provide a forewarning of failure and an indicator of failure onset.

Claims

1. A method for processing data to provide a forewarning of a critical event, comprising:

acquiring a plurality of sets of data for at least one channel of data for at least one test subject or process;

computing a renormalized measure of dissimilarity from distribution functions derived from a phase space for each respective channel of data;

comparing said renormalized measure of dissimilarity to a threshold (U_c) for a number of occurrences (N_occ) to indicate a condition change in said renormalized measure of dissimilarity;

detecting a simultaneous condition change in a plurality (N_SIM) of renormalized measures of dissimilarity to determine a forewarning of the critical event; and

wherein said one channel of data corresponds to a parameter that is calculated from a plurality of parameters corresponding to a plurality of channels of data.

2. The method of claim 1, wherein the test subject is a human patient.

3. The method of claim 1, wherein the test subject is a mechanical device or physical process.

4. The method of claim 1, wherein the process-indicative data is three-phase electrical power.

5. The method of claim 1, wherein the process-indicative data is vibration mechanical power.

6. The method of claim 1, wherein the process-indicative data is a difference between two channels of EEG data.

7. The method of claim 1, further comprising:

performing a first filtering of each set of data with a zero-phase quadratic filter that filters out high-frequency artifacts; and

performing a second filtering of each set of data with a zero-phase quadratic filter to filter out low-frequency artifacts.

8. The method of claim 1, further comprising:

sorting the data values into ascending order from a minimum to a maximum;

determining the number of unique signal values (n) and the corresponding relative occurrence frequency (F_k) for each unique signal value (v_k);

displaying a graph of frequency (F_k) versus values (v_k) in each bin in a connected phase space; and

discarding data that has [v_k−

(N/n)]/σ

₃>

z, where the value of z is determined by solving 1/n=½

erfc (z/{square root}2), and σ

₃ is the standard deviation in the occurrence frequency.

9. The method of claim 1, with an alternative embodiment for event forewarning, comprising determining a sequence of renormalized phase space dissimilarity measures from data sets for the test subject or process;

summing said renormalized measures into a composite measure, C_i, for the i-th data set;

performing a least-squares analysis over a window of m points of the said composite measure to obtain a straight line, y_i=ai+b, that best fits said composite data in a least-squares sense;

determining the variance, σ

₁²=Σ

_i(y_i−

C_i)²/(m−

1), of said composite measure with respect to the straight line fit;

obtaining the variability of the sequel window of m sequential points via the statistic, G=Σ

_i (y_i−

C_i)²/σ

₁²;

comparing said value of G to the running maximum value of the same statistic, G_max;

determining the forewarning of or failure onset of a critical event (such as a machine failure), when G is significantly more than G_max;

obtaining the ratio, R=(G_max)_k/(G_max)<sub>k−

1</sub>, of the present and previous running maximum in G_i and determining the forewarning of a critical event when R is significantly more than some limit.

17. The method of claim 1, wherein the process-indicative data is vibration mechanical power.

18. The method of claim 1, wherein the process-indicative data is a difference between two channels of EEG data.

10. A method for processing data to provide a forewarning of a critical event, comprising:

acquiring a plurality of sets of data for at least two channels of data for at least one test subject or process;

computing to a multi-channel time-delay phase-space (PS) construction, which has the form;

y(i)=[s(1)_i, s(1)_i+λ

, s(1)_i+2λ

, . . . , s(2), s(2)_i+λ

, s(2)_i+2λ

, . . . , s(C)_i, s(C)_i+λ

, s(C)_i+2λ

, . . . ], where s(c) denotes the symbolized data for c-th channel;

computing a renormalized measure of dissimilarity from distribution functions derived from the (non)connected phase space for the multi-channel of data;

comparing said renormalized measure of dissimilarity to a threshold (U_C) for a number of occurrences (N_OCC) to indicate a condition change in said renormalized measure of dissimilarity; and

detecting a simultaneous condition change in a plurality (N_SIM) of renormalized measures of dissimilarity to determine a forewarning of the critical event.

11. The method of claim 10, further comprising:

performing a first filtering of each set of data with a zero-phase quadratic filter that filters out high-frequency artifacts; and

performing a second filtering of each set of data with a zero-phase quadratic filter to filter out low-frequency artifacts.

12. The method of claim 10, using an alternative embodiment for event forewarning, comprising determining a sequence of renormalized phase space dissimilarity measures from data sets collected during increasingly severe fault conditions;

summing said renormalized measures into a composite measure, C_i, for the i-th data set;

performing a least-squares analysis over a window of m points of the said composite measure to obtain a straight line, y_i=ai+b, that best fits said composite data in a least-squares sense;

determining the variance, σ

₁²=Σ

_i(y_i−

C_i)²/(m−

1), of said composite measure with respect to the straight line fit;

obtaining the variability of a sequel window of m sequential points via the statistic, G=Σ

_i(y_i−

C_i)²/(σ

₁²;

comparing said value of G to the running maximum value of the same statistic, G_max; and

determining the onset of a critical event, such as forewarning of a machine failure, when G is significantly more than G(non-end-of-life), or when R is significantly more than R(non-end-of-life), or detection of failure onset when G is significantly greater than G(end-of-life).

13. The method of claim 10, wherein the test subject is a human patient.

14. The method of claim 10, wherein the test subject is a mechanical device or physical process.

15. The method of claim 10, further comprising:

sorting the data values into ascending order from a minimum to a maximum;

determining the number of unique signal values (n) and the corresponding relative occurrence frequency (F_k) for each unique signal value (v_k);

displaying a graph of frequency (F_k) versus values (v_k) in each bin in a connected phase space; and

discarding data that has [v_k−

(N/n)]/σ

₃>

z, where the value of z is determined by solving 1/n=½

erfc(z/{square root}2), and σ

₃ is the standard deviation in the occurrence frequency.

16. The method of claim 10, wherein the process-indicative data is three-phase electrical power.