Method, Computer Program, and System for Automated Real-Time Signal Analysis for Detection, Quanitification, and Prediction of Signal Changes

• US 20130012825A1
• Filed: 09/10/2012
• Published: 01/10/2013
• Est. Priority Date: 04/03/2000
• Status: Active Grant

• Current Assignee: Flint Hills Scientific, LLC
• Original Assignee: Flint Hills Scientific, LLC
• Inventors: Nikitin, Alexei V., Frei, Mark G., Bhavaraju, Naresh C., Osorio, Ivan, Davidchack, Ruslan L.
• Granted Patent: US 9,659,216 B2
• Time in Patent Office: Days
• US Class Current: 600/509
• CPC Class Codes: G06F 17/10 Complex mathematical operations function generation by table look-up G06F1/03; evaluation of elementary functions by calculation G06F7/544
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06F 2218/12 Classification; Matching
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/10 Signal processing, e.g. from mass spectrometry [MS] or from PCR
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

A method, computer program, and system for real-time signal analysis providing characterization of temporally-evolving densities and distributions of signal features of arbitrary-type signals in a moving time window by tracking output of order statistic filters (also known as percentile, quantile, or rank-order filters). Given a raw input signal of arbitrary type, origin, or scale, the present invention enables automated quantification and detection of changes in the distribution of any set of quantifiable features of that signal as they occur in time. Furthermore, the present invention'"'"'s ability to rapidly and accurately detect changes in certain features of an input signal can also enable prediction in cases where the detected changes associated with an increased likelihood of future signal changes.

Claims

1. A non-transitive computer readable program storage device encoded with instructions that, when executed by a computer, perform a method of detecting an epileptic seizure using an estimate of a percentile value of a patient'"'"'s heart signal in a plurality of time windows, the method comprising the steps of:

(a) receiving a heart signal from a patient;

(b) determining a heart feature signal from said heart signal;

(c) determining a first percentile tracking filter estimate from the heart feature signal in a foreground time window;

(d) determining a second percentile tracking filter estimate from the heart feature signal in a background time window;

(e) determining a ratio of the first percentile tracking filter estimate and the second percentile tracking filter estimate;

(f) comparing the ratio to a threshold value;

(g) detecting an epileptic seizure when the ratio crosses the threshold value; and

(i) in response to the detecting, providing an output indicative of the detection of the seizure.

2. The non-transitive computer readable program storage device of claim 46, further comprising:

(j) receiving the percentile value from a programmer

3. The non-transitive computer readable program storage device of claim 46, wherein determining the first percentile tracking filter estimate and the second percentile tracking filter estimate each comprise determining a time-weighted function of the heart feature signal, and estimating the percentile tracking filter estimate from the time-weighted function using a difference equation.

4. The non-transitive computer readable program storage device of claim 48, wherein the time-weighted function is determined by first determining at least one parameter upon which the time-weighted function depends and where the time-weighted function is selected from the group consisting of:

Gaussian distributions, Chi-square distributions, Uniform distributions, and Triangle distributions.

5. The non-transitive computer readable program storage device of claim 46, wherein at least one of the first and second the percentile tracking filter estimates is updated using a difference equation, wherein the difference equation updates the estimate by:

incrementing the estimate by a first non-negative value if the heart feature signal is greater than the most recent percentile tracking filter estimate,

decrementing the estimate by a second non-negative value if the heart feature signal is below the most recent percentile tracking filter estimate, or

leaving the estimate unchanged if the heart feature signal value is equal to the most recent percentile tracking filter estimate.

6. The non-transitive computer readable program storage device of claim 50, wherein the first non-negative value and the second non-negative value are equal.

7. The non-transitive computer readable program storage device of claim 51, wherein the first non-negative value and the second non-negative value are unequal.

8. The non-transitive computer readable program storage device of claim 46, wherein the heart feature signal is the heart signal.

9. A non-transitive computer readable program storage device encoded with instructions that, when executed by a computer, perform a method of detecting an epileptic seizure using an estimate of a percentile value of a patient'"'"'s heart signal in a plurality of time windows, the method comprising the steps of:

(a) receiving a heart signal from a patient;

(b) determining a heart feature signal from said heart signal;

(c) determining a first percentile tracking filter estimate from the heart feature signal in a time window;

(d) first percentile tracking filter estimate to a reference value;

(g) detecting an epileptic seizure first percentile tracking filter estimate crosses the reference value; and

(i) in response to the detecting, providing an output indicative of the detection of the seizure.