System and method for evaluating an electrophysiological signal

• US 8,923,958 B2
• Filed: 09/06/2012
• Issued: 12/30/2014
• Est. Priority Date: 02/04/2011
• Status: Active Grant

• Current Assignee: Analytics For Life, Inc.
• Original Assignee: Analytics For Life, Inc.
• Inventors: Gupta, Sunny, Yazdi, Mohsen Najafi
• Primary Examiner(s): KAHELIN, MICHAEL WILLIAM
• Application Number:
  US13/605,364
• Publication Number:
  US 20130096394A1
• Time in Patent Office: 845 Days
• Field of Search: None
• US Class Current: 600/509
• CPC Class Codes: A61B 5/02028 Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction
• CPC Class Codes: A61B 5/0245 by using sensing means generating electric signals, i.e. ECG signals
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/339 Displays specially adapted therefor
• CPC Class Codes: A61B 5/341 Vectorcardiography [VCG]
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/35 by template matching
• CPC Class Codes: A61B 5/361 Detecting fibrillation
• CPC Class Codes: A61B 5/363 Detecting tachycardia or bradycardia
• CPC Class Codes: A61B 5/4848 Monitoring or testing the effects of treatment, e.g. of medication
• CPC Class Codes: A61B 5/4884 inducing physiological or psychological stress, e.g. applications for stress testing
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7232 involving compression of the physiological signal, e.g. to extend the signal recording period
• CPC Class Codes: A61B 5/7239 using differentiation including higher order derivatives
• CPC Class Codes: A61B 5/7253 characterised by using transforms
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: G06F 2218/04 Denoising
• CPC Class Codes: G06F 2218/12 Classification; Matching
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

A method of evaluating an electrophysiological signal is disclosed. A mathematical reconstruction over at least one cycle of the electrophysiological signal is used to identify an abnormal substrate. A non-transitory computer readable medium is also disclosed. The nontransitory computer readable medium has stored thereon instructions for identifying a pathological substrate from a mathematical reconstruction of an electrophysiological signal, which, when executed by a processor, causes the processor to perform steps comprising using a mathematical reconstruction over many cycles of the electrophysiological signal to identify a pathological state. A system for evaluating an electrophysiological signal includes a processor configured to identify a pathological condition from a mathematical reconstruction of the electrophysiological signal. The system also includes a data input coupled to the processor and configured to provide the processor with the electrophysiological signal. The system further includes a user interface coupled to either the processor or the data input.

Claims

1. A method of evaluating an electrophysiological signal, comprising:

receiving an electrophysiological signal;

applying, using a processor of a computing device, a model-derived reconstruction using a summation series of complex exponentials over at least one cycle of the electrophysiological signal to identify a pathological substrate, the identification of the pathological substrate being based on a relative large or absolute value of the irrational fractional subspace derivative produced from the model-derived reconstruction of the electrophysiological signal; and

displaying, on a user interface, one or more indicators of the electrophysiological signal to represent at least a portion of the electrophysiological signal and the pathological condition of the substrate and predict the risk for adverse clinical outcomes.

2. The method of claim 1, wherein the complex exponentials of the model-derived reconstruction of the electrophysiological signal comprises an input/output (I/O) expansion of the electrophysiological signal, in which at least one of the terms of the I/O expansion are fractionally differentiable and integrable.

3. The method of claim 2, wherein at least one term of the I/O expansion is Irrationally fractionally differentiable, and wherein the at least one term of the I/O expansion is analytically irrationally fractionally differentiable.

4. The method of claim 2, wherein the I/O expansion comprises a fractional derivative of order α

that can be any real or complex number of the model-derived reconstruction.

5. The method of claim 1, wherein the accumulated absolute value of the irrational fractional subspace derivatives value indicate instability or arrhythmogenic potential, wherein the instability or arrhythmogenic potential is associated to a status of cardiac health including the risk of sudden death.

6. The method of claim 1, wherein the model-derived reconstruction which can include it'"'"'s isolated subspaces of the electrophysiological signals over at least 100 seconds is transformed using delayed phase space reconstructions to increase the number of phase space dimensions to six or higher to extract informative high dimensional space time metrics,

wherein cloud signal densities (SD), in high dimensional space, are a mixture of Gaussian sinusoidal functions that radiate from the moment center to the outer boundary of a manifold to allow for mixtures of Gaussian sinusoidal distributions to form complex decision boundaries that are used to predict a risk for adverse clinical outcomes, and

wherein the Gaussian sinusoidal distributions are associated to a status of cardiac health including the risk of sudden death.

7. The method of claim 1, wherein the pathological substrate is selected from the group consisting of:

a heart disease, a cardiac arrhythmia;

a diabetic autonomic neuropathy;

Parkinson'"'"'s disease;

a form of epilepsy;

a brain injury;

an altered state of cognition;

a stability of a heart at different heart rates;

an effectiveness of a medication;

a measure of a toxicity of medication;

an ischemia;

a silent ischemia;

a congenital heart defect;

an atrial fibrillation;

a ventricular fibrillation;

a ventricular tachycardia; and

a blood vessel blockage.

8. The method of claim 1, further comprising displaying on a user interface one or more indicators based on an input/output (I/O) expansion to represent at least a portion of the electrophysiological signal.

9. The method of claim 8, wherein the one or more indicators are displayed on one of a phase space plot and at least a portion of the electrophysiological signal.

10. The method of claim 1, wherein the one or more indicators are highlighted to further convey information about the pathological substrate identified from the complex exponential model-derived reconstruction of the electrophysiological signal.

11. The method of claim 1, wherein the highlighted indicators comprise at least one of color-coded indicators, shaded indicators, one or more types of a broken line, and one or more types of line thickness.

12. The method of claim 1, wherein the one or more indicators are displayed on a physiological image corresponding to the electrophysiological signal.

13. The method of claim 12, wherein the electrophysiological signal comprises an electrocardiogram (ECG) and the physiological image comprises an image of at least a portion of a heart.

14. The method of claim 12, wherein the electrophysiological signal comprises an electrocardiogram (ECG) and the physiological image comprises an image of at least a portion of a perfusion bed or the coronary artery circulation.

15. The method of claim 12, wherein the physiological image comprises a tissue image from a subject from whom the electrophysiological signal was obtained.