Method and system for evaluating cardiac ischemia

• US 20060052717A1
• Filed: 08/31/2005
• Published: 03/09/2006
• Est. Priority Date: 08/31/2004
• Status: Abandoned Application

• Current Assignee: University of Akron
• Original Assignee: University of Akron
• Inventors: Acharya, Soumyadipta, Mugler, Dale H., Gopalakrishnan, Raghavan
• Application Number:
  US11/216,235
• Publication Number:
  US 20060052717A1
• Time in Patent Office: Days
• US Class Current: 600/509
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/7267 involving training the classification device

Abstract

The present invention relates to methods and systems for evaluating abnormalities in electrocardiograms (ECGs), including abnormalities associates with cardiac ischemia. More particularly, the present invention relates to an automated system and method for interpreting any abnormalities present in an electrocardiogram (ECG), including those abnormalities associated with cardiac ischemia. In one embodiment, the present invention relates to a method for monitoring abnormalities in an ECG, the method comprising the steps of: (a) gathering at least one ECG; (b) subjecting the at least one ECG to a QRS detection algorithm in order to scan for R-peak location; (c) calculating the Hermite coefficients corresponding to the individual ECG complexes from each individual ECG; and (d) subjecting the Hermite coefficients to a Neural Network in order to determine the present and/or absence of ECG abnormalities.

Claims

1. A method for monitoring/detecting abnormalities in an ECG, the method comprising the steps of:

(a) gathering at least one ECG;

(b) subjecting the at least one ECG to a QRS detection algorithm in order to scan for R-peak location;

(c) calculating the Hermite coefficients corresponding to the individual ECG complexes from each individual ECG; and

(d) subjecting the Hermite coefficients to a Neural Network in order to determine the present and/or absence of ECG abnormalities.

3. The method of claim 1, wherein the Hermite coefficients of Step (c) are calculated by a simple dot product.

4. The method of claim 1, wherein the ECG abnormalities being monitored are associated with cardiac ischemia.

5. The method of claim 4, wherein the Neural Network outputs the presence or absence of ST segment changes, T wave changes, beat classification, ischemia, or a combination of one or more thereof.

6. A computer system designed to carry out the method of claim 1, the computer system comprising:

at least one power source;

at least one input device;

at least one display; and

at least one memory device,

wherein the computer system is designed to act as a Neural Network.

2. The method of claim 2, wherein Steps (b) and (c) are conducted simultaneously.

7. A method for monitoring/detecting abnormalities in an ECG, the method comprising the steps of:

(a) gathering at least one ECG;

(b) subjecting the at least one ECG to a QRS detection algorithm in order to scan for R-peak location;

(c) calculating the Hermite coefficients corresponding to the individual ECG complexes from each individual ECG; and

(d) subjecting the Hermite coefficients to a Neural Network in order to determine the present and/or absence of ECG abnormalities,

wherein the ECG abnormalities being monitored/detected are associated with cardiac ischemia, and wherein Steps (b) and (c) are conducted simultaneously.

8. The method of claim 7, wherein the Hermite coefficients of Step (c) are calculated by a simple dot product.

9. The method of claim 7, wherein the Neural Network outputs the presence or absence of ST segment changes, T wave changes, beat classification, ischemia, or a combination of one or more thereof.

10. A computer system designed to carry out the method of claim 7, the computer system comprising:

at least one power source;

at least one input device;

at least one display; and

at least one memory device,

wherein the computer system is designed to act as a Neural Network.