Computerized Systems and Methods for Stability-Theoretic Prediction and Prevention of Sudden Cardiac Death

• US 20110190650A1
• Filed: 12/30/2010
• Published: 08/04/2011
• Est. Priority Date: 12/31/2009
• Status: Active Grant

• Current Assignee: Cerner Innovation, Inc. (Oracle Corporation)
• Original Assignee: Cerner Innovation, Inc. (Oracle Corporation)
• Inventors: McNair, Douglas S.
• Granted Patent: US 9,585,589 B2
• Time in Patent Office: Days
• US Class Current: 600/518
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/00 Measuring for diagnostic purposes radiation diagnosis A61B6/00; diagnosis by ultrasonic, sonic or infrasonic waves A61B8/00; Identification of persons
• CPC Class Codes: A61B 5/0004 characterised by the type of physiological signal transmitted
• CPC Class Codes: A61B 5/1117 Fall detection
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/6831 Straps, bands or harnesses
• CPC Class Codes: A61B 5/6891 Furniture
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 involving training the classification device
• 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: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/7405 using sound
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: A61B 5/747 in case of emergency, i.e. alerting emergency services
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

Systems, methods and computer-readable media are provided for automatic identification of patients according to near-term risk of ventricular arrhythmias and sudden cardiac death (SCD). Embodiments of the invention are directed to event prediction, risk stratification, and optimization of the assessment, communication, and decision-making to prevent SCD, and in one embodiment take the form of a platform for wearable, mobile, unteathered monitoring devices with embedded decision support. Thus embodiments relate to automatically identifying persons at risk for arrhythmias and SCD through the use of noninvasive, portable, wearable electronic device and sensors equipped with signal-processing software and statistical predictive algorithms that calculate stability-theoretic measures derived from the digital electrocardiogram timeseries acquired by the device. The measurements and predictive algorithms embedded within the device provide for unsupervised use in the home or in general acute-care and chronic-care venues and afford a degree of robustness against variations in individual anatomy and sensor placement.

Claims

1. Computer-readable media having computer-executable instructions embodied thereon that when executed, facilitate a method for automatically predicting ventricular arrhythmias in an individual that are likely to result in sudden cardiac death (SCD), the method comprising:

obtaining signals representative of electrical activity of the heart of said individual;

determining, utilizing an objective function, a QT dispersion stability index (QTdSI) from said signals; and

determining difference between the index and a reference value to detect the presence of instability of QT interval dispersion or other measurements in said signals,

wherein a significant difference is indicative of an increased risk of said individual of sudden cardiac death (SCD).

2. The computer-readable media of claim 1, wherein the objective function evaluates digitized electrocardiographic waveforms from one or a plurality of previous time intervals to classify a likelihood of a cascade of events leading to SCD within a future time interval.

3. The computer-readable media of claim 1, wherein the objective function comprises a timeseries calculated from serially-acquired waveform data embodying a Lyapunov exponent.

4. The computer-readable media of claim 3, wherein the Lyapunov exponent is evaluated on one or a plurality of ECG or other physiologic variables as functions of time.

5. The computer-readable media of claim 4, wherein the results of the objective function are used by a decision-support algorithm to determine a quantitative risk for SCD.

6. The computer-readable media of claim 5, wherein the decision-support algorithm comprises at least one of a decision-tree, a suite of ‘

if-then’

production rules and an inference engine, a support vector machine (SVM), a logistic regression equation, an automated neural network, or a statistical predictive algorithm.

7. Computer-readable media having computer-executable instructions embodied thereon that when executed, facilitate a method for determining a QT dispersion stability index for an individual, the method comprising:

identifying QRS and T wave morphology for isochronic points between substantially-consecutive T waves in signals representative of electrical activity of the heart of the individual; and

determining a QT dispersion stability index as a function of said morphology.

8. The computer-readable media of claim 7, wherein said determining a QT dispersion stability index as a function of said morphology further comprises eliminating ectopic beats and sinus beats preceding and following said ectopic beats and calculating the maximal value of root-mean-square differences for isochronic points of a repolarization interval between pairs of substantially-consecutive T waves.

9. The computer-readable media of claim 8, wherein said ectopic beats comprise a ventricular premature contraction or an atrial premature contraction.

10. The computer-readable media of claim 8, wherein the method further comprises filtering said signal prior to said calculating.

11. The computer-readable media of claim 8, wherein said determining a QT dispersion stability index as a function of said ECG morphology further comprises removing baseline fluctuation from said signal prior to said calculating.

12. The computer-readable media of claim 8, wherein said determining a QT dispersion stability index as a function of said ECG morphology further comprises normalizing said maximal value of root-mean-square differences to an absolute magnitude of the signal-averaged QRS complex after said calculating.

13. The computer-readable media of claim 8, wherein said determining a QT dispersion stability index as a function of said ECG morphology further comprises calculating and storing Lyapunov exponents after said calculating.

14. A method for automatically predicting ventricular arrhythmias in an individual that are likely to result in sudden cardiac death (SCD), the method comprising:

obtaining ECG signals representative of electrical activity of the heart of said individual; and

determining, utilizing an objective function, a QT dispersion stability index (QTdSI) from said signals,

determining difference between the index and a reference value to detect the presence of instability of QT interval dispersion or other measurements in said signals;

wherein a significant difference is indicative of an increased risk of said individual of SCD.

15. The method of claim 14, wherein the objective function evaluates digitized electrocardiographic waveforms from one or a plurality of previous time intervals to classify a likelihood of a cascade of events leading to SCD within a future time interval.

16. The method of claim 14, wherein the objective function comprises a timeseries calculated from serially-acquired waveform data embodying a Lyapunov exponent of one or a plurality of ECG or other physiologic variables as functions of time.

18. The method of claim 14, wherein the results of the objective function are used by a decision-support algorithm to determine a quantitative risk for SCD.

19. The method of claim 18, wherein the decision-support algorithm comprises at least one of a decision-tree, a suite of ‘

if-then’

production rules and an inference engine, a support vector machine (SVM), a logistic regression equation, an automated neural network, or a statistical predictive algorithm.

20. The method of claim 14, wherein the method further comprises providing a notification to a health care provider when said increased risk for SCD is indicated.