Calculation of self-correlation in an implantable cardiac device

• US 9,451,893 B2
• Filed: 08/06/2015
• Issued: 09/27/2016
• Est. Priority Date: 08/18/2014
• Status: Active Grant

• Current Assignee: Cameron Incorporated (Schlumberger NV)
• Original Assignee: Cameron Incorporated (Schlumberger NV)
• Inventors: Siejko, Krzysztof Z., Huelskamp, Paul
• Primary Examiner(s): Gedeon, Brian T
• Application Number:
  US14/819,889
• Publication Number:
  US 20160045136A1
• Time in Patent Office: 418 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: A61B 5/0245 by using sensing means generating electric signals, i.e. ECG signals
• CPC Class Codes: A61B 5/287 Holders for multiple electrodes, e.g. electrode catheters for electrophysiological study [EPS]
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/341 Vectorcardiography [VCG]
• CPC Class Codes: A61B 5/35 by template matching
• CPC Class Codes: A61B 5/352 Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
• CPC Class Codes: A61B 5/361 Detecting fibrillation
• CPC Class Codes: A61B 5/363 Detecting tachycardia or bradycardia
• CPC Class Codes: A61B 5/4836 Diagnosis combined with treatment in closed-loop systems or methods A61B5/0036 takes precedence
• CPC Class Codes: A61B 5/686 Permanently implanted devices, e.g. pacemakers, other stimulators, biochips A61B5/6861 takes precedence
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61N 1/3704 Circuits specially adapted therefor, e.g. for sensitivity control
• CPC Class Codes: A61N 1/3925 Monitoring; Protecting
• CPC Class Codes: A61N 1/3956 Implantable devices for applying electric shocks to the heart, e.g. for cardioversion
• CPC Class Codes: A61N 1/3987 characterised by the timing or triggering of the shock
• CPC Class Codes: G06F 17/11 for solving equations , e.g. nonlinear equations, general mathematical optimization problems optimization specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06F 17/16 Matrix or vector computation , e.g. matrix-matrix or matrix-vector multiplication, matrix factorization matrix transposition G06F7/78
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 40/67 for remote operation

Abstract

Self-correlation enhancements and implementations are described. In particular, certain examples demonstrate the analytical tools to reduce the computational burden of generating a self-correlation function within an implantable medical device. Peak selector and tracking analysis are also included as secondary elements for identifying and generating confidence in rate estimates based on the self-correlation function. The approach may enable an alternative calculation of cardiac rate in an implantable medical device as a stand-alone rate detector or as a double-check of other rate calculations.

Claims

1. A method of iterative analysis of cardiac signals comprising:

sensing cardiac signals from a plurality of electrodes;

generating a self-correlation function from the sensed cardiac signals, the self-correlation function having amplitudes as a function of lag depth; and

determining a first estimate of cardiac rate using the self-correlation function;

wherein the step of generating the self-correlation function includes;

selecting a first data set from the cardiac signal having a first length, N, and a second data set from the cardiac signal having a second length approximately twice the length of the first length, wherein the second data set includes the first data set;

repeatedly subtracting the first data set from the second data set at a series of lag depths from approximately zero to approximately N, to generate the self-correlation function.

2. A method as in claim 1 further comprising identifying amplitude peaks in the self-correlation function and finding a first estimate of cardiac rate.

3. A method as in claim 2 wherein the step of generating the self-correlation function includes normalizing results of the repeated subtracting step.

4. A method as in claim 3 wherein the step of determining a first estimate of cardiac rate includes identifying a peak in the self-correlation having a first lag depth, wherein the peak is analytically determined to be likely to indicate reliably high correlation of the first data set to the second data set at the first lag depth, and calculating the cardiac rate using the first lag depth.

5. A method comprising:

performing the method of claim 4 at a first time;

repeating the method of claim 4 at a second time;

comparing the first lag depth as identified at the first time to the first lag depth as identified at the second time; and

if the comparing step finds similarity of the first lag depths, determining that a reliable rate is likely.

6. A method as in claim 1 wherein the first data set comprises data from the sensed cardiac signals covering approximately two seconds of data, and the second data set comprises data from the sensed cardiac signals covering approximately four seconds of data.

7. A method as in claim 1 wherein the first data set comprises data from the sensed cardiac signals covering approximately one second of data, and the second data set comprises data from the sensed cardiac signals covering approximately two seconds of data.

8. A method as in claim 1 further comprising determining whether cardiac therapy is needed using at least the first estimate of cardiac rate and, if so, delivering cardiac therapy.

9. A method as in claim 1 further comprising:

detecting R-waves in order to identify a second estimate of cardiac rate therefrom; and

selecting from between the first estimate of cardiac rate and the second estimate of cardiac rate to yield a final estimate of cardiac rate.

10. An implantable cardiac system comprising a canister housing operational circuitry, the operational circuitry being configured for use with a plurality of electrodes, the operational circuitry being configured to operate as follows:

sensing cardiac signals by receiving electrical signals from the plurality of electrodes;

generating a self-correlation function from the sensed cardiac signals, the self-correlation function having amplitudes as a function of lag depth, by;

selecting a first data set from the cardiac signal having a first length, N, and a second data set from the cardiac signal having a second length approximately twice the length of the first length, wherein the second data set includes the first data set;

repeatedly subtracting the first data set from the second data set at a series of lag depths from approximately zero to approximately N, to generate the self-correlation function; and

determining a first estimate of cardiac rate using the self-correlation function.

11. A system as in claim 10 wherein the operational circuitry is configured such that determining a first estimate of cardiac rate using the self-correlation function includes identifying amplitude peaks in the self-correlation function and finding a first estimate of cardiac rate.

12. A system as in claim 11 wherein the operational circuitry is configured such that generating the self-correlation function includes normalizing results of the repeated subtracting step.

13. A system as in claim 12 wherein the operational circuitry is configured such that determining a first estimate of cardiac rate includes identifying a peak in the self-correlation having a first lag depth, wherein the peak is analytically determined to be likely to indicate reliably high correlation of the first data set to the second data set at the first lag depth, and calculating the cardiac rate using the first lag depth.

14. A system as in claim 13 wherein the operational circuitry is configured to generate at least first and second estimates of cardiac rate at first and second times using first and second lag depths, to compare the first lag depth and the second lag depth and determine that a reliable cardiac rate calculation has occurred if the first and second lag depths are similar.

15. A system as in claim 10 wherein the operational circuitry is configured such that the first data set comprises data from the sensed cardiac signals covering approximately two seconds of data, and the second data set comprises data from the sensed cardiac signals covering approximately four seconds of data.

16. A system as in claim 10 wherein the operational circuitry is configured such that the first data set comprises data from the sensed cardiac signals covering approximately one second of data, and the second data set comprises data from the sensed cardiac signals covering approximately two seconds of data.

17. A system as in claim 10 wherein the operational circuitry comprises therapy delivery circuitry for generating an electrical stimulus output, and the operational circuitry is further configured to determine whether cardiac therapy is needed using at least the first estimate of cardiac rate and, if so, to deliver cardiac therapy using the therapy delivery circuitry.

18. A system as in claim 10 wherein the operational circuitry is configured to:

detect R-waves in the sensed cardiac signals to calculate a second estimate of cardiac rate therefrom; and

select from between the first estimate of cardiac rate and the second estimate of cardiac rate to yield a final estimate of cardiac rate.

19. A system as in claim 18 wherein the operational circuitry comprises therapy delivery circuitry for generating an electrical stimulus output, and the operational circuitry is further configured to determine whether cardiac therapy is needed using at least the first estimate of cardiac rate and, if so, to deliver cardiac therapy using the therapy delivery circuitry.

20. An implantable medical device comprising sensing circuitry and a processor and a non-transitory medium with instructions contained therein for implementation by the processor, the processor configured to operate on the instructions to use the sensing circuitry and therapy output circuitry as follows:

sensing cardiac signals with the sensing circuitry;

generating a self-correlation function from the sensed cardiac signals, the self-correlation function having amplitudes as a function of lag depth, by;

selecting a first data set from the cardiac signal having a first length, N, and a second data set from the cardiac signal having a second length approximately twice the length of the first length, wherein the second data set includes the first data set;

repeatedly subtracting the first data set from the second data set at a series of lag depths from approximately zero to approximately N, to generate the self-correlation function; and

determining a first estimate of cardiac rate using the self-correlation function.