Peak selection for self correlation analysis of cardiac rate in an implantable medical devices

• US 9,629,565 B2
• Filed: 08/06/2015
• Issued: 04/25/2017
• 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.
• Primary Examiner(s): Gedeon, Brian T
• Application Number:
  US14/819,851
• Publication Number:
  US 20160045132A1
• Time in Patent Office: 628 Days
• Field of Search: None
• 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 use of a peak selector to identify peaks of a self-correlation function which serve as candidate cardiac rates for an implantable medical device. 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 analyzing cardiac signals in an implantable medical device having a plurality of electrodes for sensing cardiac signals coupled to operational circuitry for at least performing analysis of sensed cardiac signals, the method comprising:

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

identifying amplitude peaks in the self-correlation function and finding a first estimate of cardiac rate by;

identifying one or more candidate amplitude peaks each having lag depths;

selecting a first candidate peak having a first lag depth by choosing either;

a candidate peak having the least lag depth of the identified candidate peaks, or

a candidate peak having an amplitude that is larger than that of the candidate peak with the least lag depth by at least a first margin and which corresponds to a cardiac rate exceeding a rate threshold; and

applying a picket test to the first candidate peak by determining whether at least one additional peak appears at a second lag depth that is a multiple of the lag depth of the first candidate peak and, if so, finding that the picket test is passed for the first candidate peak.

2. The method of claim 1 wherein the first estimate of cardiac rate is generated by converting the first lag depth to a time interval and converting the time interval into a rate in response to finding that the picket test was passed by the first candidate peak.

3. The method of claim 1 further comprising:

finding that the picket test was not passed for the first candidate peak;

selecting a second candidate peak; and

applying the picket test to the second candidate peak.

4. The method of claim 1 further comprising:

finding that the picket test was not passed for at least the first candidate peak;

identifying a largest peak in the self-correlation function;

assessing whether the largest peak is larger than any other peak in the self-correlation function by at least a threshold amount;

finding that the largest peak is larger than any other peak by at least the threshold amount; and

using the lag depth of the largest peak to calculate a the first estimate of cardiac rate.

5. The method of claim 1 further comprising:

finding that the picket test was passed for the first candidate peak;

checking whether the self-correlation function includes a non-candidate peak having a lag depth less than that of the first candidate peak and an amplitude within a threshold of the first candidate peak; and

if so, determining whether the non-candidate peak passes the picket test.

6. The method of claim 1 further comprising:

performing R-wave detection on the sensed cardiac signals to generate a plurality of R-wave detections;

calculating a second estimate of cardiac rate using the R-wave detections; and

analyzing the first and second estimates of cardiac rate to determine whether a therapy is needed.

7. The method of claim 1 further comprising tracking the first estimate of cardiac rate over time to establish confidence measures if the first estimate of cardiac rate is consistent over either a period of time or a series of calculations.

8. The method of claim 1 further comprising generating a confidence in affiliation with the first estimate of cardiac rate as follows:

analyzing a largest peak in the self-correlation function to determine whether it is a dominant peak, if the picket test is not passed by at least the first candidate peak;

analyzing whether a non-candidate peak having an amplitude within similarity bounds of the first candidate peak and a lag depth less than that of the first candidate peak and associated with a heart rate above a tachy threshold appears in the self-correlation function and, if so, determining whether the non-candidate peak passes the picket test; and

generating the confidence affiliated with the first estimate of cardiac rate as follows;

if a candidate peak or non-candidate peak passes the picket test with at least two pickets, placing high confidence with the first estimate of cardiac rate;

if a dominant peak is found or if a candidate peak or non-candidate peak passes the picket test with only one picket, placing medium confidence with the first estimate of cardiac rate;

or

if neither a dominant peak nor a peak passing the picket test is found, reporting an estimated cardiac rate based on the largest peak in the self-correlation function and placing low confidence in the first estimate of cardiac rate;

wherein a peak passing the picket test with two peaks means that there are at least first and second additional peaks at multiples of the lag depth of the peak under analysis in the picket test.

9. The method of claim 8 further comprising:

performing R-wave detection on the sensed cardiac signals to generate a plurality of R-wave detections;

calculating a second estimate of cardiac rate using the R-wave detections; and

analyzing the first and second estimates of cardiac rate to determine whether a therapy is needed by;

treating the first estimate of cardiac rate as more reliable than the second estimate of cardiac rate if the first estimate comes with high confidence; and

treating the second estimate of cardiac rate as more reliable than the first estimate of cardiac rate if the first estimate comes with low confidence.

10. The method of claim 1 further comprising checking for a pattern of peaks in the self-correlation function consistent with Bigemini and, if so, replacing the candidate peak with a peak consistent with Bigemini.

11. The method of claim 1 further comprising checking for a pattern of peaks in the self-correlation function consistent with jitter and, if so, replacing the candidate peak with a peak consistent with jitter.

12. An implantable cardiac device comprising:

an implantable canister housing operational circuitry for performing cardiac signal analysis; and

a plurality of electrodes coupled to the operational circuitry to provide cardiac signals thereto;

wherein the operational circuitry is configured to perform a method of cardiac signal analysis comprising;

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

identifying amplitude peaks in the self-correlation function and finding a first estimate of cardiac rate by;

identifying one or more candidate amplitude peaks each having lag depths;

selecting a first candidate peak having a first lag depth by choosing either;

a candidate peak having the least lag depth of the identified candidate peaks, or

a candidate peak having an amplitude that is larger than that of the candidate peak with the least lag depth by at least a first margin and which corresponds to a cardiac rate exceeding a rate threshold; and

applying a picket test to the first candidate peak by determining whether at least one additional peak appears at a second lag depth that is a multiple of the lag depth of the first candidate peak and, if so, finding that the picket test is passed for the first candidate peak.

13. The implantable device of claim 12 wherein the operational circuitry is further configured such that the first estimate of cardiac rate is generated by converting the first lag depth to a time interval and converting the time interval into a rate in response to finding that the picket test was passed by the first candidate peak.

14. The implantable device of claim 12 wherein the operational circuitry is further configured for:

finding that the picket test was not passed for at least the first candidate peak;

identifying a largest peak in the self-correlation function;

assessing whether the largest peak is larger than any other peak in the self-correlation function by at least a threshold amount;

finding that the largest peak is larger than any other peak by at least the threshold amount; and

using the lag depth of the largest peak to calculate a the first estimate of cardiac rate.

15. The implantable device of claim 12 wherein the operational circuitry is further configured for:

performing R-wave detection on the sensed cardiac signals to generate a plurality of R-wave detections;

calculating a second estimate of cardiac rate using the R-wave detections; and

analyzing the first and second estimates of cardiac rate to determine whether a therapy is needed.

16. The implantable device of claim 12 wherein the operational circuitry is further configured for tracking the first estimate of cardiac rate over time to establish confidence measures if the first estimate of cardiac rate is consistent over either a period of time or a series of calculations.

17. The implantable device of claim 12 wherein the operational circuitry is further configured for checking for a pattern of peaks in the self-correlation function consistent with Bigemini and, if so, replacing the candidate peak with a peak consistent with Bigemini.

18. The implantable device of claim 12 wherein the operational circuitry is further configured for checking for a pattern of peaks in the self-correlation function consistent with jitter and, if so, replacing the candidate peak with a peak consistent with jitter.

19. 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:

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

identifying amplitude peaks in the self-correlation function and finding a first estimate of cardiac rate by;

identifying one or more candidate amplitude peaks each having lag depths;

selecting a first candidate peak having a first lag depth by choosing either;

a candidate peak having the least lag depth of the identified candidate peaks, or

a candidate peak having an amplitude that is larger than that of the candidate peak with the least lag depth by at least a first margin and which corresponds to a cardiac rate exceeding a rate threshold; and

applying a picket test to the first candidate peak by determining whether at least one additional peak appears at a second lag depth that is a multiple of the lag depth of the first candidate peak and, if so, finding that the picket test is passed for the first candidate peak.

20. The device of claim 19 wherein the processor is configured to operate on the instructions to further use the sensing circuitry and therapy output circuitry as follows:

performing R-wave detection on the sensed cardiac signals to generate a plurality of R-wave detections;

calculating a second estimate of cardiac rate using the R-wave detections; and

analyzing the first and second estimates of cardiac rate to determine whether a therapy is needed.