SIGNAL QUALITY MONITORING FOR MULTIPLE SENSE VECTORS IN CARDIAC DEVICES

• US 20170113053A1
• Filed: 10/19/2016
• Published: 04/27/2017
• Est. Priority Date: 10/23/2015
• Status: Active Application

• Current Assignee: Cardiac Pacemakers Incorporated (Boston Scientific Corporation)
• Original Assignee: Cardiac Pacemakers Incorporated (Boston Scientific Corporation)
• Inventors: Brisben, Amy Jean, Allavatam, Venugopal, Siejko, Krzysztof Z., Mahajan, Deepa, Wika, Kevin G., Herrmann, Keith L., Hahn, Stephen J.
• Granted Patent: US 10,264,989 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/0245 by using sensing means generating electric signals, i.e. ECG signals
• CPC Class Codes: A61B 5/283 Invasive
• 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 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/327 Generation of artificial ECG signals based on measured signals, e.g. to compensate for missing leads
• 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 5/352 Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
• CPC Class Codes: A61B 5/364 Detecting abnormal ECG interval, e.g. extrasystoles, ectopic heartbeats
• CPC Class Codes: A61B 5/366 Detecting abnormal QRS complex, e.g. widening
• CPC Class Codes: A61B 5/686 Permanently implanted devices, e.g. pacemakers, other stimulators, biochips A61B5/6861 takes precedence
• CPC Class Codes: A61B 5/6869 Heart
• CPC Class Codes: A61B 5/72 Signal processing specially adapted for physiological signals or for diagnostic purposes character recognition, recognising digital ink, document-oriented image-based pattern recognition G06V30/00; recognition or understanding of postal images G06V30/424
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• CPC Class Codes: A61N 1/046 Specially adapted for shock therapy, e.g. defibrillation
• CPC Class Codes: A61N 1/05 for implantation or insertion into the body, e.g. heart electrode A61N1/06 takes precedence
• CPC Class Codes: A61N 1/0563 : specially adapted for defibrillation or cardioversion
• CPC Class Codes: A61N 1/3622 : comprising two or more electrodes co-operating with different heart regions
• CPC Class Codes: A61N 1/3625 : External stimulators
• CPC Class Codes: A61N 1/36514 : controlled by a physiological quantity other than heart potential, e.g. blood pressure controlled by two or more physical parameters A61N1/36585
• 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

Abstract

New and alternative approaches to the monitoring of cardiac signal quality for external and/or implantable cardiac devices. In one example, signal quality is monitored continuously or in response to a triggering event or condition and, upon identification of a reduction in signal quality, a device may reconfigure its sensing state. In another example, one or more trends of signal quality are monitored by a device, either continuously or in response to a triggering event or condition, and sensing reconfiguration may be performed in response to identified trends and events. In yet another example, a device may use a looping data capture mode to track sensing data in multiple vectors while primarily relying on less than all sensing vectors to make decisions and, in response to a triggering event or condition, the looped data can be analyzed automatically, without waiting for additional data capture to reconfigure sensing upon identification of the triggering event or condition. In another example a device calculates a composite cardiac cycle by overlaying signal morphology for a number of cardiac cycles and analyzes the composite cardiac cycle to calculate signal quality metrics.

Claims

1. A cardiac rhythm management device for use with a patient having a plurality of electrodes coupled to sensing circuitry to allow a plurality of sensing configurations to be defined thereby and operational circuitry configured to perform the following:

detect cardiac cycles of the patient using a first sensing configuration;

identify a triggering event for sensing reconfiguration analysis;

analyze sensing quality data comprising data from two or more cardiac cycles of the patient for one or more sensing vectors, in which a sensing vector is defined between at least two of the plurality of electrodes;

identify a trend indicating one or more unfavorable sensing configurations;

select a sensing reconfiguration which is not one which has been identified as unfavorable in light of the trend; and

applying the sensing reconfiguration to detect and analyze cardiac signal data.

2. The device of claim 1 wherein operational circuitry is configured to identify the trend indicating one or more unfavorable sensing configurations by:

capturing data within sensing windows defined for several of the detected cardiac cycles;

calculating a composite variability factor by analysis of the data captured within each sensing window; and

finding that the variability factor indicates relatively high variability for one or more sensing configurations.

3. The device of claim 2 wherein each sensing window comprises a plurality of sample points and the variability factor is determined by calculating one or more of a variance, a standard deviation, or a range of detected signal amplitudes on a sample by sample basis within the sensing windows.

4. The device of claim 1 wherein the operational circuitry is configured to identify the trend indicating one or more unfavorable sensing configurations by:

detecting a plurality of cardiac cycles of the patient;

calculating a signal quality metric for each cardiac cycle;

observing variability of the signal quality metric for the plurality of cardiac cycles; and

finding high variability of the signal quality metric.

5. The device of claim 1 wherein the operational circuitry is configured to identify the trend indicating one or more unfavorable sensing configurations by:

detecting a plurality of cardiac cycles of the patient;

analyzing the plurality of cardiac cycles to identify one or more of noise or overdetection;

calculating a frequency with which one or more of noise or overdetection occurs; and

finding a high frequency of occurrence of noise or overdetection.

6. The device of claim 1 wherein the operational circuitry is configured to identify the trend indicating one or more unfavorable sensing configurations by:

detecting a plurality of cardiac cycles of the patient;

analyzing the plurality of cardiac cycles to identify one or more of noise or overdetection;

calculating a frequency with which one or more of noise or overdetection occurs as a function of time; and

finding that the frequency of occurrence of noise or overdetection is increasing with time.

7. The device of claim 1 wherein the operational circuitry is configured to identify the trend indicating one or more unfavorable sensing configurations by:

detecting a plurality of cardiac cycles of the patient with each of first and second sensing configurations;

calculating stability of a signal quality metric for each of the first and second sensing configurations over time; and

identifying whichever of the first and second sensing configurations has less stability of the signal quality metric.

8. The device claim 1 wherein the device is a wearable cardiac rhythm management device.

9. The device of claim 1 wherein the device is an implantable cardiac rhythm management device adapted to deliver therapy.

10. The device of claim 1 wherein the device is an implantable cardiac monitoring device.

11. A cardiac rhythm management device having a plurality of sensing electrodes defining at least first and second sensing vectors coupled to operational circuitry configured to select a default sensing vector from among the at least first and second sensing vectors, wherein the operational circuitry is configured to perform the following:

analyzing a cardiac signal captured using the default sensing vector to determine whether an arrhythmia appears to be present with one or more sensing vectors that are not the default sensing vector disabled;

determining that an arrhythmia is likely occurring;

enabling one or more of the disabled sensing vectors to capture data; and

storing data captured from each of the default sensing vector and the one or more enabled sensing vectors for use in later diagnostic work.

12. A cardiac rhythm management device having a plurality of sensing electrodes defining at least first and second sensing vectors coupled to operational circuitry configured to select a default sensing vector from among the at least first and second sensing vectors by:

the operational circuitry identifying a current state of one or more signal quality metric for at least each of the first and second sensing vectors;

the operational circuitry analyzing historical data for each of the first and second sensing vectors to identify a long term trend of one or more signal quality trend metrics;

the operational circuitry analyzing historical data for at least one of the first and second sensing vectors to identify any clinical hazards; and

the operational circuitry combining the current state, long term trend, and clinical hazards to select the default sensing vector.

13. The device of claim 12 wherein the operational circuitry is configured to identify the trend by:

capturing data within sensing windows defined for several of the detected cardiac cycles;

calculating a composite variability factor by analysis of the data captured within each sensing window; and

determining whether the variability factor indicates relatively high variability for one or more sensing configurations.

14. The device of claim 13 wherein each sensing window comprises a plurality of sample points and the variability factor is determined by calculating one or more of a variance, a standard deviation, or a range of detected signal amplitudes on a sample by sample basis within the sensing windows.

15. The device of claim 12 wherein the operational circuitry is configured to identify the trend by:

detecting a plurality of cardiac cycles of the patient;

calculating a signal quality metric for each cardiac cycle;

observing variability of the signal quality metric for the plurality of cardiac cycles; and

assessing whether the signal quality metric is subject to high variability.

16. The device of claim 12 wherein the operational circuitry is configured to identify the trend by:

detecting a plurality of cardiac cycles of the patient;

analyzing the plurality of cardiac cycles to identify one or more of noise or overdetection;

calculating a frequency with which one or more of noise or overdetection occurs; and

finding whether there is a high frequency of occurrence of noise or overdetection.

17. The device of claim 12 wherein the operational circuitry is configured to identify the trend by:

detecting a plurality of cardiac cycles of the patient;

analyzing the plurality of cardiac cycles to identify one or more of noise or overdetection;

calculating a frequency with which one or more of noise or overdetection occurs as a function of time; and

finding whether the frequency of occurrence of noise or overdetection is increasing with time.

18. The device of claim 12 wherein the operational circuitry is configured to identify the trend by:

detecting a plurality of cardiac cycles of the patient with each of first and second sensing configurations;

calculating stability of a signal quality metric for each of the first and second sensing configurations over time; and

determining whichever of the first and second sensing configurations has less stability of the signal quality metric.

19. The device claim 12 wherein the device is a wearable cardiac rhythm management device.

20. The device of claim 12 wherein the device is an implantable cardiac rhythm management device.