Rhythm discriminator with immunity to body posture

• US 9,924,885 B2
• Filed: 07/24/2014
• Issued: 03/27/2018
• Est. Priority Date: 07/24/2014
• Status: Active Grant

• Current Assignee: Medtronic Incorporated (Medtronic Plc)
• Original Assignee: Medtronic Incorporated (Medtronic Plc)
• Inventors: Stadler, Robert W., Zhang, Xusheng
• Primary Examiner(s): Gedeon, Brian T
• Application Number:
  US14/339,789
• Publication Number:
  US 20160022166A1
• Time in Patent Office: 1,342 Days
• Field of Search: None
• CPC Class Codes: A61B 5/1116 Determining posture transitions
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/35 by template matching
• CPC Class Codes: A61B 5/363 Detecting tachycardia or bradycardia
• 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 5/7207 of noise induced by motion artifacts
• CPC Class Codes: A61N 1/3621 for treating or preventing abnormally high heart rate
• CPC Class Codes: A61N 1/3622 comprising two or more electrodes co-operating with different heart regions
• CPC Class Codes: A61N 1/3624 occurring in the atrium, i.e. atrial tachycardia
• CPC Class Codes: A61N 1/3627 for treating a mechanical deficiency of the heart, e.g. congestive heart failure or cardiomyopathy
• CPC Class Codes: A61N 1/36507 controlled by gradient or slope of the heart potential
• CPC Class Codes: A61N 1/36535 controlled by body position or posture
• CPC Class Codes: A61N 1/36542 controlled by body motion, e.g. acceleration
• CPC Class Codes: A61N 1/3702 Physiological parameters A61N1/365 takes precedence; evoked response A61N1/371

Abstract

An implantable medical device system includes an implantable cardioverter defibrillator (ICD) for detecting and treating ventricular tachycardia (VT). The ICD includes a sensing module for sensing a cardiac signal from available cardiac signal sensing vectors. A control module generates morphology templates of the cardiac signals for multiple patient postures for each of the available sensing vectors and determines a set of posture-independent template features. An unknown cardiac rhythm is classified in response to comparing features of a cardiac signal received during the unknown rhythm to the set of posture-independent features.

Claims

1. A method performed by an implantable medical device, comprising:

sensing a first cardiac signal during a known cardiac rhythm from each of a plurality of available sensing vectors,

for each of the plurality of available sensing vectors, generating a plurality of morphology templates of the first cardiac signal for each of a plurality of patient postures;

determining a set of template features from each of the plurality of morphology templates;

for each of the plurality of available sensing vectors, comparing the set of template features from one of the plurality of morphology templates corresponding to one of the plurality of postures to each of the sets of template features from all of the other morphology templates corresponding to all of the other of the plurality of postures;

for each of the plurality of available sensing vectors, storing a set of posture-independent template features in response to the comparing;

sensing a second cardiac signal during an unknown cardiac rhythm from at least one of the plurality of available sensing vectors;

determining features from the second cardiac signal that are analogous to the set of posture-independent template features stored for the at least one of the plurality of available sensing vectors;

comparing the features determined from the second cardiac signal to the analogous set of posture-independent template features; and

classifying the unknown cardiac rhythm in response to comparing the features determined from the second cardiac signal to the analogous set of posture-independent template features.

2. The method of claim 1, wherein classifying the unknown cardiac rhythm comprises:

selecting at least two of the available sensing vectors;

classifying each of the at least two of the available sensing vectors as one of ventricular tachycardia and supraventricular tachycardia in response to the comparing the features determined from the second cardiac signal to the set of posture-independent templates for the respective sensing vector; and

classifying the unknown cardiac rhythm as supraventricular tachycardia in response to at least one of the selected sensing vectors being classified as supraventricular tachycardia.

3. The method of claim 1, further comprising:

producing a supraventricular tachycardia classification region of an n-dimensional space defined by the set of posture-independent features for a respective one of the available sensing vectors;

wherein comparing the features determined from the cardiac signal to the set of posture-independent templates comprises determining if the features define a point within the supraventricular tachycardia classification region.

4. The method of claim 1, further comprising:

determining each set of posture-independent features as a posture-independent set of coordinates in an n-dimensional space, the set of coordinates comprising a value of each posture-independent feature included in a respective set of posture-independent features, wherein the n-dimensional space comprises a dimension corresponding to each posture-independent feature included in the set of posture-independent features;

determining the features from the second cardiac signal as a cardiac signal set of coordinates in the n-dimensional space;

determining a distance between the cardiac signal set of coordinates and the posture-independent set of coordinates; and

classifying the unknown cardiac rhythm based on the determined distance.

5. The method of claim 4, further comprising comparing the distance to a supraventricular tachycardia classification threshold and classifying the unknown cardiac rhythm as supraventricular tachycardia in response to the distance being less than the threshold.

6. The method of claim 1, further comprising:

determining features from each one of a plurality of cardiac cycles from the second cardiac signal that are analogous to the set of posture-independent template features stored for the at least one of the plurality of available sensing vectors;

comparing the features determined from each one of the plurality of cardiac cycles to the analogous set of posture-independent template features; and

classifying the at least one of the plurality of available sensing vectors in response to a threshold number of the plurality of cardiac cycles matching the analogous set of posture-independent template features; and

classifying the unknown cardiac rhythm based on the classification of the at least one of the plurality of available sensing vectors.

7. The method of claim 1, wherein determining the features of the second cardiac signal comprises:

determining an average cardiac cycle signal from a plurality of cardiac cycles of the cardiac signal; and

determining the features of the second cardiac signal from the averaged cardiac cycle.

8. The method of claim 1, wherein the classifying comprises classifying the unknown cardiac rhythm as one of supraventricular tachycardia and ventricular tachycardia.

9. The method of claim 8, further comprising classifying the unknown cardiac rhythm as a supraventricular tachycardia in response to at least one feature of the second cardiac signal matching an analogous feature of the set of posture-independent features within a predetermined match threshold.

10. The method of claim 8, further comprising withholding a ventricular tachycardia therapy in response to classifying the unknown cardiac rhythm as a supraventricular tachycardia.