System and method to determine premature ventricular contraction (PVC) type and burden

Patent metadata

Field	Value
Publication number	US-9706938-B2
Application number	US-201514926458-A
Country	US
Kind code	B2
Filing date	Oct 29, 2015
Priority date	Oct 29, 2015
Publication date	Jul 18, 2017
Grant date	Jul 18, 2017

Abstract

A system and method of monitoring and reporting premature ventricular contractions (PVCs) detected in a patient is described. The method includes monitoring an electro-cardiogram (ECG) signal of the patient with an adherent device that includes a plurality of electrodes. Premature ventricular contraction (PVC) beats are detected based on the monitored ECG signal, and ECG episodes associated with detected PVC beats are stored for subsequent analysis. A morphology signal is calculated by quantifying the stored ECG episodes associated with detected PVC beats, wherein the morphology signal numerically represents the shape of the detected PVC beat. The PVC beats are then numerically clustered based on the calculated morphology signals to group PVC beats having a similar morphology or shape, and a report is generated based on the clustering of the PVC beats.

First claim

The invention claimed is:

1. A method of monitoring and reporting premature ventricular contractions (PVCs) detected in a patient, the method comprising:
monitoring an electro-cardiogram (ECG) signal of the patient with an adherent device that includes a plurality of electrodes;
detecting premature ventricular contraction (PVC) beats based on the monitored ECG signal, and storing ECG episodes associated with detected PVC beats;
calculating a morphology signal by quantifying the stored ECG episodes associated with detected PVC beats;
numerically clustering the PVC beats based on the calculated morphology signals to group PVC beats having a similar morphology;
generating an output that includes one or more of a total number of clusters generated, a count of the number of PVC beats included in each cluster, and a representative PVC beat from each cluster
wherein calculating the morphology signal includes calculating a wavelet signal decomposition of the ECG episode associated with each detected PVC beat, and wherein calculating the wavelet signal decomposition of the ECG episode includes utilizing a linear combination of sub-band signals defined by equation s morph =ybph+ybpm+ybpl, wherein ybph, ybpm, and ybpl are band-pass limited sub-bands that correspond to different QRS frequencies and s morph is the resulting morphology signal.

2. The method of claim 1 , wherein generating an output includes providing an indication of whether the PVC beats are unifocal or multifocal based on numerical clustering of the PVC beats.

3. The method of claim 1 , wherein numerically clustering the PVC beats includes utilizing a correlation distance metric to compare morphology signals and cluster PVC beats sharing a similar morphology.

4. The method of claim 1 , further including scaling the calculated morphology signals to a reference heart rate.

5. The method of claim 4 , wherein scaling includes contracting/dilating the calculated morphology based on a heart rate of the PVC beat and a reference heart rate.

6. A system for monitoring and reporting premature ventricular contractions (PVCs) detected with respect to a patient, the system comprising:
an adherent device that includes a plurality of electrodes and sensing circuitry for monitoring an electrocardiogram (ECG) signal of a patient to which the adherent device is affixed;
a processing module configured to receive the monitored ECG signal, wherein the processing module detects premature ventricular contractions (PVC) beats based on the monitored ECG signal, wherein the processing module calculates a morphology signal for each detected PVC beat and numerically clusters the PVC beats based on a comparison of the morphology signals, wherein the processing module generates an output indicating a total number of clusters generated, a count of the number of PVC beats included in each cluster, and a representative PVC beat from each cluster, wherein the processing module utilizes a wavelet signal decomposition to calculate the morphology of each PVC beat and wherein the wavelet signal decomposition of the PVC beat is based on a linear combination of sub-band signals defined by equation s morph =ybph+ybpm+ybpl, wherein ybph, ybpm, and ybpl are band-pass limited sub-bands that correspond to different QRS frequencies and S morph is the resulting morphology signal.

7. The system of claim 6 , wherein the output generated by the processing module includes an indication of whether the PVC beats are unifocal or multifocal based on numerical clustering of the PVC beats.

8. The system of claim 6 , wherein numerical clustering of PVC beats by the processing module includes calculating a correlation distance metric between two or more PVC beats to determine whether the PVC beats should be included in the same cluster, wherein the calculated correlation distance metric is compared to a threshold value.

9. The system of claim 6 , wherein the processing module further provides scaling of the calculated morphology signals to a reference heart rate prior to or as part of calculating a morphology of each PVC beat.

10. The system of claim 9 , wherein the processing module selectively contracts/dilates the calculated morphology based on a determined heart rate of the PVC beat and a reference heart rate.

11. The system of claim 6 , wherein the processing module is a distributed processing system that includes a processor located locally on the adherent device and a processor located remotely at a remote monitoring center, wherein the adherent device further includes a wireless transmitter configured to communicate data from the adherent device to the remote monitoring center.

12. A method of monitoring and reporting premature ventricular contractions (PVCs) detected in a patient, the method comprising:
monitoring an electro-cardiogram (ECG) signal of the patient with an adherent device that includes a plurality of electrodes;
detecting premature ventricular contraction (PVC) beats based on the monitored ECG signal, and storing ECG episodes associated with detected PVC beats;
determining an underlying heart rate associated with the monitored ECG signal;
calculating a morphology signal by quantifying the stored ECG episodes associated with detected PVC beats, wherein calculating a morphology signal includes scaling the calculated morphology signals to a reference heart rate based on the determined underlying heart rate;
numerically clustering the PVC beats based on the calculated morphology signals to group PVC beats having a similar morphology; and
generating an output that includes one or more of a total number of clusters generated, a count of the number of PVC beats included in each cluster, and a representative PVC beat from each cluster.

13. The method of claim 12 , wherein scaling includes contracting/dilating the calculated morphology based on the underlying heart rate of the PVC beat and a reference heart rate.

14. The method of claim 12 , wherein numerically clustering the PVC beats includes utilizing a correlation distance metric to compare morphology signals and cluster PVC beats sharing a similar morphology.

15. The method of claim 12 , wherein generating an output includes providing an indication of whether the PVC beats are unifocal or multifocal based on numerical clustering of the PVC beats.

Assignees

• Medtronic Monitoring Inc

Inventors

• Chakravarthy Niranjan
• Chavan Abhi

Classifications

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title

• A61B5/725

  using specific filters therefor, e.g. Kalman or adaptive filters (specific diagnostics methods using using bioelectric or biomagnetic signals A61B5/316) · CPC title

• A61B5/7264

  Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems · CPC title

• A61B5/04012

  Human Necessities · mapped topic

• A61B5/0468 Primary

  Human Necessities · mapped topic