Method and system to detect atrial flutter waves in cardiac activity signals

Patent metadata

Field	Value
Publication number	US-2020046245-A1
Application number	US-201816058910-A
Country	US
Kind code	A1
Filing date	Aug 8, 2018
Priority date	Aug 8, 2018
Publication date	Feb 13, 2020
Grant date	—

Abstract

A method and system are provided for detecting arrhythmias in cardiac activity. The method the method and system, under control of one or more processors configured with specific executable instructions, obtain cardiac activity (CA) signals for a series of beats, build a QRS-T template based on an ensemble of QRS complexes within the CA signals, and subtract the QRS-T template from the CA signals to obtain QRS-T scrubbed CA signals. The method and system determine an atrial flutter (AFL) timing feature within the QRS scrubbed CA signals, and declare an AFL episode based on a relation between the AFL timing feature and an AFL cluster criteria.

First claim

What is claimed is:

1 . A computer implemented method for detecting arrhythmias in cardiac activity, comprising:
under control of one or more processors configured with specific executable instructions, obtaining cardiac activity (CA) signals for a series of beats; building a QRS-T template based on an ensemble of QRS complexes within the CA signals; subtracting the QRS-T template from the CA signals to obtain QRS-T scrubbed CA signals; determining an atrial flutter (AFL) timing feature within the QRS scrubbed CA signals; and declaring an AFL episode based on a relation between the AFL timing feature and an AFL cluster criteria.

2 . The method of claim 1 , further comprising determining cycle lengths of AFL wave features within the QRS scrubbed CA signals, the AFL timing feature determined based on the cycle lengths.

3 . The method of claim 2 , wherein the AFL timing feature represents a distribution of the cycle lengths.

4 . The method of claim 2 , wherein the declaring operation comprises declaring the AFL episode when the AFL timing feature exhibits a distribution of cycle lengths having a select peak within a select timing range.

5 . The method of claim 1 , further comprising, before performing the subtracting operation, aligning the QRS-T template with each QRS-T segment in the CA signals.

6 . The method of claim 5 , further comprising scaling the QRS-T template based on the amplitude of each QRS-T segment in the CA signals before the subtracting operation.

7 . The method of claim 1 , wherein the building operation comprises building a QRS-T template and wherein the subtracting operation comprises subtracting the QRS-T template from each QRS-T segment in the CA signals to calculate the QRS-T scrubbed CA signals.

8 . The method of claim 1 , wherein the QRS-T scrubbed CA signals include residual QRS complex, the method further comprising applying a blanking mask to blank a QRS segment of the QRS-T scrubbed CA signal to eliminate a residual QRS components from the QRS-T scrubbed CA signals.

9 . The method of claim 8 , wherein the applying the blanking mask comprises identifying leading and trailing values of the CA signals at the beginning and ending points of the blanking mask and replacing the CA signals with a straight-line signal between the beginning and ending points.

10 . The method of claim 1 , further comprising generating a histogram that maintains a count of AFL cycle lengths within predetermined bins, the determining operation determining whether the count within a select one or more of the predetermined bins exceeds a threshold.

11 . A system for detecting arrhythmias in cardiac activity, comprising:
memory to store specific executable instructions; one or more processors configured to execute the specific executable instructions for: obtaining cardiac activity (CA) signals for a series of beats; building a QRS-T template based on an ensemble of QRS complexes within the CA signals; subtracting the QRS-T template from the CA signals to obtain QRS scrubbed CA signals; determining an atrial flutter (AFL) timing feature within the QRS scrubbed CA signals; and declaring an AFL episode based on a relation between the AFL timing feature and an AFL cluster criteria.

12 . The system of claim 11 , wherein the one or more processors are further configured to determine cycle lengths of AFL wave features within the QRS scrubbed CA signals, the AFL timing feature determined based on the cycle lengths.

13 . The system of claim 12 , wherein the AFL timing feature represents a distribution of the cycle lengths.

14 . The system of claim 11 , wherein the one or more processors are configured to declare the AFL episode when the AFL timing feature exhibits a distribution of cycle lengths having a select peak within a select timing range.

15 . The system of claim 11 , wherein the one or more processors are further configured to align the QRS-T template with an R-wave marker of a QRS complex of the CA signals before performing the subtracting operation.

16 . The system of claim 15 , wherein the one or more processors are further configured to scale the QRS-Template based on the amplitude of QRS-T segment in the CA signals before the subtracting operation.

17 . The system of claim 11 , wherein the building operation comprises building, as the QRS-Template, a QRS-T template and wherein the subtracting operation comprises subtracting the QRS-T template from the CA signals to calculate the QRS-T scrubbed CA signals.

18 . The system of claim 11 , wherein the QRS scrubbed CA signals include residual QRS complex, and wherein the one or more processors are configured to apply a blanking mask to blank a QRS segment of the QRS-T scrubbed CA signal to eliminate a residual QRS components from the QRS-T scrubbed CA signals.

19 . The system of claim 11 , further comprising an implantable medical device housing the processor and memory.

20 . The system of claim 11 , wherein the processor and memory are housed within at least one of a local external device and a remote server.

Assignees

• Pacesetter Inc

Inventors

• Qu Fujian
• Bornzin Gene A
• Gill Jong
• Rosenberg Stuart
• Malhotra Neha

Classifications

• A61B5/1118

  Determining activity level · CPC title

• A61B5/0022

  Monitoring a patient using a global network, e.g. telephone networks, internet · CPC title

• A61B5/0816

  Measuring devices for examining respiratory frequency (measuring frequency of electric signals G01R23/00) · CPC title

• A61B2562/0271

  Thermal or temperature sensors · CPC title

• A61B5/14539

  for measuring pH · CPC title