Methods and systems for mapping cardiac activity

1. A method of mapping cardiac activity, comprising:

receiving an electrogram signal S(t) at an electroanatomical mapping system; and

executing, via the electroanatomical mapping system, a process comprising:

transforming the electrogram signal S(t) into the wavelet domain, thereby computing a scalogram G(f, t);

computing at least one energy function L(t) of the scalogram G(f, t), wherein L(t)=ΣG(f,t); and

computing at least one metric of the electrogram signal S(t) using the at least one energy function L(t) by computing a pulse wave L Pulse (t) having a pulse duration and one or more pulses, where

L

Pulse

⁡

(

t

)

=

{

1

,

if

⁢

⁢

L

⁡

(

t

)

>

0

0

,

otherwise

;

and

displaying a graphical representation of the at least one metric of the electrogram signal S(t) on a graphical representation of a cardiac model.

2. The method according to claim 1 , wherein transforming the electrogram signal S(t) into the wavelet domain comprises applying a continuous wavelet transformation to the electrogram signal S(t) to compute the scalogram G(f, t).

3. The method according to claim 1 , further comprising setting values of G(f, t) less than a preset noise threshold to zero.

4. The method according to claim 1 , wherein the at least one metric of the electrogram signal S(t) comprises a QRS activity duration for the electrogram signal S(t), wherein the QRS activity duration for the electrogram signal S(t) is defined to be equal to the pulse duration of the pulse wave L Pulse (t).

5. The method according to claim 1 , wherein f is within a cardiac activity frequency range defined by a preset lower frequency limit and a preset upper frequency limit.

6. The method according to claim 5 , further comprising:

detecting a plurality of local maximum peaks in the at least one energy function L(t); and

categorizing each local maximum peak of the plurality of local maximum peaks as a near-field peak, a far-field peak, or a noise peak.

7. The method according to claim 6 , wherein categorizing each local maximum peak of the plurality of local maximum peaks as a near-field peak, a far-field peak, or a noise peak comprises:

categorizing a local maximum peak as a near-field peak if the at least one energy function L(t) exceeds a preset near field threshold at the local maximum peak;

categorizing the local maximum peak as a far-field peak if the at least one energy function L(t) exceeds a preset far field threshold and not the preset near field threshold at the local maximum peak; and

categorizing the local maximum peak as a noise peak otherwise.

8. The method according to claim 6 , wherein the at least one metric of the electrogram signal S(t) comprises at least one of a near-field component duration and a far-field component duration for the electrogram signal S(t), computed by a method comprising:

for each pulse of the one or more pulses of the pulse wave L Pulse (t):

if the pulse includes a near-field peak, defining the near-field component duration for the electrogram signal S(t) to be equal to a duration of the pulse; and

if the pulse includes a far-field peak, defining the far-field component duration for the electrogram signal S(t) to be equal to the duration of the pulse.

9. The method according to claim 6 , wherein the at least one metric of the electrogram signal S(t) comprises a number of multiple components for the electrogram signal S(t), wherein the number of multiple components for the electrogram signal S(t) is defined to be equal to a total number of the plurality of local maximum peaks in the at least one energy function L(t).

10. The method according to claim 6 , wherein the at least one metric of the electrogram signal S(t) comprises a slope of a sharpest component of the electrogram signal S(t).

11. The method according to claim 10 , wherein the slope of a sharpest component of the electrogram signal S(t) is computed by a method comprising:

identifying a maximum energy near-field peak of the plurality of local maximum peaks in the at least one energy function L(t);

computing a maximum value of a first derivative S′(t) of the electrogram signal S(t) within a preset refractory window surrounding the maximum energy near-field peak; and

defining the maximum value of the first derivative S′(t) of the electrogram signal S(t) within the preset refractory window as the slope of the sharpest component of the electrogram signal S(t).

12. The method according to claim 1 , wherein:

the at least one energy function L(t) comprises:

computing a high frequency energy function L High (t)=ΣG(f High ,t); and

computing a low frequency energy function L Low (t)ΣG (f Low ,t); and

computing the at least one metric of the electrogram signal S(t) comprises computing a ratio of L High (t) to L Low (t).

13. The method according to claim 12 , wherein f High is between 60 Hz and 300 Hz and f Low is between 10 Hz and 60 Hz.

14. The method according to claim 12 , wherein the at least one metric of the electrogram signal S(t) comprises a cycle-length based metric computed using the ratio of L High (t) to L Low (t).

15. The method according to claim 14 , wherein the cycle-length based metric is computed using the ratio of L High (t) to L Low (t) based upon a plurality of local activation times detected when the ratio of L High (t) to L Low (t) exceeds a preset threshold.

16. A system for mapping cardiac activity, comprising:

a wavelet transformation processor configured:

to receive an electrogram S(t);

to transform the electrogram signal S(t) into the wavelet domain, thereby computing a scalogram G(f, t); and

to compute at least one energy function L(t) of the scalogram G(f, t), wherein L(t)=ΣG(f,t); and

a mapping processor configured to:

compute at least one metric of the electrogram signal S(t) in the wavelet domain using the at least one energy function L(t), comprising computing a pulse wave L Pulse (t) having a pulse duration and one or more pulses, where

L

Pulse

⁡

(

t

)

=

{

1

,

if

⁢

⁢

L

⁡

(

t

)

>

0

0

,

otherwise

;

 and

output a graphical representation of the at least one metric of the electrogram signal S(t) on a graphical representation of a cardiac model; and

a display to display the graphical representation.

17. The system according to claim 16 , wherein the at least one metric of the electrogram signal S(t) is selected from the group consisting of: a QRS activity duration for the electrogram signal S(t); a near-field component duration for the electrogram signal S(t); a far-field component duration for the electrogram signal S(t); a number of multiple components for the electrogram signal S(t); a slope of a sharpest component of the electrogram signal S(t); a scalogram width; an energy ratio in the electrogram signal S(t); and a cycle-length based metric of the electrogram signal S(t).