Deconvolution: a novel signal processing approach for determining activation time from fractionated electrograms and detecting infarcted tissue.
BACKGROUND
Two important signal processing applications in electrophysiology are activation mapping and characterization of the tissue substrate from which electrograms are recorded. We hypothesize that a novel signal-processing method that uses deconvolution is more accurate than amplitude, derivative, and manual activation time estimates. We further hypothesize that deconvolution quantifies changes in morphology that detect electrograms recorded from regions of myocardial infarction.


RESULTS
To determine the accuracy of activation time estimation, 600 unipolar electrograms were calculated with a detailed computer model using various degrees of coupling heterogeneity to model infarction. Local activation time was defined as the time of peak inward sodium current in the modeled myocyte closest to the electrode. Deconvolution, minimum derivative, and maximum amplitude were calculated. Two experienced electrophysiologists blinded to the computer-determined activation times marked their estimates of activation time. F tests compared the variance of activation time estimation for each method. To evaluate the performance of deconvolution to detect infarction, 380 unipolar electrograms were recorded from 10 dogs with infarcts resulting from ligation of the left anterior descending coronary artery. The amplitude, duration, number of inflections, peak frequency, bandwidth, minimum derivative, and deconvolution were calculated. Metrics were compared by Mann-Whitney rank-sum tests, and receiver operating curves were plotted.


CONCLUSIONS
Deconvolution estimated local activation time more accurately than the other metrics (P < .0001). Furthermore, the algorithm quantified changes in morphology (P < .0001) with superior performance, detecting electrograms recorded from regions of myocardial infarction. Thus, deconvolution, which incorporates a priori knowledge of electrogram morphology, shows promise to improve present clinical metrics.
Ellis
W S
WS
Cardiovascular Research Institute, University of California, San Francisco 94143-1354, USA.
Eisenberg
S J
SJ
Auslander
D M
DM
Dae
M W
MW
Zakhor
A
A
Lesh
M D
MD
D000465:Algorithms; D000818:Animals; D004285:Dogs; D001330:Electronic Data Processing; D004594:Electrophysiology / Q000379:methods*; D008955:Models, Cardiovascular; D009203:Myocardial Infarction / Q000175:diagnosis*; D012372:ROC Curve; D016037:Single-Blind Method