Elyar Ghafoori1, Nathan Angel2, Derek J Dosdall3, Rob S MacLeod4, Ravi Ranjan4. 1. Cardiovascular Medicine, University of Utah, Salt Lake City, UT, USA; Department of Bioengineering, University of Utah, Salt Lake City, UT, USA; Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA. Electronic address: elyar.ghafoori@utah.edu. 2. Acutus Medical, Carlsbad, CA, USA. 3. Cardiovascular Medicine, University of Utah, Salt Lake City, UT, USA; Department of Bioengineering, University of Utah, Salt Lake City, UT, USA; Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA; Department of Surgery, University of Utah, Salt Lake City, UT, USA. 4. Cardiovascular Medicine, University of Utah, Salt Lake City, UT, USA; Department of Bioengineering, University of Utah, Salt Lake City, UT, USA; Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA.
Abstract
BACKGROUND: Differentiating between atrial fibrillation (AF) and atrial tachycardia (AT) or atrial flutter (AFL) on surface ECG can be challenging. The same problem arises in animal models of AF, in which atrial arrhythmias are induced by pacing or pharmacological intervention with the goal of making mechanistic determinations. Some of these induced arrhythmias can be AFL or AT, even though it might appear as AF on the body-surface ECG based on irregular R-R intervals. We hypothesize that a dominant frequency (DF) analysis of the ECG can differentiate between the two distinct arrhythmias, even when it is not evident by the presence of flutter waves or beat-to-beat regularity when looking at brief recordings. METHODS: Canine model (n = 15, 10 controls and 5 Persistent AF animals with >6 months of AF) was used to test the hypothesis. Atrial arrhythmia was induced by rapid atrial pacing. Five blinded observers evaluated the 3‑lead surface ECGs recorded during atrial arrhythmia and classified the rhythm as AFL/AT or AF. The 64-electrode Constellation (Boston Scientific) catheter was used to acquire left (entire group) and right (7 of 10 controls) atrial intracardiac electrograms. For the surface ECG and the intracardiac electrograms, Welch method with a hamming window and 50% overlap was used to calculate DF of two-minute segments. Mean of standard deviations of the DF values were calculated for both ECGs and intracardiac EGMs. Ground truth came from activations maps and DF analysis derived from the intracardiac electrograms recorded in the two chambers. RESULTS: Rapid pacing induced atrial arrhythmias in all the control animals. The ECG in 8 of the 10 control cases was read as AF by at least 80% percent of observers even though the EGMs from the Constellation showed organized activation and consistent DF (STD of DF < 0.001) in all the electrodes confirming the arrhythmia as AFL in 10/10 cases. In the persistent AF group, the DF from the three lead ECGs were significantly different (Mean of STDs = 2.65 ± 0.99) whereas the DF in the control animals with AFL was consistent across all ECG channels (Mean of STDs < 0.001), and the DF in the control animals ECGs was in agreement with the DF of the intracardiac electrograms. CONCLUSION: Surface ECG recordings can mimic AF even when the underlying atrial arrhythmia is AFL in control canine models. DF variation of the signals from multiple surface ECG leads can help differentiate between the AF and AFL.
BACKGROUND: Differentiating between atrial fibrillation (AF) and atrial tachycardia (AT) or atrial flutter (AFL) on surface ECG can be challenging. The same problem arises in animal models of AF, in which atrial arrhythmias are induced by pacing or pharmacological intervention with the goal of making mechanistic determinations. Some of these induced arrhythmias can be AFL or AT, even though it might appear as AF on the body-surface ECG based on irregular R-R intervals. We hypothesize that a dominant frequency (DF) analysis of the ECG can differentiate between the two distinct arrhythmias, even when it is not evident by the presence of flutter waves or beat-to-beat regularity when looking at brief recordings. METHODS:Canine model (n = 15, 10 controls and 5 Persistent AF animals with >6 months of AF) was used to test the hypothesis. Atrial arrhythmia was induced by rapid atrial pacing. Five blinded observers evaluated the 3‑lead surface ECGs recorded during atrial arrhythmia and classified the rhythm as AFL/AT or AF. The 64-electrode Constellation (Boston Scientific) catheter was used to acquire left (entire group) and right (7 of 10 controls) atrial intracardiac electrograms. For the surface ECG and the intracardiac electrograms, Welch method with a hamming window and 50% overlap was used to calculate DF of two-minute segments. Mean of standard deviations of the DF values were calculated for both ECGs and intracardiac EGMs. Ground truth came from activations maps and DF analysis derived from the intracardiac electrograms recorded in the two chambers. RESULTS: Rapid pacing induced atrial arrhythmias in all the control animals. The ECG in 8 of the 10 control cases was read as AF by at least 80% percent of observers even though the EGMs from the Constellation showed organized activation and consistent DF (STD of DF < 0.001) in all the electrodes confirming the arrhythmia as AFL in 10/10 cases. In the persistent AF group, the DF from the three lead ECGs were significantly different (Mean of STDs = 2.65 ± 0.99) whereas the DF in the control animals with AFL was consistent across all ECG channels (Mean of STDs < 0.001), and the DF in the control animals ECGs was in agreement with the DF of the intracardiac electrograms. CONCLUSION: Surface ECG recordings can mimic AF even when the underlying atrial arrhythmia is AFL in control canine models. DF variation of the signals from multiple surface ECG leads can help differentiate between the AF and AFL.
Authors: David E Krummen; Mitul Patel; Hong Nguyen; Gordon Ho; Dhruv S Kazi; Paul Clopton; Marian C Holland; Scott L Greenberg; Gregory K Feld; Mitchell N Faddis; Sanjiv M Narayan Journal: J Cardiovasc Electrophysiol Date: 2010-11
Authors: B M Psaty; T A Manolio; L H Kuller; R A Kronmal; M Cushman; L P Fried; R White; C D Furberg; P M Rautaharju Journal: Circulation Date: 1997-10-07 Impact factor: 29.690
Authors: Junaid A B Zaman Ma Bm BChir; Amir Schricker Md; Gautam G Lalani Md; Rishi Trikha Bs; David E Krummen Md; Sanjiv M Narayan Md PhD Journal: J Atr Fibrillation Date: 2014-08-31