The ECG as a Tool to Determine Atrial Fibrillation Complexity
The ECG as a Tool to Determine Atrial Fibrillation Complexity
The ultimate goal of research on AF complexity measures based on the ECG is to develop an AF classification allowing patient-tailored AF treatment as advocated by recent guidelines and consensus conferences. The current classification of AF, divided in paroxysmal, persistent or long-standing persistent AF, is purely based on the duration of the AF episodes and whether or not AF terminates by itself. Assessing the duration of AF can be challenging due to high variability of symptoms. The ECG derived atrial complexity parameters mentioned in this review might serve as a basis to determine the relative degree of electrophysiological changes occurring over time, because some of these parameters are able to distinguish between patients from the different groups. Patients with paroxysmal AF have a lower DF and lower SampEn than patients with persistent AF. Patients with persistent AF have a higher degree of spatiotemporal organisation compared with patients with long-standing persistent AF. Figure 3 shows an example of V1 and the frequency spectrum from a patient with paroxysmal AF and a patient with persistent AF. The patient with paroxysmal AF shows a lower DF, higher OI and lower SE than the patient with persistent AF. In figure 4 an example of a BSPM from a patient with paroxysmal and persistent AF is given. While the DF is higher in the patient with persistent AF (upper panel) the organisation is lower. The degree of these changes certainly partly reflects the duration of AF episodes but also might reflect the presence of predisposing structural alterations due to HF or ageing. These structural alterations—besides duration of AF—have also been shown to be significant determinants of successful rhythm control therapy. For this reason, ECG-based atrial substrate complexity parameters might be more accurate to classify patients than the clinical history.
(Enlarge Image)
Figure 3.
The left panel shows lead V1 from a patient with paroxysmal atrial fibrillation (AF) and the right panel shows lead V1 from a patient with persistent AF. (A) Shows lead V1, (B) shows the atrial signal after QRST cancellation and (C) shows the corresponding frequency spectrum. Note the lower dominant frequency (DF) and spectral entropy and higher organisation index in the patient with paroxysmal AF compared with the patient with persistent AF.
(Enlarge Image)
Figure 4.
(A) Shows the dominant frequency on a body surface potential map of a patient in paroxysmal atrial fibrillation (AF) with first the dominant frequency of every single electrode and below the interpolated signals. (B) Shows the dominant frequency of a patient in persistent AF. (C and D) show the organisation index of the same patients. Blue means a low value whereas the red colour means a higher value. The dominant frequency in the patient with paroxysmal AF is lower and more evenly distributed on the body surface compared with the patient with persistent AF. Furthermore, the organisation index is higher in the patient with paroxysmal AF.
Figure 5 shows a possible flow chart for the management of patients with AF based on a combination of the patient's symptoms with an ECG-based classification of AF. In patients with a low AF complexity, a rhythm control strategy using either AADs or catheter ablation with pulmonary vein isolation only could be the preferred treatment option. In patients with a highly complex AF substrate rate control might be the best treatment option. Should rhythm control still be pursued, catheter ablation with extensive substrate modification rather than cardioversion and prophylactic AADs could be the most successful treatment. To assess the true value of a model containing different atrial complexity parameters to guide treatment a prospective clinical trial should be undertaken using the ECG as a guide for treatment.
(Enlarge Image)
Figure 5.
Possible flow chart for the management of patients with atrial fibrillation (AF) based on a combination of the patient's symptoms with an ECG-based classification of AF.
ECG Based Classification
The ultimate goal of research on AF complexity measures based on the ECG is to develop an AF classification allowing patient-tailored AF treatment as advocated by recent guidelines and consensus conferences. The current classification of AF, divided in paroxysmal, persistent or long-standing persistent AF, is purely based on the duration of the AF episodes and whether or not AF terminates by itself. Assessing the duration of AF can be challenging due to high variability of symptoms. The ECG derived atrial complexity parameters mentioned in this review might serve as a basis to determine the relative degree of electrophysiological changes occurring over time, because some of these parameters are able to distinguish between patients from the different groups. Patients with paroxysmal AF have a lower DF and lower SampEn than patients with persistent AF. Patients with persistent AF have a higher degree of spatiotemporal organisation compared with patients with long-standing persistent AF. Figure 3 shows an example of V1 and the frequency spectrum from a patient with paroxysmal AF and a patient with persistent AF. The patient with paroxysmal AF shows a lower DF, higher OI and lower SE than the patient with persistent AF. In figure 4 an example of a BSPM from a patient with paroxysmal and persistent AF is given. While the DF is higher in the patient with persistent AF (upper panel) the organisation is lower. The degree of these changes certainly partly reflects the duration of AF episodes but also might reflect the presence of predisposing structural alterations due to HF or ageing. These structural alterations—besides duration of AF—have also been shown to be significant determinants of successful rhythm control therapy. For this reason, ECG-based atrial substrate complexity parameters might be more accurate to classify patients than the clinical history.
(Enlarge Image)
Figure 3.
The left panel shows lead V1 from a patient with paroxysmal atrial fibrillation (AF) and the right panel shows lead V1 from a patient with persistent AF. (A) Shows lead V1, (B) shows the atrial signal after QRST cancellation and (C) shows the corresponding frequency spectrum. Note the lower dominant frequency (DF) and spectral entropy and higher organisation index in the patient with paroxysmal AF compared with the patient with persistent AF.
(Enlarge Image)
Figure 4.
(A) Shows the dominant frequency on a body surface potential map of a patient in paroxysmal atrial fibrillation (AF) with first the dominant frequency of every single electrode and below the interpolated signals. (B) Shows the dominant frequency of a patient in persistent AF. (C and D) show the organisation index of the same patients. Blue means a low value whereas the red colour means a higher value. The dominant frequency in the patient with paroxysmal AF is lower and more evenly distributed on the body surface compared with the patient with persistent AF. Furthermore, the organisation index is higher in the patient with paroxysmal AF.
Figure 5 shows a possible flow chart for the management of patients with AF based on a combination of the patient's symptoms with an ECG-based classification of AF. In patients with a low AF complexity, a rhythm control strategy using either AADs or catheter ablation with pulmonary vein isolation only could be the preferred treatment option. In patients with a highly complex AF substrate rate control might be the best treatment option. Should rhythm control still be pursued, catheter ablation with extensive substrate modification rather than cardioversion and prophylactic AADs could be the most successful treatment. To assess the true value of a model containing different atrial complexity parameters to guide treatment a prospective clinical trial should be undertaken using the ECG as a guide for treatment.
(Enlarge Image)
Figure 5.
Possible flow chart for the management of patients with atrial fibrillation (AF) based on a combination of the patient's symptoms with an ECG-based classification of AF.
