P439Could regional electrogram desynchronization identified using mean phase coherence be potential ablation targets in persistent atrial fibrillation?

Funding Acknowledgements

This work was supported by the NIHR Leicester Biomedical Research Centre. XL was funded by MRC(MR/S037306/1) and BHF (PG/18/33/33780)

Background

It remains controversial as to whether rotors detected using phase mapping during persistent atrial fibrillation (persAF) represent main drivers of the underlying mechanism as others found rotors to be located near line of conduction block. Regional electrogram desynchronization (RED) has been suggested as successful targets for persAF ablation, but automatic tools and quantitative measures are lacking.

Purpose

We aim to use mean phase coherence (MPC) to automatically identify RED regions during persAF. This method was compared with phase singularity density (PSD) maps.

Methods

Patients undergoing left atrial (LA) persAF ablation were enrolled (n = 10). 2048-channel virtual electrograms (VEGMs) were collected from each patient using non-contact mapping (St Jude Velocity System, Ensite Array) for 10 seconds.  To remove far field ventricular activities, QRS onset and T wave end locations were detected from ECG lead I (Figure 1A) and only the VEGM segments from T end to QRS onset were included in the analysis. VEGMs were reconstructed using sinusoidal wavelets fitting and the phase of VEGMs determined using Hilbert transform. Phase singularities (PS) were detected using the topological charge method and repetitive PSD maps were generated. RED was defined as the average of MPC of each node against direct neighbouring nodes on the 3D mesh (Figure 1A-B). Linear regression analysis was used to compare the average MPC vs. PSD and vs. the standard deviation of MPC (MPC_SD).

Results

A total of 221,184 VEGM segments were analysed with mean duration of 364.2 milliseconds.  MPC has shown the ability to quantify the level of synchronisation between VEGMs (Figure 1B). Inverse correlation was found between PSD and average MPC values for all 10 patients (p < 0.0001, Figure 1C). Average MPC and MPC_SD were found to be inversely correlated (p < 0.0001, Figure 1C). Spatially, similar graphic patterns can be found from LA MPC maps and PSD maps for all patients (Figure 1D).

Conclusion

We have proposed a method to quantify the level of synchronisation between VEGMs. Phase density mapping showed a considerable agreement with RED regions reflecting regional conducting delays, which supports the previous finding where rotors found at conduction block. Inverse correlation between local average MPC and MPC_SD suggests that conduction delays of the identified regions are not heterogenous, posing directional preferences. Rather than solely looking for rotational activities, this method could identify comprehensive RED regions, which may also explain the conflicting results from different studies targeting rotational activities, where incomplete subsets of RED regions could have been targeted. Atrial RED regions can easily be identified with simultaneously collected electrograms from multi-polar catheters and should be targeted in future persAF studies.

Abstract Figure 1

Detection of asphyxia using heart rate variability

The long-term aims of this study are to find a parameter derived from the ECG that has a high sensitivity and specificity to asphyxia and, once we know or suspect that asphyxia occurred, to estimate how severe it was. We carried out a pilot study in which 24 adult Wistar rats were anaesthetised and subjected to controlled asphyxia for specified durations. We measured the pH, 'neurological score' and the ECG, extracting from this heart rate and heart rate variability (HRV). We have developed a technique capable of detecting asphyxia in less than 1 min, based on monitoring the ECG and estimating HRV by measuring the standard deviation of normal RR intervals (the RR interval is the time interval between two consecutive R-points of the QRS complex). In all cases the heart rate decreased and HRV increased, by an average of 46 +/- 33 ms in relation to the baseline, at the onset of asphyxia. The comparison of the base level of HRV after and before asphyxia shows promise for the estimation of the severity of the episode; however, the limitations of this study should be noted as they include the small size of the cohort and the methods of analysis.

Spectral broadening of clinical Doppler signals using FFT and autoregressive modelling

OBJECTIVE

This paper investigates the behaviour of the spectral broadening index (SBI) derived from spectra obtained using autoregressive (AR) modelling compared to that of SBI based on fast Fourier transform (FFT) analysis of clinical Doppler ultrasound scans.

METHODS

Doppler signals from internal carotid arteries of patients with normal and diseased vessels with up to 80% stenosis were analysed. A threshold at -6 dB of the maximum magnitude component of each individual spectrum was implemented to reject low-level noise. The SBI was obtained using the maximum and the mean frequency envelopes extracted from the sonogram.

RESULTS

A qualitative improvement in both the appearance of the AR sonograms and the shape of the individual AR spectra was noticeable. The AR approach consistently produced narrower spectra than the FFT and the shapes of the frequency envelopes derived from the AR sonogram and the FFT sonogram were also rather different. Despite these differences a strong correlation was observed between the value of the FFT-based SBI and the AR-based SBI. The mean value of the FFT-SBI is larger than that of the AR-SBI and the variance of the FFT-SBI is smaller than that of the AR-SBI based on a set of at least 20 sequentially recorded heartbeats.

CONCLUSIONS

It was established that, for all cases where significant stenosis was present, a statistically significant value for SBI could be obtained using four or more heartbeats if five spectra around the peak systole were used to estimate the SBI of each individual heartbeat. No quantitative advantage in using the AR approach over the FFT for the determination of SBI was obtained due to the poorer variance of the AR-SBI and the additional computational complexity of the AR approach.

A study of the spectral broadening of simulated Doppler signals using FFT and AR modelling

Doppler ultrasound is used clinically to detect stenosis in the carotid artery. The presence of stenosis may be identified by disturbed flow patterns distal to the stenosis that cause spectral broadening in the spectrum of the Doppler signal around peak systole. This paper investigates the behaviour of the spectral broadening index (SBI) derived from wide-band spectra obtained using autoregressive modelling (AR), compared with the SBI based on the fast-Fourier transform (FFT) spectra. Simulated Doppler signals were created using white noise and shaped filters to analyse spectra typically found around the systolic peak and to assess the magnitude and variance of AR and FFT-SBI for a range of signal-to-noise ratios. The results of the analysis show a strong correlation between the indices calculated using the FFT and AR algorithms. Despite the qualitative improvement of the AR spectra over the FFT, the estimation of SBI for short data frames is not significantly improved using AR.

An automatic system for capturing and processing ultrasonic Doppler signals and blood pressure signals

A totally automatic system for capturing and processing ultrasonic Doppler signals and blood pressure signals at pre-programmed intervals is described. The Doppler signals are Fourier transformed and a composite maximum frequency envelope extracted from the spectral data. The frequency envelope is split into individual cardiac cycles and a number of Doppler parameters calculated. All Doppler and blood pressure results are stored on disc, and the most important may be displayed in the form of trend graphs on a computer screen. The system has been used to study variations in middle cerebral artery blood flow velocity and systemic blood pressure in neonates over periods of several hours.