Intracardiac electrograms (EGMs) form the basis for the diagnosis of arrhythmia mechanisms. Bipolar EGMs dominate clinical practice despite major disadvantages over unipolar EGMs since noise strongly distorts the latter. In this study, we quantified and reduced the noise level of uni- and bipolar EGMs recorded with Rhythmia HDx and the Orion catheter. Distinct noise frequencies in the power spectral density (PSD) were detected with a sliding win- dow of variable width and subsequently removed by notch filtering. The absolute peak to peak voltage remaining in the inactive segments after baseline removal quantified the noise level of the system. An international, multi-center selection of 33 patients served as a broad sample cohort. The case-specific detection and removal of noise peaks reduced the noise level in unipolar EGMs by 30% to 0.076 mV compared to standard clinical filtering. With a bipolar noise level of 0.01 mV, we saw that Rhythmia HDx meets the low noise floor claimed in the system specifica- tions. Certain noise frequencies presented permanently in all cases whereas others showed up only intermittently or in individual cases. The suggested extension of filter settings lowers the noise level, enhances the detailed segmentation of low volt- age areas, and encourages to exploit the advantages of unipolar over bipolar EGMs in clinical practice.
C. Nagel, N. Pilia, L. Unger, and O. Dössel. Performance of Different Atrial Conduction Velocity Estimation Algorithms Improves with Knowledge about the Depolarization Pattern. In Current Directions in Biomedical Engineering, vol. 5(1) , pp. 101-104, 2019
Quantifying the atrial conduction velocity (CV) reveals important information for targeting critical arrhythmia sites that initiate and sustain abnormal electrical pathways, e.g. during atrial flutter. The knowledge about the local CV distribution on the atrial surface thus enhances clinical catheter ablation procedures by localizing pathological propagation paths to be eliminated during the intervention. Several algorithms have been proposed for estimating the CV. All of them are solely based on the local activation times calculated from electroanatomical mapping data. They deliver false values for the CV if applied to regions near scars or wave collisions. We propose an extension to all approaches by including a distinct preprocessing step. Thereby, we first identify scars and wave front collisions and provide this information for the CV estimation algorithm. In addition, we provide reliable CV values even in the presence of noise. We compared the performance of the Triangulation, the Polynomial Fit and the Radial Basis Functions approach with and without the inclusion of the aforementioned preprocessing step. The evaluation was based on different activation patterns simulated on a 2D synthetic triangular mesh with different levels of noise added. The results of this study demonstrate that the accuracy of the estimated CV does improve when knowledge about the depolarization pattern is included. Over all investigated test cases, the reduction of the mean velocity error quantified to at least 25 mm/s for the Radial Basis Functions, 14 mm/s for the Polynomial Fit and 14 mm/s for the Triangulation approach compared to their respective implementations without the preprocessing step. Given the present results, this novel approach can contribute to a more accurate and reliable CV estimation in a clinical setting and thus improve the success of radio-frequency ablation to treat cardiac arrhythmias.
The outcomes of ablation targeting either reentry activations or fractionated activity during persistent atrial fibrillation (AF) therapy remain suboptimal due to, among others, the intricate underlying AF dynamics. In the present work, we sought to investigate such AF dynamics in a heterogeneous simulation setup using recurrence quantification analysis (RQA). AF was simulated in a spherical model of the left atrium, from which 412 unipolar atrial electrograms (AEGs) were extracted (2 s duration; 5 mm spacing). The phase was calculated using the Hilbert transform, followed by the identification of points of singularity (PS). Three regions were defined according to the occurrence of PSs: 1) no rotors; 2) transient rotors and; 3) long-standing rotors. Bipolar AEGs (1114) were calculated from pairs of unipolar nodes and bandpass filtered (30-300 Hz). The CARTO criterion (Biosense Webster) was used for AEGs classification (normal vs. fractionated). RQA attributes were calculated from the filtered bipolar AEGs: determinism (DET); recurrence rate (RR); laminarity (LAM). Sample entropy (SampEn) and dominant frequency (DF) were also calculated from the AEGs. Regions with longstanding rotors have shown significantly lower RQA attributes and SampEn when compared to the other regions, suggesting a higher irregular behaviour (P≤0.01 for all cases). Normal and fractionated AEGs were found in all regions (respectively; Region 1: 387 vs. 15; Region 2: 221 vs. 13; Region 3: 415 vs. 63). Region 1 vs. Region 3 have shown significant differences in normal AEGs (P≤0.0001 for all RQA attributes and SampEn), and significant differences in fractionated AEGs for LAM, RR and SampEn (P=0.0071, P=0.0221 and P=0.0086, respectively). Our results suggest the co-existence of normal and fractionated AEGs within long-standing rotors. RQA has unveiled distinct dynamic patterns–irrespective of AEGs classification–related to regularity structures and their nonstationary behaviour in a rigorous deterministic context.