56 research outputs found
Signal Fingerprinting as a Novel Diagnostic Tool to Identify Conduction Inhomogeneity
Get PDFBackground: Inhomogeneous intra-atrial conduction facilitates both initiation and perpetuation of atrial fibrillation (AF) and is reflected in electrogram (EGM) morphology. Objective: The primary objective of this study is to investigate regional differences in features of different EGM types during sinus rhythm (SR) and to design a patient-specific signal fingerprint, which quantifies the severity and extensiveness of inhomogeneity in conduction. Methods: Patients (N = 189, 86% male; mean age 65 ± 9 years) undergoing coronary artery bypass grafting (CABG) underwent high-resolution mapping of the right atrium (RA), left atrium (LA), and pulmonary vein area (PVA) including Bachmann’s bundle (BB). EGMs during 5 s of SR were classified as single potentials (SPs), short double potentials (SDPs, interval between deflections 15 ms), or fractionated potentials (FPs, ≥3 deflections). Of all SPs, differences in relative R- and S-wave amplitude were calculated (R/S ratios). Time difference between first and last deflection was determined (fractionation duration, FD) and potentials with amplitudes < 1.0 mV were labeled as low-voltage. Conduction block (CB) was defined as a difference in local activation time (LAT) between adjacent electrodes of ≥12 ms. Results: A total of 1,763,593 EGMs (9,331 ± 3,336 per patient) were classified (Table 1). Conclusion: The signal fingerprint, consisting of quantified EGM features, including the R/S ratio of SPs, the relative frequency distribution of unipolar voltages, the proportion of low-voltage areas, the proportion of the different types of EGMs, and durations of LDP and FDP, may serve as a diagnostic tool to determine the severity and extensiveness of conduction inhomogeneity. Further studies are required to determine whether the signal fingerprint can be used to identify patients at risk for AF onset or progression
Detection of atrial fibrillation-related electropathology by artificial intelligence: is the future already here?
Get PDFClinical Relevance of Sinus Rhythm Mapping to Quantify Electropathology Related to Atrial Fibrillation
Get PDFProgression of AF is accompanied by structural and electrical remodelling, resulting in complex electrical conduction disorders. This is defined as electropathology and it increases with the progression of AF. The severity of electropathology, thus, defines the stage of AF and is a major determinant of effectiveness of AF therapy. As specific features of AF-related electropathology are still unknown, it is essential to first quantify the electrophysiological properties of atrial tissue and then to examine the inter- and intra-individual variation during normal sinus rhythm. Comparison of these parameters between patients with and without a history of AF unravels quantified electrophysiological features that are specific to AF patients. This can help to identify patients at risk for early onset or progression of AF. This review summarises current knowledge on quantified features of atrial electrophysiological properties during sinus rhythm and discusses its relevance in identifying AF-related electropathology
A novel diagnostic tool to identify atrial endo-epicardial asynchrony using signal fingerprinting
No full textObjective: Patients with persistent atrial fibrillation (AF) have more electrical endo-epicardial asynchrony (EEA) during sinus rhythm (SR) than patients without AF. Prior mapping studies indicated that particularly unipolar, endo- and/or epicardial electrogram (EGM) morphology may be indicators of EEA. This study aim to develop a novel method for estimating the degree of EEA by using unipolar EGM characteristics recorded from either the endo- and/or epicardium. Methods: Simultaneous endo-epicardial mapping during sinus rhythm was performed in 86 patients. EGM characteristics, including unipolar voltages, low-voltage areas (LVAs), potential types (single, short/long double and fractionated potentials: SP, SDP, LDP and FP) and fractionation duration (FD) of double potentials (DP) and FP were compared between EEA and non-EEA areas. Asynchrony Fingerprinting Scores (AFS) containing quantified EGM characteristics were constructed to estimate the degree of EEA. Results: Endo- and epicardial sites of EEA areas are characterized by lower unipolar voltages, a higher number of LDPs and FPs and longer DP and FP durations. Patients with AF have lower potential voltages in EEA areas, along with alterations in the potential types. The EE-AFS, containing the proportion of endocardial LVAs and FD of epicardial DPs, had the highest predictive value for determining the degree of EEA (AUC: 0.913). Endo- and epi-AFS separately also showed good predictive values (AUC: 0.901 and 0.830 respectively). Conclusions: EGM characteristics can be used to identify EEA areas. AFS can be utilized as a novel diagnostic tool for accurately estimating the degree of EEA. These characteristics potentially indicate AF related arrhythmogenic substrates.Signal Processing System
Analyzing the effect of electrode size on electrogram and activation map properties
Get PDFBackground: Atrial electrograms recorded from the epicardium provide an important tool for studying the initiation, perpetuation, and treatment of AF. However, the properties of these electrograms depend largely on the properties of the electrode arrays that are used for recording these signals. Method: In this study, we use the electrode's transfer function to model and analyze the effect of electrode size on the properties of measured electrograms. To do so, we use both simulated as well as clinical data. To simulate electrogram arrays we use a two-dimensional (2D) electrogram model as well as an action propagation model. For clinical data, however, we first estimate the trans-membrane current for a higher resolution 2D modeled cell grid and later use these values to interpolate and model electrograms with different electrode sizes. Results: We simulate electrogram arrays for 2D tissues with 3 different levels of heterogeneity in the conduction and stimulation pattern to model the inhomogeneous wave propagation observed during atrial fibrillation. Four measures are used to characterize the properties of the simulated electrogram arrays of different electrode sizes. The results show that increasing the electrode size increases the error in LAT estimation and decreases the length of conduction block lines. Moreover, visual inspection also shows that the activation maps generated by larger electrodes are more homogeneous with a lower number of observed wavelets. The increase in electrode size also increases the low voltage areas in the tissue while decreasing the slopes and the number of detected deflections. The effect is more pronounced for a tissue with a higher level of heterogeneity in the conduction pattern. Similar conclusions hold for the measurements performed on clinical data. Conclusion: The electrode size affects the properties of recorded electrogram arrays which can respectively complicate our understanding of atrial fibrillation. This needs to be considered while performing any analysis on the electrograms or comparing the results of different electrogram arrays.</p
Identification of local atrial conduction heterogeneities using high-density conduction velocity estimation
Get PDFAims: Accurate determination of intra-atrial conduction velocity (CV) is essential to identify arrhythmogenic areas. The most optimal, commonly used, estimation methodology to measure conduction heterogeneity, including finite differences (FiD), polynomial surface fitting (PSF), and a novel technique using discrete velocity vectors (DVV), has not been determined. We aim (i) to identify the most suitable methodology to unravel local areas of conduction heterogeneities using high-density CV estimation techniques, (ii) to quantify intra-atrial differences in CV, and (iii) to localize areas of CV slowing associated with paroxysmal atrial fibrillation (PAF). Methods and results: Intra-operative epicardial mapping (>5000 sites, interelectrode distances 2 mm) of the right and left atrium and Bachmann's bundle (BB) was performed during sinus rhythm (SR) in 412 patients with or without PAF. The median atrial CV estimated using the DVV, PSF, and FiD techniques was 90.0 (62.4-116.8), 92.0 (70.6-123.2), and 89.4 (62.5-126.5) cm/s, respectively. The largest difference in CV estimates was found between PSF and DVV which was caused by smaller CV magnitudes detected only by the DVV technique. Using DVV, a lower CV at BB was found in PAF patients compared with those without atrial fibrillation (AF) [79.1 (72.2-91.2) vs. 88.3 (79.3-97.2) cm/s; P < 0.001]. Conclusions: Areas of local conduction heterogeneities were most accurately identified using the DVV technique, whereas PSF and FiD techniques smoothen wavefront propagation thereby masking local areas of conduction slowing. Comparing patients with and without AF, slower wavefront propagation during SR was found at BB in PAF patients, indicating structural remodelling
An accurate and efficient method to train classifiers for atrial fibrillation detection in ECGs: Learning by asking better questions
Get PDFBackground: An increasing number of wearables are capable of measuring electrocardiograms (ECGs), which may help in early detection of atrial fibrillation (AF). Therefore, many studies focus on automated detection of AF in ECGs. A major obstacle is the required amount of manually labelled data. This study aimed to provide an efficient and reliable method to train a classifier for AF detection using large datasets of real-life ECGs. Method: Human-controlled semi-supervised learning was applied, consisting of two phases: the pre-training phase and the semi-automated training phase. During pre-training, an initial classifier was trained, which was used to predict the classes of new ECG segments in the semi-automated training phase. Based on the degree of certainty, segments were added to the training dataset automatically or after human validation. Thereafter, the classifier was retrained and this procedure was repeated. To test the model performance, a real-life telemetry dataset containing 3,846,564 30-s ECG segments of hospitalized patients (n = 476) and the CinC Challenge 2017 database were used. Results: After pre-training, the average F1-score on a hidden testing dataset was 89.0%. Furthermore, after the pre-training phase 68.0% of all segments in the hidden test set could be classified with an estimated probability of successful classification of 99%, providing an F1-score of 97.9% for these segments. During the semi-automated training phase, this F1-score showed little variation (97.3%–97.9% in the hidden test set), whilst the number of segments which could be automatically classified increased from 68.0% to 75.8% due to the enhanced training dataset. At the same time, the overall F1-score increased from 89.0% to 91.4%. Conclusions: Human-validated semi-supervised learning makes training a classifier more time efficient without compromising on accuracy, hence this method might be valuable in the automated detection of AF in real-life ECGs.Signal Processing System
Conduction Velocity and Anisotropic Properties of Fibrillation Waves During Acutely Induced and Long-Standing Persistent AF
Get PDFBackground: Quantified features of local conduction heterogeneity due to pathological alterations of myocardial tissue could serve as a marker for the degree of electrical remodeling and hence be used to determine the stage of atrial fibrillation (AF). In this study, the authors investigated whether local directional heterogeneity (LDH) and anisotropy ratio, derived from estimated local conduction velocities (CVs) during AF, are suitable electrical parameters to stage AF. Methods: Epicardial mapping (244-electrode array, interelectrode distance 2.25 mm) of the right atrium was performed during acute atrial fibrillation (AAF) (n = 25, 32 ± 11 years of age) and during long-standing persistent atrial fibrillation (LSPAF) (n = 23, 64 ± 9 years of age). Episodes of 9 ± 4 seconds of AF were analyzed. Local CV vectors were constructed to assess the degree of anisotropy. Directions and magnitudes of individual vectors were compared with surrounding vectors to identify LDH. Results: Compared with the entire AAF group, LSPAF was characterized by slower conduction (71.5 ± 6.8 cm/s vs 67.6 ± 5.6 cm/s; P = 0.037) with a larger dispersion (1.59 ± 0.21 vs 1.95 ± 0.17; P < 0.001) and temporal variability (32.0 ± 4.7 cm/s vs 38.5 ± 3.3 cm/s; P < 0.001). Also, LSPAF was characterized by more LDH (19.6% ± 4.4% vs 26.0% ± 3.4%; P < 0.001) and a higher degree of anisotropy (1.38 ± 0.07 vs 1.51 ± 0.14; P < 0.001). Compared with the most complex type of AAF (type III), LSPAF was still associated with a larger CV dispersion, higher temporal variability of CV, and larger amount of LDH. Conclusions:Increasing AF complexity was associated with increased spatiotemporal variability of local CV vectors, local conduction heterogeneity, and anisotropy ratio. By using these novel parameters, LSPAF could potentially be discriminated from the most complex type of AAF. These observations may indicate pathological alterations of myocardial tissue underlying progression of AF.</p
Reduction of conduction velocity in patients with atrial fibrillation
Get PDFIt is unknown to what extent atrial fibrillation (AF) episodes affect intra-atrial conduction velocity (CV) and whether regional differences in local CV heterogeneities exist during sinus rhythm. This case-control study aims to compare CV assessed throughout both atria between patients with and without AF. Patients (n = 34) underwent intra-operative epicardial mapping of the right atrium (RA), Bachmann’s bundle (BB), left atrium (LA) and pulmonary vein area (PVA). CV vectors were constructed to calculate median CV in addition to total activation times (TAT) and unipolar voltages. Biatrial median CV did not differ between patients with and without AF (90 ± 8 cm/s vs. 92 ± 6 cm/s, p = 0.56); only BB showed a CV reduction in the AF group (79 ± 12 cm/s vs. 88 ± 11 cm/s, p = 0.02). In patients without AF, there was no predilection site for the lowest CV (P5 ) (RA: 12%; BB: 29%; LA: 29%; PVA: 29%). In patients with AF, lowest CV was most often measured at BB (53%) and ranged between 15 to 22 cm/s (median: 20 cm/s). Lowest CVs were also measured at the LA (18%) and PVA (29%), but not at the RA. AF was associated with a prolonged TAT (p = 0.03) and decreased voltages (P5 ) at BB (p = 0.02). BB was a predilection site for slowing of conduction in patients with AF. Prolonged TAT and decreased voltages were also found at this site. The next step will be to determine the relevance of a reduced CV at BB in relation to AF development and maintenance
An unexpected finding by epicardial mapping: Atrial fibrillation in a 14-month-old patient with short QT syndrome
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