| 1. |
- Bachi, Lorenzo, et al.
(författare)
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ECG Modeling for Simulation of Arrhythmias in Time-Varying Conditions
- 2023
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Ingår i: IEEE Transactions on Biomedical Engineering. - 0018-9294. ; 70:12, s. 3449-3460
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Tidskriftsartikel (refereegranskat)abstract
- The present paper proposes an ECG simulator that advances modeling of arrhythmias and noise by introducing time-varying signal characteristics. The simulator is built around a discrete-time Markov chain model for simulating atrial and ventricular arrhythmias of particular relevance when analyzing atrial fibrillation (AF). Each state is associated with statistical information on episode duration and heartbeat characteristics. Statistical, time-varying modeling of muscle noise, motion artifacts, and the influence of respiration is introduced to increase the complexity of simulated ECGs, making the simulator well suited for data augmentation in machine learning. Modeling of how the PQ and QT intervals depend on heart rate is also introduced. The realism of simulated ECGs is assessed by three experienced doctors, showing that simulated ECGs are difficult to distinguish from real ECGs. Simulator usefulness is illustrated in terms of AF detection performance when either simulated or real ECGs are used to train a neural network for signal quality control. The results show that both types of training lead to similar performance.
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| 2. |
- Ben-Moshe, Noam, et al.
(författare)
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RawECGNet : Deep Learning Generalization for Atrial Fibrillation Detection From the Raw ECG
- 2024
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Ingår i: IEEE Journal of Biomedical and Health Informatics. - 2168-2194. ; , s. 1-10
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Tidskriftsartikel (refereegranskat)abstract
- Introduction Deep learning models for detecting episodes of atrial fibrillation (AF) using rhythm information in long-term ambulatory ECG recordings have shown high performance. However, the rhythm-based approach does not take advantage of the morphological information conveyed by the different ECG waveforms, particularly the f-waves. As a result, the performance of such models may be inherently limited. Methods: To address this limitation, we have developed a deep learning model, named RawECGNet, to detect episodes of AF and atrial flutter (AFl) using the raw, single-lead ECG. We compare the generalization performance of RawECGNet on two external data sets that account for distribution shifts in geography, ethnicity, and lead position. RawECGNet is further benchmarked against a state-of-the-art deep learning model, named ArNet2, which utilizes rhythm information as input. Results: Using RawECGNet, the results for the different leads in the external test sets in terms of the F1 score were 0.91–0.94 in RBDB and 0.93 in SHDB, compared to 0.89–0.91 in RBDB and 0.91 in SHDB for ArNet2. The results highlight RawECGNet as a high-performance, generalizable algorithm for detection of AF and AFl episodes, exploiting information on both rhythm and morphology.
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| 3. |
- Biton, Shany, et al.
(författare)
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Estimation of f-wave Dominant Frequency Using a Voting Scheme
- 2022
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Ingår i: Computing in Cardiology, CinC 2022. - 9798350300970
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Konferensbidrag (refereegranskat)abstract
- Atrial fibrillation (AF) is the most common heart arrhythmia, characterized by the presence of fibrillatory waves (f-waves) in the ECG. We introduce a voting scheme to estimate the dominant atrial frequency (DAF) of f-waves. Methods: We analysed a subset of Holter recordings obtained from the University of Virginia AF Database. 100 Holter recordings with manually annotated AF events, resulting in a total 363 AF events lasting more than 1 min. The f-waves were extracted using four different template subtraction (TS) algorithms and the DAF was estimated from the first 1-min window of each AF event. A random forest classifier was used. We hypothesized that better extraction of the f-wave meant better AF/non-AF classification using the DAF as the single input feature of the RF model. Results: Performance on the test set, expressed in terms of AF/non-AF classification, was the best when the DAF was computed computed the three best-performing extraction methods. Using these three algorithms in a voting scheme, the classifier obtained AUC=0.60 and the DAFs were mostly spread around 6 Hz, 5.66 (4.83-7.47). Conclusions: This study has two novel contributions: (1) a method for assessing the performance of f-wave extraction algorithms, and (2) a voting scheme for improved DAF estimation.
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| 4. |
- Butkuviene, Monika, et al.
(författare)
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Atrial Fibrillation Episode Patterns and Their Influence on Detection Performance
- 2021
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Ingår i: 2021 Computing in Cardiology, CinC 2021. - 2325-887X .- 2325-8861. - 9781665479165 ; 2021-September
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Konferensbidrag (refereegranskat)abstract
- Existing studies offer little insight on how atrial fibrillation (AF) detection performance is influenced by the properties of AF episode patterns. The aim of this study is to investigate the influence of AF burden and median AF episode length on detection performance. For this purpose, three types of AF detectors, using either information on rhythm, rhythm and morphology, or ECG segments, were investigated on 1-h simulated ECGs. Comparing AF burdens of 20% and 80% for a median episode length of 167 beats, the sensitivity of the rhythm- and morphology-based detector increases only slightly whereas the specificity drops from 99.5% to 93.3%. The corresponding figures of specificity are 99.0% and 90.6% for the rhythm-based detector; 88.1% and 70.7% for the segment-based detector. The influence of AF burden on specificity becomes even more pronounced for AF patterns with brief episodes (median episode length set to 30 beats). Therefore, patterns with briefepisodes and high AF burden imply higher demands on detection performance. Future research should focus on how well episode patterns are captured.
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| 5. |
- Butkuviene, Monika, et al.
(författare)
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Characterization of Atrial Fibrillation Episode Patterns : A Comparative Study
- 2024
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Ingår i: IEEE Transactions on Biomedical Engineering. - 0018-9294. ; 71:1, s. 106-113
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Tidskriftsartikel (refereegranskat)abstract
- Objective: The episode patterns of paroxysmal atrial fibrillation (AF) may carry important information on disease progression and complication risk. However, existing studies offer very little insight into to what extent a quantitative characterization of AF patterns can be trusted given the errors in AF detection and various types of shutdown, i.e., poor signal quality and non-wear. This study explores the performance of AF pattern characterizing parameters in the presence of such errors. Methods: To evaluate the performance of the parameters AF aggregation and AF density, both previously proposed to characterize AF patterns, the two measures mean normalized difference and the intraclass correlation coefficient are used to describe agreement and reliability, respectively. The parameters are studied on two PhysioNet databases with annotated AF episodes, also accounting for shutdowns due to poor signal quality. Results: The agreement is similar for both parameters when computed for detector-based and annotated patterns, which is 0.80 for AF aggregation and 0.85 for AF density. On the other hand, the reliability differs substantially, with 0.96 for AF aggregation but only 0.29 for AF density. This finding suggests that AF aggregation is considerably less sensitive to detection errors. The results from comparing three strategies to handle shutdowns vary considerably, with the strategy that disregards the shutdown from the annotated pattern showing the best agreement and reliability. Conclusions: Due to its better robustness to detection errors, AF aggregation should be preferred. To further improve performance, future research should put more emphasis on AF pattern characterization.
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| 6. |
- Butkuviene, Monika, et al.
(författare)
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Considerations on Performance Evaluation of Atrial Fibrillation Detectors
- 2021
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Ingår i: IEEE Transactions on Biomedical Engineering. - 0018-9294. ; 68:11, s. 3250-3260
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Tidskriftsartikel (refereegranskat)abstract
- Objective: A large number of atrial fibrillation (AF) detectors have been published in recent years, signifying that the comparison of detector performance plays a central role, though not always consistent. The aim of this study is to shed needed light on aspects crucial to the evaluation of detection performance. Methods: Three types of AF detector, using either information on rhythm, rhythm and morphology, or segments of ECG samples, are implemented and studied on both real and simulated ECG signals. The properties of different performance measures are investigated, for example, in relation to dataset imbalance. Results: The results show that performance can differ considerably depending on the way detector output is compared to database annotations, i.e., beat-to-beat, segment-to-segment, or episode-to-episode comparison. Moreover, depending on the type of detector, the results substantiate that physiological and technical factors, e.g., changes in ECG morphology, rate of atrial premature beats, and noise level, can have a considerable influence on performance. Conclusion: The present study demonstrates overall strengths and weaknesses of different types of detector, highlights challenges in AF detection, and proposes five recommendations on how to handle data and characterize performance.
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| 7. |
- Halvaei, Hesam, et al.
(författare)
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Detection of Non-Sustained Supraventricular Tachycardia in Atrial Fibrillation Screening
- 2024
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Ingår i: IEEE Journal of Translational Engineering in Health and Medicine. - 2168-2372.
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Non-sustained supraventricular tachycardia (nsSVT) is associated with a higher risk of developing atrial fibrillation (AF), and, therefore, detection of nsSVT can improve AF screening efficiency. However, the detection is challenged by the lower signal quality of ECGs recorded using handheld devices and the presence of ectopic beats which may mimic the rhythm characteristics of nsSVT. Methods: The present study introduces a new nsSVT detector for use in single-lead, 30-s ECGs, based on the assumption that beats in an nsSVT episode exhibits similar morphology, implying that episodes with beats of deviating morphology, either due to ectopic beats or noise/artifacts, are excluded. A support vector machine is used to classify successive 5-beat sequences in a sliding window with respect to similar morphology. Due to the lack of adequate training data, the classifier is trained using simulated ECGs with varying signal-to-noise ratio. In a subsequent step, a set of rhythm criteria is applied to similar beat sequences to ensure that episode duration and heart rate is acceptable. Results: The performance of the proposed detector is evaluated using the StrokeStop II database, resulting in sensitivity, specificity, and positive predictive value of 84.6%, 99.4%, and 18.5%, respectively. Conclusion: The results show that a significant reduction in expert review burden (factor of 6) can be achieved using the proposed detector. Clinical and Translational Impact: The reduction in the expert review burden shows that nsSVT detection in AF screening can be made considerably more efficiently.
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| 8. |
- Halvaei, Hesam, et al.
(författare)
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Detection of Short Supraventricular Tachycardias in Single-lead ECGs Recorded Using a Handheld Device
- 2022
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Ingår i: Computing in Cardiology, CinC 2022. - 9798350300970
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Konferensbidrag (refereegranskat)abstract
- Short supraventricular tachycardias (S-SVTs) have been associated with a higher risk of developing atrial fibrillation (AF). Hence, identification of participants with such arrhythmias may increase the yield of AF screening. However, the lower signal quality of ECGs recorded using handheld screening devices challenges the detection of S-SVT. In the present work, a new method for detection of S-SVT is presented, which is based on the requirement on morphologic similarity between the detected beats. Specifically, any episode with a sequence of beats of similar morphology is considered as an S-SVT candidate while any episode with detections of different morphology, either due to signal disturbances or aberrant ectopic beats, is excluded. For this purpose, a support vector machine (SVM) was trained and validated, using a simulated ECG database, to classify an episode as either consisting of beats of similar or non-similar morphologies. Episodes identified as S-SVT candidates are subject to two further rhythm criteria in order to confirm the presence of an S-SVT. The performance of the S-SVT detector is evaluated using a subset of the StrokeStop I database (305 S-SVT out of 8258), resulting in a sensitivity, specificity, and positive predictive value of 88.8%, 92.0%, and 29.9%, respectively. In conclusion, the results suggest that the detection of S-SVT in AF screening can be done at an acceptable balance between sensitivity and positive predictive value.
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| 9. |
- Henriksson, Mikael, et al.
(författare)
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Modeling and Estimation of Temporal Episode Patterns in Paroxysmal Atrial Fibrillation
- 2021
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Ingår i: IEEE Transactions on Biomedical Engineering. - 0018-9294. ; 68:1, s. 319-329
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Tidskriftsartikel (refereegranskat)abstract
- Objective: The present study proposes a model-based, statistical approach to characterizing episode patterns in paroxysmal atrial fibrillation (AF). Thanks to the rapid advancement of noninvasive monitoring technology, the proposed approach should become increasingly relevant in clinical practice. Methods: History-dependent point process modeling is employed to characterize AF episode patterns, using a novel alternating, bivariate Hawkes self-exciting model. In addition, a modified version of a recently proposed statistical model to simulate AF progression throughout a lifetime is considered, involving non-Markovian rhythm switching and survival functions. For each model, the maximum likelihood estimator is derived and used to find the model parameters from observed data. Results: Using three databases with a total of 59 long-term ECG recordings, the goodness-of-fit analysis demonstrates that the proposed alternating, bivariate Hawkes model fits SR-to-AF transitions in 40 recordings and AF-to-SR transitions in 51; the corresponding numbers for the AF model with non-Markovian rhythm switching are 40 and 11, respectively. Moreover, the results indicate that the model parameters related to AF episode clustering, i.e., aggregation of temporal AF episodes, provide information complementary to the well-known clinical parameter AF burden. Conclusion: Point process modeling provides a detailed characterization of the occurrence pattern of AF episodes that may improve the understanding of arrhythmia progression.
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| 10. |
- Kontaxis, Spyridon, et al.
(författare)
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Investigating Respiratory Rate Estimation during Paroxysmal Atrial Fibrillation Using an Improved ECG Simulation Model
- 2020
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Ingår i: 2020 Computing in Cardiology, CinC 2020. - 2325-887X .- 2325-8861. - 9781728173825 ; 2020-September
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Konferensbidrag (refereegranskat)abstract
- The present study addresses the problem of respiratory rate estimation from ECG-derived respiration (EDR) signals during paroxysmal atrial fibrillation (AF). Novel signal-to-noise ratios between various components of the ECG including the influence of respiration, measured by QRS ensemble variance, the amplitude of fibrillatory waves (f-waves), and the QRS amplitude are introduced to characterize EDR performance. Using an improved ECG simulation model accounting for morphological variation induced by respiration, the results show that 1. the error in estimating the respiratory rate increases as a function of the time spent in AF, 2. the leads farthest away from the atria, i.e., V_{4}, V_{5}, V_{6}, exhibit the best performance due to lower f-wave amplitudes, 3. lower errors in leads with similar f-wave amplitude are due to a more pronounced respiratory influence, and 4. the respiratory influence is higher in V_{2}, V_{3}, and V_{4} compared to other precordial leads.
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| 11. |
- Martin-Yebra, Alba, et al.
(författare)
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Characterization of Atrial Fibrillation Episodes Using a Point Process Model
- 2020
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Ingår i: 2020 11th Conference of the European Study Group on Cardiovascular Oscillations : Computation and Modelling in Physiology: New Challenges and Opportunities, ESGCO 2020 - Computation and Modelling in Physiology: New Challenges and Opportunities, ESGCO 2020. - 9781728157511
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Konferensbidrag (refereegranskat)abstract
- The purpose of the present study is to introduce a point process model for characterizing the pattern of atrial fibrillation (AF) episodes. A variant of the bivariate Hawkes process is proposed, accounting for clustered episodes. The model parameters are inferred by the maximum likelihood method. The goodness-of-fit analysis show that model fits the data in most of the recordings (27 out of 32). The information provided by this approach is complementary to AF burden.
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| 12. |
- Martin-Yebra, Alba, et al.
(författare)
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Model-Based Characterization of Atrial Fibrillation Episodes and its Clinical Association
- 2020
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Ingår i: 2020 Computing in Cardiology, CinC 2020. - 2325-887X .- 2325-8861. - 9781728173825 ; 2020-September
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Konferensbidrag (refereegranskat)abstract
- Studies investigating risk factors associated with atrial fibrillation (AF) have mostly focused on AF presence and burden, disregarding the temporal distribution of AF episodes although such information can be relevant. In the present study, the alternating, bivariate Hawkes model was used to characterize paroxysmal AF episode patterns. Two parameters: the intensity ratio µ, describing the dominating rhythm (AF or non-AF) and the exponential decay ß 1, providing information on clustering, were investigated in relation to AF burden and atrial echocardiographic measurements. Both µ and ß1were weakly correlated with atrial volume (r=0.19 and r=0.34, respectively), whereas µ was correlated with atrial strain (r=-0.74, p=0.1) and AF burden (r=0.68, p=0.05). Weak correlation between ß1 and AF burden was found (r=0.29). Atrial structural remodeling is associated with changes in AF characteristics, often manifested as episodes of increasing duration, thus µ may reflect the degree of atrial electrical and structural remodeling. Moreover, clustering information (ß1) is complementary information to AF burden, which may be useful for understanding arrhythmia progression and risk assessment of ischemic stroke.
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| 13. |
- Martin-Yebra, Alba, et al.
(författare)
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QT interval Adaptation to Heart Rate Changes in Atrial Fibrillation as a Predictor of Sudden Cardiac Death
- 2022
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Ingår i: IEEE Transactions on Biomedical Engineering. - 0018-9294. ; 69:10, s. 3109-3118
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Tidskriftsartikel (refereegranskat)abstract
- Objective: The clinical significance of QT interval adaptation to heart rate changes has been poorly investigated in atrial fibrillation (AF), since QT delineation in the presence of f-waves is challenging. Therefore, the objective of the present study is to investigate new techniques for QT adaptation estimation in permanent AF. Methods: A multilead strategy based on generalized periodic component analysis is proposed for QT delineation, involving a spatial, linear transformation which emphasizes Twave periodicity and attenuates f-waves. QT adaptation is modeled by a linear, time-invariant filter, whose impulse response describes the dependence between the current QT interval and the preceding RR intervals, followed by a memoryless, possibly nonlinear, function. The QT adaptation time lag is determined from the estimated impulse response. Results: Using simulated ECGs in permanent AF, the transformed lead was found to offer more accurate QT delineation and time lag estimation than did the original ECG leads for a wide range of f-wave amplitudes (the time lag estimation error was found to be -0.2+/-0.6 s for SNR = 12 dB). In a population with chronic heart failure and permanent AF, the time lag estimated from the transformed lead was found to have the strongest, statistically significant association with sudden cardiac death (SCD) (hazard ratio = 3.49), whereas none of the original, orthogonal leads had any such association. Conclusions: Periodic component analysis provides more accurate QT delineation and improves time lag estimation in AF. A prolonged adaptation time of the QT interval to heart rate changes is associated with a high risk for SCD. Significance: This study demonstrates that SCD risk markers, originally developed for sinus rhythm, can also be used in AF, provided that Twave periodicity is emphasized. The time lag is a potentially useful marker for identifying patients at high risk for SCD, guiding clinicians in adopting effective therapeutic decisions.
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| 14. |
- Perez, Cristina, et al.
(författare)
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Evaluation of a QT Adaptation Time Estimator for ECG Exercise Stress Test in Controlled Simulation
- 2023
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Ingår i: Computing in Cardiology, CinC 2023. - 2325-887X .- 2325-8861. - 9798350382525
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Konferensbidrag (refereegranskat)abstract
- Slowed adaptation of the QT interval to sudden abrupt heart rate (HR) changes has been identified as a marker of ventricular arrhythmic risk. However, abrupt HR changes are difficult to induce in patients. Quantifying the QT adaptation time in gradual HR changes, as observed in ECGs recording during an exercise stress test, has been recently proposed. The time lag between the QT series and an instantaneous memoryless HR-dependent QT series along stress test was computed as QT memory. Here, this method was evaluated in a control scenario using simulated exercise stress test ECG signals presenting different QT adaptation times. The method robustness was studied by contaminating the ECGs with muscular noise (MN) signals with different Signal-to-Noise ratio (SNR) values, either synthetic or extracted from real recordings. We found that delineation of the T-wave end point in the first transformed lead from Periodic Component Analysis offers the best performance for low SNR. Moreover, we confirmed that the estimator provides an unbiased estimate of the QT memory introduced in the simulations for the studied range of SNR values (25 to 50 dB).
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| 15. |
- Plusciauskaite, Vilma, et al.
(författare)
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Detection of Pre-and Post-Trigger Atrial Fibrillation in Long-Term Photoplethysmogram Signals Acquired in Free-Living
- 2023
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Ingår i: Computing in Cardiology, CinC 2023. - 9798350382525
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Konferensbidrag (refereegranskat)abstract
- Modifiable factors, such as alcohol or physical exertion, may trigger atrial fibrillation (AF) episodes. Identifying and eliminating these triggers can lead to effective strategies which reduce risk of AF recurrence. This study aims to evaluate pre-and post-trigger AF in long-term photo-plethysmogram (PPG) signals obtained during daily living from patients with paroxysmal AF. Thirty-seven patients were instructed to wear a wrist-worn device for a week. They were also asked to log suspected triggers using a smartphone app. Of these patients, 15 experienced AF episodes, resulting in an average AF burden of 0.15. The results indicate that longer post-trigger analysis time intervals resulted in better performance of PPG-based AF detection. The sensitivity was highest for the 16-h post-trigger interval (0.76) and lowest for the 4-h interval (0.43). In contrast, the specificity slightly decreased with an increasing longer post-trigger analysis time interval, being 0.98 and 0.95 for the 4-h and 16-h intervals, respectively. The PPG-based post-trigger AF burden was approximately half of that determined by the annotated ECG-based AF pattern. The study suggests that long-term PPG-based monitoring is a suitable alternative for detecting post-trigger AF instead of ECG-based. However, the accuracy of AF burden estimation using PPG-based technology still calls for improvement.
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| 16. |
- Plusciauskaite, Vilma, et al.
(författare)
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Modeling of the Effect of Alcohol on Episode Patterns in Atrial Fibrillation
- 2022
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Ingår i: 2022 Computing in Cardiology, CinC 2022. - 2325-887X .- 2325-8861. - 9798350300970 ; 2022-September
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Konferensbidrag (refereegranskat)abstract
- Growing evidence shows that alcohol triggers paroxysmal atrial fibrillation (PAF) in some patients. How-ever, there is a lack of methods for assessing the causality between triggers and atrial fibrillation (AF) episodes. Accordingly, this work aims to develop an approach to episode modeling under the influence of alcohol for the purpose of evaluating causality assessment methods. The alternating, bivariate Hawkes model is used to produce episode patterns, where the conditional intensity function λ1(t) defines the transitions from sinus rhythm (SR) to AF. The effect of alcohol consumption is characterized by a body reactivity function, defined by the base intensity μ1(t), which alters λ1(t). Different AF episode patterns were modeled for alcohol units ranging from 0 to 15. The mean AF burden without alcohol was 17.2%, which doubled with 9 alcohol units; the number of AF episodes doubled from 12.9 with 8 alcohol units. The aggregation of AF episodes tended to decrease after 6 alcohol units. The proposed model of alcohol-affected PAF patterns may be useful for assessing the methods for evaluation of causality between triggers and PAF occurrence.
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| 17. |
- Sornmo, Leif, et al.
(författare)
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Spectral Analysis of Heart Rate Variability in Time-Varying Conditions and in the Presence of Confounding Factors
- 2022
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Ingår i: IEEE Reviews in Biomedical Engineering. - 1937-3333. ; , s. 1-21
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Tidskriftsartikel (refereegranskat)abstract
- The tools for spectrally analyzing heart rate variability (HRV) has in recent years grown considerably, with emphasis on the handling of time-varying conditions and confounding factors. Time–frequency analysis holds since long an important position in HRV analysis, however, this technique cannot alone handle a mean heart rate or a respiratory frequency which vary over time. Overlapping frequency bands represents another critical condition which needs to be dealt with to produce accurate spectral measurements. The present survey offers a comprehensive account of techniques designed to handle such conditions and factors by providing a brief description of the main principles of the different methods. Several methods derive from a mathematical/statistical model, suggesting that the model can be used to simulate data used for performance evaluation. The inclusion of a respiratory signal, whether measured or derived, is another feature of many recent methods, e.g., used to guide the decomposition of the HRV signal so that signals related as well as unrelated to respiration can be analyzed. It is concluded that the development of new approaches to handling time-varying scenarios are warranted, as is benchmarking of performance evaluated in technical as well as in physiological/clinical terms.
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| 18. |
- Vraka, Aikaterini, et al.
(författare)
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Alternative Time-Domain P-wave Analysis for Precise Information on Substrate Alteration after Pulmonary Vein Isolation for Atrial Fibrillation
- 2021
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Ingår i: 2021 9th E-Health and Bioengineering Conference, EHB 2021. - 9781665440004
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Konferensbidrag (refereegranskat)abstract
- While P-wave duration (PWD) is primarily employed to observe the atrial substrate alterations after pulmonary vein isolation (PVI) on atrial fibrillation (AF) patients, the acquired information corresponds to the entire atria. Left (LA) and right atrium (RA), though, may be differently affected by PVI, implying the need for different after-PVI handling. In order to clarify this assumption, five-minute lead II recordings from 29 paroxysmal AF patients undergoing first-ever PVI were recruited before and after PVI. PWD was analyzed integrally and in parts, with the first part (PWD1) from the onset to the peak corresponding to RA and the second part (PWD2) to LA depolarization. Time from P-wave onset or offset to the R peak were also calculated (Pon - R and Poff - R, respectively). Normalization (N) to mitigate heart-rate effect was applied. Results before and after PVI were compared with Mann-Whitney U-test (MWU). Median values and variations due to PVI were calculated for all features and compared between PWD and the remaining features via Pearson correlation. After PVI, PWD (-9.84%, p = 0.0085, N: - 17.96%, p = 0.0442) and PWD2 (-22.03%, p = 0.0250, N: - 27.77%, p = 0.0268) were significantly decreased. PWD1 did not shorten significantly (up to -8.96%, p > 0.05 at either cases). PWD - PWD1 (ρ > 74.5%, p < 0.0001) showed higher correlation than PWD - PWD2 (ρ > 41.9%, p < 0.0001) in before and after PVI analysis but not for PVI-related variation (ρPWD-PWD1 = 54.0%, p = 0.0114 and ρPWD-PWD2 = 61.4%, p = 0.0031). While RA depolarization time is more in line with PWD analysis, the effect of PVI in PWD is more coherent with LA's findings. Additionally, PWD shortening is only observed in the LA. Therefore, LA is crucial for the assessment of the atrial substrate alteration after PVI and its analysis should be considered by future studies.
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| 19. |
- Vraka, Aikaterini, et al.
(författare)
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The P-Wave Time-Domain Significant Features to Evaluate Substrate Modification After Catheter Ablation of Paroxysmal Atrial Fibrillation
- 2022
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Ingår i: 2022 Computing in Cardiology, CinC 2022. - 2325-887X .- 2325-8861. - 9798350300970 ; 2022-September
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Konferensbidrag (refereegranskat)abstract
- The outcome of catheter ablation (CA) of atrial fibrillation (AF) is vastly analyzed by the entire P-wave duration (PWD). However, the first and second P-wave parts, corresponding to right (RA) and left atrial (LA) wavefront propagation, may be unequally modified. Five-minute lead II recordings before and after the first-ever CA of 40 parox-ysmal AF patients were analyzed and P-wave features were calculated: PWDon-off of the entire P-wave and each P-wave part (RA:PWDon-peak, LA:PWDpeak-off) and the time from P-wave onset or offset to the R-peak (PWDon-R and PWDoff-R, respectively). Heart-rate (HR) adjustment (HRA) mitigated the HR fluctuations. Prelpost-CA comparison was performed with Mann-Whitney U-test and median values were calculated. Pearson's correlation was calculated between PWD and the remaining features. The effect of CA with (Δ: -17.96%) or without HRA (Δ: -9.84%) was significant at the entire PWDon-off and at the PWDpeak-off(HRA:Δ: -27.77%, no HRA: Δ: -22.03%). PWDon-off showed a stronger correlation with RA than LA(ρmax=0.805 vs ρmax=0.541). P-wave features corresponding to RA are more strongly related to the entire P-wave. Nevertheless, only the P-wave part associated with LA is significantly affected by CA. That being so, studies are encouraged to incorporate part-time P-wave analysis.
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