Extraction of f Waves

Sörnmo, Leif; Petrėnas, Andrius; Laguna, Pablo; Marozas, Vaidotas

doi:10.1007/978-3-319-68515-1_5

Cited by 6 publications

(6 citation statements)

References 117 publications

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“…Thus, the QRS-T complex was absent from our simulated signals. Nevertheless, the removal of QRS-T complex and its replacement with a sigmoid function, as done here, or other forms of QRS-T cancellation 54 has proven to robustly extract the f-wave component from the clinical 12-lead ECG, so that the signals can be analyzed by our classifier without relevant disturbance from ventricle activity. Further refinements of the synthetic dataset could focus on including heterogeneous atrial wall thickness 55 , 56 or extending the dataset to cover even more anatomical torso, atrial, 57 and conduction velocity variability.…”

Section: Discussionmentioning

confidence: 99%

Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG

Luongo

Azzolin

Schuler

et al. 2021

Cardiovascular Digital Health Journal

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Background Atrial fibrillation (AF) is the most common supraventricular arrhythmia, characterized by disorganized atrial electrical activity, maintained by localized arrhythmogenic atrial drivers. Pulmonary vein isolation (PVI) allows to exclude PV-related drivers. However, PVI is less effective in patients with additional extra-PV arrhythmogenic drivers. Objectives To discriminate whether AF drivers are located near the PVs vs extra-PV regions using the noninvasive 12-lead electrocardiogram (ECG) in a computational and clinical framework, and to computationally predict the acute success of PVI in these cohorts of data. Methods AF drivers were induced in 2 computerized atrial models and combined with 8 torso models, resulting in 1128 12-lead ECGs (80 ECGs with AF drivers located in the PVs and 1048 in extra-PV areas). A total of 103 features were extracted from the signals. Binary decision tree classifier was trained on the simulated data and evaluated using hold-out cross-validation. The PVs were subsequently isolated in the models to assess PVI success. Finally, the classifier was tested on a clinical dataset (46 patients: 23 PV-dependent AF and 23 with additional extra-PV sources). Results The classifier yielded 82.6% specificity and 73.9% sensitivity for detecting PV drivers on the clinical data. Consistency analysis on the 46 patients resulted in 93.5% results match. Applying PVI on the simulated AF cases terminated AF in 100% of the cases in the PV class. Conclusion Machine learning–based classification of 12-lead-ECG allows discrimination between patients with PV drivers vs those with extra-PV drivers of AF. The novel algorithm may aid to identify patients with high acute success rates to PVI.

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Section: Discussionmentioning

confidence: 99%

Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG

Luongo

Azzolin

Schuler

et al. 2021

Cardiovascular Digital Health Journal

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“…Since the focus is the estimation of τ , the SNR was defined by the QRS-and f-wave amplitudes, cf. (20), rather than by the f-wave amplitude and the noise level. Since the noise level was held fixed at 20 µV for all simulations, a future study may investigate how performance is influenced by higher noise levels.…”

Section: Discussionmentioning

confidence: 99%

“…Due to the presence of f-waves in certain ECG leads, especially V1 and V2, delineation of the QT interval in AF recordings may be a challenging problem [20]. Most clinical studies have bypassed this challenge by simply analyzing a lead with negligible f-wave presence.…”

Section: Introductionmentioning

confidence: 99%

QT Interval Adaptation to Heart Rate Changes in Atrial Fibrillation as a Predictor of Sudden Cardiac Death

Martín-Yebra

Sörnmo

Laguna³

2022

IEEE Trans. Biomed. Eng.

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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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“…2) Suppression of f-waves: In contrast to AF signal processing, where the objective is to extract and analyze the fwaves, the objective is here to suppress the f-waves, while preserving respiratory related QRS variability, before respiratory rate estimation is performed. Signal-and noise-dependent weighted QRS averaging is used for this objective [26], see also [27], being based on the same principle as average beat subtraction (ABS). The i:th observed beat x i (n) is modeled as:…”

Section: A Preprocessingmentioning

confidence: 99%

“…The f-wave variance σ 2 f is estimated from the observed signal by computing the sample variance of the concatenated TQ intervals of all beats, following bandpass filtering in the band [0. 5,30] Hz to preserve f-wave related frequency components and reduce the influence of noise [27]. The QRS variance σ 2 QRS (n) is estimated bŷ…”

Section: A Preprocessingmentioning

confidence: 99%

ECG-Derived Respiratory Rate in Atrial Fibrillation

Kontaxis

Lázaro

Corino

et al. 2020

IEEE Trans. Biomed. Eng.

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The present study addresses the problem of estimating the respiratory rate from the morphological ECG variations in the presence of atrial fibrillatory waves (f-waves). The significance of performing f-wave suppression before respiratory rate estimation is investigated. Methods: The performance of a novel approach to ECG-derived respiration, named "slope range" (SR) and designed particularly for operation in atrial fibrillation (AF), is compared to that of two well-known methods based on either R-wave angle (RA) or QRS loop rotation angle (LA). A novel rule is proposed for spectral peak selection in respiratory rate estimation. The suppression of f-waves is accomplished using signal-and noise-dependent QRS weighted averaging. The performance evaluation embraces real as well as simulated ECG signals acquired from patients with persistent AF; the estimation error of the respiratory rate is determined for both types of signals. Results: Using real ECG signals and reference respiratory signals, rate estimation without f-wave suppression resulted in a median error of 0.015±0.021 Hz and 0.019±0.025 Hz for SR and RA, respectively, whereas LA with f-wave suppression resulted in 0.034±0.039 Hz. Using simulated signals, the results also demonstrate that f-wave suppression is superfluous for SR and RA, whereas it is essential for LA. Conclusion: The results show that SR offers the best performance as well as computational simplicity since f-wave suppression is not needed. Significance: The respiratory rate can be robustly estimated from the ECG in the presence of AF.

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Extraction of f Waves

Cited by 6 publications

References 117 publications

Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG

Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG

QT Interval Adaptation to Heart Rate Changes in Atrial Fibrillation as a Predictor of Sudden Cardiac Death

ECG-Derived Respiratory Rate in Atrial Fibrillation

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