Signal Quality Assessment of F-waves in Atrial Fibrillation

Introduction: Information about autonomic nervous system (ANS) activity may offer insights about atrial fibrillation (AF) progression and support personalized AF treatment but is not easily accessible from the ECG. In this study, we propose a new approach for ECG-based assessment of respiratory modulation in atrioventricular (AV) nodal refractory period and conduction delay.Methods: A 1-dimensional convolutional neural network (1D-CNN) was trained to estimate respiratory modulation of AV nodal conduction properties from 1-minute segments of RR series, respiration signals, and atrial fibrillatory rates (AFR) using synthetic data that replicates clinical ECG-derived data. The synthetic data were generated using a network model of the AV node and 4 million unique model parameter sets. The 1D-CNN was then used to analyze respiratory modulation in clinical deep breathing test data of 28 patients in AF, where an ECG-derived respiration signal was extracted using a novel approach based on periodic component analysis.Results: We demonstrated using synthetic data that the 1D-CNN can estimate the respiratory modulation from RR series alone with a Pearson sample correlation of r = 0.805 and that the addition of either respiration signal (r = 0.830), AFR (r = 0.837), or both (r = 0.855) improves the estimation.Discussion: Initial results from analysis of ECG data suggest that our proposed estimate of respiration-induced autonomic modulation, aresp, is reproducible and sufficiently sensitive to monitor changes and detect individual differences. However, further studies are needed to verify the reproducibility, sensitivity, and clinical significance of aresp.

Section: Estimation Of Atrial Fibrillatory Ratementioning

confidence: 99%

ECG-based estimation of respiration-induced autonomic modulation of AV nodal conduction during atrial fibrillation

Plappert,

Engström,

Platonov

et al. 2024

“…For the estimation of a high-resolution f-wave frequency trend, a harmonic f-wave model (Henriksson et al, 2018) is employed. The model f-wave signal is defined as the sum of a complex exponential signal with fundamental frequency f and its second harmonic,…”

Section: Estimation Of F-wave Frequency Trendmentioning

confidence: 99%

“…where A m and ϕ m denote the amplitude and phase of m:th harmonic, respectively, and f s is sampling frequency. The use of two harmonics in the model is motivated by the observations in (Henriksson et al, 2018), that additional harmonic results in more noise due to the additional degrees of freedom of this model. The parameters θ [f A 1 A 2 ϕ 1 ϕ 2 ] T , are estimated by fitting the harmonic model s (n; θ) to the analytic equivalent of x(n), denoted x a (n), using maximum likelihood approach.…”

Section: Estimation Of F-wave Frequency Trendmentioning

confidence: 99%

“…S ranges from 0 to 1, where a higher value corresponds to a better fit. Only segments with S > 0.3 is considered for further analysis, since previous studies has shown that S larger than 0.3 was sufficient for accurate estimation of f(n) (Henriksson et al, 2018). The atrial fibrillatory rate (AFR) is estimated by the median of f(n).…”

Section: Estimation Of F-wave Frequency Trendmentioning

confidence: 99%

See 1 more Smart Citation

A subspace projection approach to quantify respiratory variations in the f-wave frequency trend

Abdollahpur

Engström²,

Platonov

et al. 2022

Self Cite

Background: The autonomic nervous system (ANS) is known as a potent modulator of the initiation and perpetuation of atrial fibrillation (AF), hence information about ANS activity during AF may improve treatment strategy. Respiratory induced ANS variation in the f-waves of the ECG may provide such information.Objective: This paper proposes a novel approach for improved estimation of such respiratory induced variations and investigates the impact of deep breathing on the f-wave frequency in AF patients.Methods: A harmonic model is fitted to the f-wave signal to estimate a high-resolution f-wave frequency trend, and an orthogonal subspace projection approach is employed to quantify variations in the frequency trend that are linearly related to respiration using an ECG-derived respiration signal. The performance of the proposed approach is evaluated and compared to that of a previously proposed bandpass filtering approach using simulated f-wave signals. Further, the proposed approach is applied to analyze ECG data recorded for 5 min during baseline and 1 min deep breathing from 28 AF patients from the Swedish cardiopulmonary bioimage study (SCAPIS).Results: The simulation results show that the estimates of respiratory variations obtained using the proposed approach are more accurate than estimates obtained using the previous approach. Results from the analysis of SCAPIS data show no significant differences between baseline and deep breathing in heart rate (75.5 ± 22.9 vs. 74 ± 22.3) bpm, atrial fibrillation rate (6.93 ± 1.18 vs. 6.94 ± 0.66) Hz and respiratory f-wave frequency variations (0.130 ± 0.042 vs. 0.130 ± 0.034) Hz. However, individual variations are large with changes in heart rate and atrial fibrillatory rate in response to deep breathing ranging from −9% to +5% and −8% to +6%, respectively and there is a weak correlation between changes in heart rate and changes in atrial fibrillatory rate (r = 0.38, p < 0.03).Conclusion: Respiratory induced f-wave frequency variations were observed at baseline and during deep breathing. No significant changes in the magnitude of these variations in response to deep breathing was observed in the present study population.

“…The atrial features are primarily based on the P-wave disappearance or f-waves appearance. Typical methods include the wavelet energy method (Garcia et al, 2016;Serhal et al, 2022), the frequency and amplitude features of the f-wave (Henriksson et al, 2018), and the time between P-waves as a measure of the atrial rate (Huang et al, 2020). The ventricular features mainly describe irregularity of intervals between subsequent R-peaks (R-R intervals).…”

Section: Introductionmentioning

confidence: 99%

Atrial fibrillation classification based on the 2D representation of minimal subset ECG and a non-deep neural network

Zhang

Liu²,

Tang³

et al. 2023

Atrial fibrillation (AF) is the most common cardiac arrhythmia, and its early detection is critical for preventing complications and optimizing treatment. In this study, a novel AF prediction method is proposed, which is based on investigating a subset of the 12-lead ECG data using a recurrent plot and ParNet-adv model. The minimal subset of ECG leads (II &V1) is determined via a forward stepwise selection procedure, and the selected 1D ECG data is transformed into 2D recurrence plot (RP) images as an input to train a shallow ParNet-adv Network for AF prediction. In this study, the proposed method achieved F1 score of 0.9763, Precision of 0.9654, Recall of 0.9875, Specificity of 0.9646, and Accuracy of 0.9760, which significantly outperformed solutions based on single leads and complete 12 leads. When studying several ECG datasets, including the CPSC and Georgia ECG databases of the PhysioNet/Computing in Cardiology Challenge 2020, the new method achieved F1 score of 0.9693 and 0.8660, respectively. The results suggested a good generalization of the proposed method. Compared with several state-of-art frameworks, the proposed model with a shallow network of only 12 depths and asymmetric convolutions achieved the highest average F1 score. Extensive experimental studies proved that the proposed method has a high potential for AF prediction in clinical and particularly wearable applications.