Reference database and performance evaluation of methods for extraction of atrial fibrillatory waves in the ECG

Alcaraz, Raúl; Sörnmo, Leif; Rieta, J.J.

doi:10.1088/1361-6579/ab2b17

Cited by 6 publications

(6 citation statements)

References 48 publications

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“…A simulated database is used for the purpose of evaluating the performance of different f-wave extraction methods. This database was created, curated and presented by Alcaraz et al (Alcaraz et al 2019). The database is composed of eight sets of simulated ECG signals in AF, accounting for a wide range of major challenges in f-wave extraction including large variations in heart rate, large variations in QRST morphology (LVM), and the presence of ventricular premature beats (VPB).…”

Section: Databasementioning

confidence: 99%

“…Comparing the performance of different methods is made complicated by several factors, notably the number of ECG leads, large variations in heart rate, large variations in QRST morphology (LVM), ventricular premature beats (VPBs), noise and artifacts, and, in case of paroxysmal AF, 'episodes' of sinus rhythm (Alcaraz et al 2019). As the ECG is a signal resulting from a mixture of multiple electrical sources.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, having a reference database of simulated ECG signals along with associated ground truth f-waves is necessary for evaluating extraction performance. Thanks to the publicly available reference database in (Alcaraz et al 2019), performance evaluation has recently been made possible, with the inclusion of challenging situations.…”

Section: Introductionmentioning

confidence: 99%

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A comparative study of the performance of methods for f-wave extraction

et al. 2022

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Objective: This study proposes a novel technique for atrial fibrillatory waves (f-waves) extraction and investigates the performance of the proposed method comparing with different f-wave extraction methods. Approach: We propose a novel technique combining a periodic component analysis (PiCA) and echo state network (ESN) for f-waves extraction, denoted PiCA-ESN. PiCA-ESN benefits from the advantages of using both source separation and nonlinear adaptive filtering. PiCA-ESN is evaluated by comparing with other state-of-the-art approaches, which include template subtraction technique based on principal component analysis, spatiotemporal cancellation, nonlinear adaptive filtering using an echo state neural network, and a source separation technique based on PiCA. Quality assessment is performed on a recently published reference database including a large number of simulated ECG signals in atrial fibrillation (AF). The performance of the f-wave extraction methods is evaluated in terms of signal quality metrics (SNR, ΔSNR) and robustness of f-wave features. Main results: The proposed method offers the best signal quality performance, with a ΔSNR of approximately 22 dB across all 8 sets of the reference database, as well as the most robust extraction of f-wave features, with 75% of all estimates of dominant atrial frequency well below 1 Hz.

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Section: Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A comparative study of the performance of methods for f-wave extraction

et al. 2022

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“…By analyzing the frequency spectrum and amplitude of the F-wave, not only can we distinguish the type of AF but also judge whether AF relapses. However, how to extract F-waves from ECG becomes a difficult problem since F-waves overlap with QRST-wave in time and space [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

F-Wave Extraction from Single-Lead Electrocardiogram Signals with Atrial Fibrillation by Utilizing an Optimized Resonance-Based Signal Decomposition Method

Zhu

Kong

2022

Entropy

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(1) Background: A typical cardiac cycle consists of a P-wave, a QRS complex, and a T-wave, and these waves are perfectly shown in electrocardiogram signals (ECG). When atrial fibrillation (AF) occurs, P-waves disappear, and F-waves emerge. F-waves contain information on the cause of atrial fibrillation. Therefore it is essential to extract F-waves from the ECG signal. However, F-waves overlap the QRS complex and T-waves in both the time and frequency domain, causing this matter to be a difficult one. (2) Methods: This paper presents an optimized resonance-based signal decomposition method for detecting F-waves in single-lead ECG signals with atrial fibrillation (AF). It represents the ECG signal utilizing morphological component analysis as a linear combination of a finite number of components selected from the high-resonance and low-resonance dictionaries, respectively. The linear combination of components in the low-resonance dictionary reconstructs the oscillatory part (F-wave) of the ECG signal. In contrast, the linear combination of components in the high-resonance dictionary reconstructs the transient components part (QRST wave). The tunable Q-factor wavelet transform generates the high and low resonance dictionaries, with a high Q-factor producing a high resonance dictionary and a low Q-factor producing a low resonance dictionary. The different Q-factor settings affect the dictionaries’ characteristics, hence the F-wave extraction. A genetic algorithm was used to optimize the Q-factor selection to select the optimal Q-factor. (3) Results: The presented method helps reduce RMSE between the extracted and the simulated F-waves compared to average beat subtraction (ABS) and principal component analysis (PCA). According to the amplitude of the F-wave, RMSE is reduced by 0.24–0.32. Moreover, the dominant frequency of F-waves extracted by the presented method is clearer and more resistant to interference. The presented method outperforms the other two methods, ABS and PCA, in F-wave extraction from AF-ECG signals with the ventricular premature heartbeat. (4) Conclusion: The proposed method can potentially improve the accuracy of F-wave extraction for mobile ECG monitoring equipment, especially those with fewer leads.

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“…For instance, the simulator proposed for generating ECG signals accounts for atrial and ventricular activity morphologies during PAF and various types of noise encountered in ambulatory recordings [6]. The simulator was used to investigate algorithms for assessment of f-wave signal quality [7], estimate ECG-derived respiratory rate [8], f-wave extraction [9], and evaluate AF detectors [10].…”

Section: Introductionmentioning

confidence: 99%

Modeling of the Effect of Alcohol on Episode Patterns in Atrial Fibrillation

Pluščiauskaitė¹,

Rapalis²,

Butkuvienė³

et al. 2022

Computing in Cardiology Conference (CinC)

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Growing evidence shows that alcohol triggers paroxysmal atrial fibrillation (PAF) in some patients. However, 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 λ 1 (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 µ 1 (t), which alters λ 1 (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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Reference database and performance evaluation of methods for extraction of atrial fibrillatory waves in the ECG

Cited by 6 publications

References 48 publications

A comparative study of the performance of methods for f-wave extraction

A comparative study of the performance of methods for f-wave extraction

F-Wave Extraction from Single-Lead Electrocardiogram Signals with Atrial Fibrillation by Utilizing an Optimized Resonance-Based Signal Decomposition Method

Modeling of the Effect of Alcohol on Episode Patterns in Atrial Fibrillation

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