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

[EN] Objective: This study proposes a reference database, composed of a large number of simulated ECG signals in atrial fibrillation (AF), for investigating the performance of methods for extraction of atrial fibrillatory waves (f -waves). Approach: The simulated signals are produced using a recently published and validated model of 12-lead ECGs in AF. The database is composed of eight signal sets together accounting for a wide range of characteristics known to represent major challenges in f -wave extraction, including high heart rates, high morphological QRST variability, and the presence of ventricular premature beats. Each set contains 30 5 min signals with different f -wave amplitudes. The database is used for the purpose of investigating the statistical association between different indices, designed for use with either real or simulated signals. Main results: Using the database, available at the PhysioNet repository of physiological signals, the performance indices unnormalized ventricular residue (uVR), designed for real signals, and the root mean square error, designed for simulated signals, were found to exhibit the strongest association, leading to the recommendation that uVR should be used when characterizing performance in real signals. Significance: The proposed database facilitates comparison of the performance of different f -wave extraction methods and makes it possible to express performance in terms of the error between simulated and extracted f -wave signals.This work was supported by project DPI2017-83952-C3 of the Spanish Ministry of Economy, Industry and Competitiveness, project SBPLY/17/180501/000411 of the Junta de Comunidades de Castilla-La Mancha, Grant 'Jose Castillejo' (CAS17/00436) from the Spanish Ministry of Education, Culture and Sport, Grant No. BEST/2017/028 from the Education, Research, Culture and Sports Department of Generalitat Valenciana, European Regional Development Fund, and Grant No. 03382/2016 from the Swedish Research Council.Alcaraz, R.; Sornmo, L.; Rieta, JJ. (2019). Reference database and performance evaluation of methods for extraction of atrial fibrillatory waves in the ECG. Physiological Measurement. 40(7):1-11. https://doi.org/10.1088/1361-6579/ab2b17S111407Chugh, S. 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L., Lecannelier, E. A., Pino, E. J., & Rojas, A. J. (2013). Atrial activity selection for atrial fibrillation ECG recordings. Computers in Biology and Medicine, 43(10), 1628-1636. doi:10.1016/j.compbiomed.2013.08.002Fauchier, L., Villejoubert, O., Clementy, N., Bernard, A., Pierre, B., Angoulvant, D., … Lip, G. Y. H. (2016). Causes of Death and Influencing Factors in Patients with Atrial Fibrillation. The American Journal of Medicine, 129(12), 1278-1287. doi:10.1016/j.amjmed.2016.06.045Fujiki, A., Sakabe, M., Nishida, K., Mizumaki, K., & Inoue, H. (2003). Role of Fibrillation Cycle Length in Spontaneous and Drug-Induced Termination of Human Atrial Fibrillation. Circulation Journal, 67(5), 391-395. doi:10.1253/circj.67.391Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., … Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet. Circulation, 101(23). doi:10.1161/01.cir.101.23.e215Roonizi, E. K., & Sassi, R. (2017). 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Event synchronous adaptive filter based atrial activity estimation in single-lead atrial fibrillation electrocardiograms. Medical & Biological Engineering & Computing, 50(8), 801-811. doi:10.1007/s11517-012-0931-7Lemay, M., Vesin, J.-M., van Oosterom, A., Jacquemet, V., & Kappenberger, L. (2007). Cancellation of Ventricular Activity in the ECG: Evaluation of Novel and Existing Methods. IEEE Transactions on Biomedical Engineering, 54(3), 542-546. doi:10.1109/tbme.2006.888835Llinares, R., Igual, J., & Miró-Borrás, J. (2010). A fixed point algorithm for extracting the atrial activity in the frequency domain. Computers in Biology and Medicine, 40(11-12), 943-949. doi:10.1016/j.compbiomed.2010.10.006Malik, J., Reed, N., Wang, C.-L., & Wu, H. (2017). Single-lead f-wave extraction using diffusion geometry. Physiological Measurement, 38(7), 1310-1334. doi:10.1088/1361-6579/aa707cMateo, J., & Joaquín Rieta, J. (2013). Radial basis function neural networks applied to efficient QRST cancellation in atrial fibrillation. Computers in Biology and Medicine, 43(2), 154-163. doi:10.1016/j.compbiomed.2012.11.007McSharry, P. E., Clifford, G. D., Tarassenko, L., & Smith, L. A. (2003). A dynamical model for generating synthetic electrocardiogram signals. IEEE Transactions on Biomedical Engineering, 50(3), 289-294. doi:10.1109/tbme.2003.808805Nault, I., Lellouche, N., Matsuo, S., Knecht, S., Wright, M., Lim, K.-T., … Haïssaguerre, M. (2009). Clinical value of fibrillatory wave amplitude on surface ECG in patients with persistent atrial fibrillation. Journal of Interventional Cardiac Electrophysiology, 26(1), 11-19. doi:10.1007/s10840-009-9398-3Petrenas, A., Marozas, V., Sološenko, A., Kubilius, R., Skibarkiene, J., Oster, J., & Sörnmo, L. (2017). Electrocardiogram modeling during paroxysmal atrial fibrillation: application to the detection of brief episodes. 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Combined Nonlinear Analysis of Atrial and Ventricular Series for Automated Screening of Atrial Fibrillation

[EN] Atrial fibrillation (AF) is the most common cardiac arrhythmia in clinical practice. It often starts with asymptomatic and short episodes, which are difficult to detect without the assistance of automatic monitoring tools. The vast majority of methods proposed for this purpose are based on quantifying the irregular ventricular response (i.e., RR series) during the arrhythmia. However, although AF totally alters the atrial activity (AA) reflected on the electrocardiogram(ECG), replacing stable P-waves by chaotic and time-variant fibrillatory waves, this information has still not been explored for automated screening of AF. Hence, a pioneering AF detector based on quantifying the variability over time of the AA morphological pattern is here proposed. Results from two public reference databases have proven that the proposed method outperforms current state-of-the-art algorithms, reporting accuracy higher than 90%. A less false positive rate in the presence of other arrhythmias different from AF was also noticed. Finally, the combination of this algorithm with the classical analysis of RR series variability also yielded a promising trade-off between AF accuracy and detection delay. Indeed, this combination provided similar accuracy than RR-based methods, but with a significantly shorter delay of 10 beats.This work was supported by the Spanish Ministry of Economy and Competitiveness (Project TEC2014-52250-R).Rodenas, J.; Garcia, M.; Alcaraz, R.; Rieta, JJ. (2017). Combined Nonlinear Analysis of Atrial and Ventricular Series for Automated Screening of Atrial Fibrillation. Complexity. (2163610):1-13. doi:10.1155/2017/2163610S113216361

A Deep Learning Solution for Automatized Interpretation of 12-Lead ECGs

[EN] A broad variety of algorithms for detection and classification of rhythm and morphology abnormalities in ECG recordings have been proposed in the last years. Although some of them have reported very promising results, they have been mostly validated on short and non-public datasets, thus making their comparison extremely difficult. PhysioNet/CinC Challenge 2020 provides an interesting opportunity to compare these and other algorithms on a wide set of ECG recordings. The present model was created by ¿ELBIT¿ team. The algorithm is based on deep learning, and the segmentation of all beats in the 12-lead ECG recording, generating a new signal for each one by concatenating sequentially the information found in each lead. The resulting signal is then transformed into a 2- D image through a continuous Wavelet transform and inputted to a convolutional neural network. According to the competition guidelines, classification results were evaluated in terms of a class-weighted F-score (Fß) and a generalization of the Jaccard measure (Gß). In average for all training signals, these metrics were 0.933 and 0.811, respectively. Regarding validation on the testing set from the first phase of the challenge, mean values for both performance indices were 0.654 and 0.372, respectivelyThis research has been supported by the grants DPI2017¿83952¿C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha, AICO/2019/036 from Generalitat Valenciana and FEDER 2018/11744Huerta, A.; Martinez-Rodrigo, A.; Rieta, JJ.; Alcaraz, R. (2020). A Deep Learning Solution for Automatized Interpretation of 12-Lead ECGs. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.305S1

ECG Quality Assessment via Deep Learning and Data Augmentation

[EN] Quality assessment of ECG signals acquired with wearable devices is essential to avoid misdiagnosis of some cardiac disorders. For that purpose, novel deep learning algorithms have been recently proposed. However, training of these methods require large amount of data and public databases with annotated ECG samples are limited. Hence, the present work aims at validating the usefulness of a well-known data augmentation approach in this context of ECG quality assessment. Precisely, classification between high- and low-quality ECG excerpts achieved by a common convolutional neural network (CNN) trained on two databases has been compared. On the one hand, 2,000 5 second-length ECG excerpts were initially selected from a freely available database. Half of the segments were extracted from noisy ECG recordings and the other half from high-quality signals. On the other hand, using a data augmentation approach based on time-scale modification, noise addition, and pitch shifting of the original noisy ECG experts, 1,000 additional low-quality intervals were generated. These surrogate noisy signals and the original highquality ones formed the second dataset. The results for both cases were compared using a McNemar test and no statistically significant differences were noticed, thus suggesting that the synthesized noisy signals could be used for reliable training of CNN-based ECG quality indices.Huerta, Á.; Martínez-Rodrigo, A.; Rieta, JJ.; Alcaraz, R. (2021). ECG Quality Assessment via Deep Learning and Data Augmentation. 1-4. https://doi.org/10.22489/CinC.2021.2431

Response to the article by Sungnoon et al. "Atrial electrophysiological property analysis by sample entropy and atrial fibrillatory rate with cardiac autonomic derangements in acute ischemic stroke with atrial fibrillation"

Alcaraz, R.; Rieta, JJ. (2014). Response to the article by Sungnoon et al. "Atrial electrophysiological property analysis by sample entropy and atrial fibrillatory rate with cardiac autonomic derangements in acute ischemic stroke with atrial fibrillation". Neurology Asia. 19(3):335-336. http://hdl.handle.net/10251/62850S33533619

A novel wavelet-based filtering strategy to remove powerline interference from electrocardiograms with atrial fibrillation

This is an author-created, un-copyedited versíon of an article published in Physiological Measurement. IOP Publishing Ltd is not responsíble for any errors or omissíons in this versíon of the manuscript or any versíon derived from it. The Versíon of Record is available online at http://doi.org/10.1088/1361-6579/aae8b1[EN] Objective: The electrocardiogram (ECG) is currently the most widely used recording to diagnose cardiac disorders, including the most common supraventricular arrhythmia, such as atrial fibrillation (AF). However, different types of electrical disturbances, in which power-line interference (PLI) is a major problem, can mask and distort the original ECG morphology. This is a significant issue in the context of AF, because accurate characterization of fibrillatory waves (f-waves) is unavoidably required to improve current knowledge about its mechanisms. This work introduces a new algorithm able to reduce high levels of PLI and preserve, simultaneously, the original ECG morphology. Approach: The method is based on stationary wavelet transform shrinking and makes use of a new thresholding function designed to work successfully in a wide variety of scenarios. In fact, it has been validated in a general context with 48 ECG recordings obtained from pathological and non-pathological conditions, as well as in the particular context of AF, where 380 synthesized and 20 long-term real ECG recordings were analyzed. Main results: In both situations, the algorithm has reported a notably better performance than common methods designed for the same purpose. Moreover, its effectiveness has proven to be optimal for dealing with ECG recordings affected by AF, sincef-waves remained almost intact after removing very high levels of noise. Significance: The proposed algorithm may facilitate a reliable characterization of thef-waves, preventing them from not being masked by the PLI nor distorted by an unsuitable filtering applied to ECG recordings with AF.Research supported by grants DPI2017-83952-C3 MINECO/AEI/FEDER, UE and SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha.García, M.; Martínez, M.; Ródenas, J.; Rieta, JJ.; Alcaraz, R. (2018). A novel wavelet-based filtering strategy to remove powerline interference from electrocardiograms with atrial fibrillation. Physiological Measurement. 39(11):1-15. https://doi.org/10.1088/1361-6579/aae8b1S115391

Study on the Stability of CFAEs to Characterize the Atrial Substrate in Atrial Fibrillation

[EN] A variety of indexes has been applied to complex fractionated atrial electrograms (CFAEs) of atrial fibrillation (AF) aimed at characterizing the atrial substrate. However, often the reported results miss the assessment of intra-recording and intra-patient stability of the analyzed data, as well as CFAEs signal quality. This work introduces a study in which Determinism (DET) of Recurrence Quantification Analysis (RQA) and Sample Entropy (SE) have been applied to assess intra-recording and intrapatient stability of 1, 2 and 4 s-length segments CFAEs recorded from patients with paroxysmal and persistent AF using the coefficient of variation (CV). Furthermore, the analyses verified changes introduced by discarding artifacted and noisy CFAE segments. The intra-recording analysis pointed out that discarding segments provoked a significant variation of CV(%) in any segment length both for DET and SE, with deeper decreases for longer segments. Intra-patient stability provided large variations in CV(%) for DET and even bigger for SE at any segment length. In this case discarding segments was useless and CV provided limited variations. Kruskal-Wallis test revealed significant differences in DET and SE values among channels, independently from the discarding process.Research supported by grants DPI2017-83952-C3 from MINECO/AEI/FEDER UE, SBPLY/17/180501/000411 from JCCLM and AICO/2019/036 from GVA.Finotti, E.; Ciaccio, EJ.; Garan, H.; Hornero, F.; Alcaraz, R.; Rieta, JJ. (2020). Study on the Stability of CFAEs to Characterize the Atrial Substrate in Atrial Fibrillation. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.252S1

An Efficient Algorithm Based on Wavelet Transform to Reduce Powerline Noise From Electrocardiograms

Nowadays, the electrocardiogram (ECG) is still the most widely used signal for the diagnosis of cardiac pathologies. However, this recording is often disturbed by the powerline interference (PLI), its removal being mandatory to avoid misdiagnosis. Although a broad variety of methods have been proposed for that purpose, often they substantially alter the original signal morphology or are computationally expensive. Hence, the present work introduces a simple and efficient algorithm to suppress the PLI from the ECG. Briefly, the input signal is decomposed into four Wavelet levels and the resulting coefficients are thresholded to remove the PLI estimated from the TQ intervals. The denoised ECG signal is then reconstructed by computing the inverse Wavelet transform. The method has been validated making use of fifty 10-min length clean ECG segments obtained from the MIT BIH Normal Sinus Rhythm database, which were contaminated with a sinusoidal signal of 50 Hz and variable harmonic content. Comparing the original and denoised ECG signals through a signed correlation index, improvements between 10 - 72% have been observed with respect to common adaptive notch filtering, implemented for comparison. These results suggest that the proposed method is featured by an enhanced trade-off between noise reduction and signal morphology preservation

Novel Photoplethysmographic and Electrocardiographic Features for Enhanced Detection of Hypertensive Individuals

[EN] Hypertension is a major risk factor for many cardiovascular diseases, which are the leading cause of death worldwide. Regular monitoring is essential to provide early diagnosis since most patients with elevated blood pressure (BP) have asymptomatic hypertension. This work presents a method for BP classification using simultaneous electrocardiographic (ECG), photoplethysmographic (PPG) and BP signals. 86 recordings were used, being 35 normotensive, 26 prehypertensive and 25 hypertensive. It has been proposed 23 novel features to improve the discrimination between healthy and hypertensive individuals based on pulse arrival times (PAT) and morphological features from PPG, VPG and APG signal. Moreover, alternative classification models as Support Vector Machines (SVM), Naive Bayes or Coarse Trees were trained with the defined features to compare the classification performance. The classifier that provided the highest results comparing normotensive with prehypertensive and hypertensive subjects were Coarse Tree, providing an F1 score of 85.44% (Se of 86.27% and Sp of 77.14%). The use of new PPG and ECG features has successfully improved the discrimination between healthy and hypertensive individuals, around 7% of F1 score, so this machine learning methodology would be of high interest to detect HT introducing these techniques in wearable devices.Research supported by grants DPI2017-83952-C3 from MINECO/AEI/FEDER UE, SBPLY/17/180501/000411 from JCCLM and AICO/2021/286 from GVA.Cano, J.; Quesada, A.; Ravelli, F.; Zangróniz, R.; Alcaraz, R.; Rieta, JJ. (2021). Novel Photoplethysmographic and Electrocardiographic Features for Enhanced Detection of Hypertensive Individuals. IEEE. 1-4. https://doi.org/10.1109/EHB52898.2021.96575461

Discrimination Between CFAEs of Paroxysmal and Persistent Atrial Fibrillation With Simple Classification Models of Reduced Features

[EN] A significant number of variables to discriminate between paroxysmal and persistent atrial fibrillation (ParAF vs. PerAF) has been widely exploited, mostly assessed with statistical tests aimed to suggest adequate approaches for catheter ablation (CA) of AF. However, in practice, it would be desirable to utilize simple classification models readily understandable. In this work dominant frequency (DF), AF cycle length (AFCL), sample entropy (SE) and determinism (DET) of recurrent quantification analysis were applied to recordings of complex fractionated atrial electrograms (CFAEs) of AF patients, aimed to create simple models to discriminate between ParAF and PerAF. Correlation matrix filters removed redundant information and Random Forests ranked the variables by relevance. Next, coarse tree models were built, optimally combining high-ranking indexes, and tested with leave-one-out cross-validation. The best classification performance combined SE and DF with an Accuracy (Acc) of 88.2% to discriminate ParAF from PerAF, while the highest single Acc was provided by DET reaching 82.4%. Hence, careful selection of reduced sets of features feeding simple classification models is able to discriminate accurately between CFAEs of ParAF and PerAFFinotti, E.; Ciaccio, EJ.; Garan, H.; Bertomeu-Gonzalez, V.; Alcaraz, R.; Rieta, JJ. (2020). Discrimination Between CFAEs of Paroxysmal and Persistent Atrial Fibrillation With Simple Classification Models of Reduced Features. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.360S1