Deep conv-attention model for diagnosing left bundle branch block from 12-lead electrocardiograms

Cardiac resynchronization therapy (CRT) is a treatment that is used to compensate for irregularities in the heartbeat. Studies have shown that this treatment is more effective in heart patients with left bundle branch block (LBBB) arrhythmia. Therefore, identifying this arrhythmia is an important initial step in determining whether or not to use CRT. On the other hand, traditional methods for detecting LBBB on electrocardiograms (ECG) are often associated with errors. Thus, there is a need for an accurate method to diagnose this arrhythmia from ECG data. Machine learning, as a new field of study, has helped to increase human systems' performance. Deep learning, as a newer subfield of machine learning, has more power to analyze data and increase systems accuracy. This study presents a deep learning model for the detection of LBBB arrhythmia from 12-lead ECG data. This model consists of 1D dilated convolutional layers. Attention mechanism has also been used to identify important input data features and classify inputs more accurately. The proposed model is trained and validated on a database containing 10344 12-lead ECG samples using the 10-fold cross-validation method. The final results obtained by the model on the 12-lead ECG data are as follows. Accuracy: 98.80+-0.08%, specificity: 99.33+-0.11 %, F1 score: 73.97+-1.8%, and area under the receiver operating characteristics curve (AUC): 0.875+-0.0192. These results indicate that the proposed model in this study can effectively diagnose LBBB with good efficiency and, if used in medical centers, will greatly help diagnose this arrhythmia and early treatment

A novel automated tower graph based ECG signal classification method with hexadecimal local adaptive binary pattern and deep learning

Electrocardiography (ECG) signal recognition is one of the popular research topics for machine learning. In this paper, a novel transformation called tower graph transformation is proposed to classify ECG signals with high accuracy rates. It employs a tower graph, which uses minimum, maximum and average pooling methods altogether to generate novel signals for the feature extraction. In order to extract meaningful features, we presented a novel one-dimensional hexadecimal pattern. To select distinctive and informative features, an iterative ReliefF and Neighborhood Component Analysis (NCA) based feature selection is utilized. By using these methods, a novel ECG signal classification approach is presented. In the preprocessing phase, tower graph-based pooling transformation is applied to each signal. The proposed one-dimensional hexadecimal adaptive pattern extracts 1536 features from each node of the tower graph. The extracted features are fused and 15,360 features are obtained and the most discriminative 142 features are selected by the ReliefF and iterative NCA (RFINCA) feature selection approach. These selected features are used as an input to the artificial neural network and deep neural network and 95.70% and 97.10% classification accuracy was obtained respectively. These results demonstrated the success of the proposed tower graph-based method.</p

Ανίχνευση κολπικής μαρμαρυγής με συσκευές μέτρησης της αρτηριακής πίεσης

ΕΙΣΑΓΩΓΗ: Η κολπική μαρμαρυγή(ΚΜ) αποτελεί μια καρδιακή αρρυθμία συνοδευόμενη από μεγάλη θνησιμότητα και θνητότητα λόγω της πρόκλησης αγγειακών εγκεφαλικών επεισοδίων. Η έγκαιρη διάγνωση και αντιπηκτική αγωγή μειώνει σημαντικά αυτόν τον κίνδυνο, καθιστώντας την κατάλληλη νόσο για εφαρμογή καθολικού προγράμματος διαλογής(screening). ΣΚΟΠΟΣ: Συστηματική Ανασκόπηση της βιβλιογραφίας σχετικά με την ανίχνευση ΚΜ με συσκευές μέτρησης αρτηριακής πίεσης(ΑΠ). ΥΛΙΚΟ ΚΑΙ ΜΕΘΟΔΟΣ: Αναζήτηση δημοσιεύσεων στη βάση δεδομένων MEDLINE (Pubmed) έως και τον Μάιο του 2022 χρησιμοποιώντας τους εξής όρους: “atrial fibrillation”, “blood pressure”, “blood pressure monitor”, “blood pressure device”, “blood pressure measurement”, “detect”, “screen”. ΑΠΟΤΕΛΕΣΜΑΤΑ: Screening για την κολπική μαρμαρυγή προτείνεται σε άτομα ηλικίας &gt; 65 ετών. Η αρτηριακή υπέρταση και η ΚΜ συχνά συνυπάρχουν σαν συννοσηρότητες, ιδιαίτερα σε ηλικιωμένα άτομα, στην καθημερινή κλινική πράξη. Χαρακτηριστικά της νόσου (αυξημένος επιπολασμός, υψηλή θνητότητα και θνησιμότητα σε περίπτωση αγγειακού εγκεφαλικού επεισοδίου(ΑΕΕ), πρόληψη των ΑΕΕ με έγκαιρη εφαρμογή κατάλληλης θεραπευτικής αγωγής) υποδεικνύουν περαιτέρω την χρησιμότητα αυτού. Τα εργαλεία διαθέσιμα για screening της αρρυθμίας είναι πολλά και η αποτελεσματικότητά τους βρίσκεται υπό μελέτη. οι συσκευές μέτρησης της ΑΠ με προσαρμοσμένους αλγορίθμους κυκλοφορούν και μελετώνται χρόνια με πολύ καλά αποτελέσματα. Επιπλέον κατέχονται συνήθως από υπερτασικούς και ηλικιωμένους ασθενείς, το ιδανικό target group μιας δυνητικής screening στρατηγικής. Η τεχνική αυτή φαίνεται να είναι πιο αποτελεσματική από την ψηλάφηση σφυγμού από επαγγελματία υγείας και λιγότερο δαπανηρή από την διενέργεια ηλεκτροκαρδιογραφήματος(ΗΚΓ) 12 απαγωγών στον γενικό πληθυσμό. ΣΥΜΠΕΡΑΣΜΑΤΑ: Η υιοθέτηση από τα συστήματα υγείας ενός υβριδικού προγράμματος screening που αξιοποιεί τόσο τις νεότερες τεχνολογίες όσο και τις συσκευές μέτρησης ΑΠ, με επακόλουθη παραπομπή των ατόμων για περαιτέρω διερεύνηση φαίνεται να αποτελεί χρήσιμη στρατηγική για την έγκαιρη διάγνωση και πρόληψη των ΑΕΕ.INTRODUCTION: Atrial fibrillation(AF) is a cardiac arrhythmia with high morbidity and mortality due to its potential to cause stroke. Timely diagnosis and anticoagulatory treatment reduce this risk by an important amount, making therefore a screening program for its diagnosis of high value. AIM: To review the data regarding atrial fibrillation detection in blood pressure measurement devices. MATERIAL AND METHOD: Literature review was conducted in MEDLINE database (PubMed) until May, 2022 using the following search terms: “atrial fibrillation”, “blood pressure”, “blood pressure monitor”, “blood pressure device”, “blood pressure measurement”, “detect”, “screen”. RESULTS: Screening for AF is suggested for individuals older than 65 years of age. Arterial hypertension and AF frequently coexist as comorbidities, specially in older individuals. Particular features of AF(increasing prevalence, high burden of morbidity and mortality in case of stroke, stroke prevention with right timed anticoagulation treatment) also support the argument of screening. The tools in disposal for AF screening are many and their effectiveness under study. BP measurement devices with AF detection algorithms are well studied with satisfying results. Moreover, elderly individuals suffering from hypertension frequently own them, making them ideal for screening this population. This screening tool appears to be more effective than pulse palpation from health workers and less costly than using a 12-lead ECG to the general population. CONCLUSION: The adoption of a hybrid screening program that utilises modern technologies as well as BP measurement devices from health care systems, with consequent referral of them for further evaluation seems to be a reasonable strategy for the early AF detection and efficient stroke detection

Characterization of atrial arrhythmias in body surface potential mapping: A computational study

[EN] Purpose: Atrial tachycardia (AT), flutter (AFL) and fibrillation (AF) are very common cardiac arrhythmias and are driven by localized sources that can be ablation targets. Non-invasive body surface potential mapping (BSPM) can be useful for early diagnosis and ablation planning. We aimed to characterize and differentiate the arrhythmic mechanisms behind AT, AFL and AF from the BSPM perspective using basic features reflecting their electrophysiology. Methods: 19 simulations of 567-lead BSPMs were used to obtain dominant frequency (DF) maps and estimate the atrial driving frequencies using the highest DF (HDF). Regions with vertical bar DF - HDF vertical bar <= 1Hz were segmented and characterized (size, area); the spatial distribution of the differences |DF -atrial HDF estimate vertical bar was qualitatively analyzed. Phase singularity points (SPs) were detected on maps generated with Hilbert transform after band-pass filtering around the HDF (1Hz). Connected SPs along time (filaments) and their histogram (heatmaps) were used for rotational activity characterization (duration, spatiotemporal stability). Results were reproduced in clinical layouts (252 to 12 leads) and with different rotations and translations of the atria within the torso, and compared with the original 567-lead outcomes using structural similarity index (SSIM) between maps, sensitivity and precision in SP detection and direct feature comparison. Random forest and least-square based algorithms were used to classify the arrhythmias and their mechanisms' location, respectively, based on the obtained features. Results: Frequency and phase analyses revealed distinct behavior between arrhythmias. AT and AFL presented uniform DF maps with low variance, while AF maps were more heterogeneous. Lower differences from the atrial HDF regions correlated with the driver location. Rotational activity was most stable in AFL, followed by AT and AF. Features were robust to lower spatial resolution layouts and modifications in the atrial geometry; DF and heatmaps presented decreasing SSIM along the layouts. The classification of the arrhythmias and their mechanisms' location achieved balanced accuracy of 72.0% and 73.9%, respectively. Conclusion: Non-invasive characterization of AT, AFL and AF based on realistic models highlights intrinsic differences between the arrhythmias, enhancing the BSPM utility as an auxiliary clinical tool.This study was supported in part by grants from Sao Paulo Research Foundation (2017/19775-3), Instituto de Salud Carlos III Research Foundation, Fondo Europeo de Desarrollo Regional FEDER, Spain (PI17/01106), and Generalitat Valenciana, Spain (AICO/2018/26,7 APOSTD/2017, GVA/2018/103).Gonçalves Marques, V.; Rodrigo Bort, M.; Guillem Sánchez, MS.; Salinet, J. (2020). Characterization of atrial arrhythmias in body surface potential mapping: A computational study. Computers in Biology and Medicine. 127:1-13. https://doi.org/10.1016/j.compbiomed.2020.103904113127Haïssaguerre, M., Jaïs, P., Shah, D. C., Takahashi, A., Hocini, M., Quiniou, G., … Clémenty, J. (1998). Spontaneous Initiation of Atrial Fibrillation by Ectopic Beats Originating in the Pulmonary Veins. New England Journal of Medicine, 339(10), 659-666. doi:10.1056/nejm199809033391003Atienza, F., Almendral, J., Jalife, J., Zlochiver, S., Ploutz-Snyder, R., Torrecilla, E. G., … Berenfeld, O. (2009). Real-time dominant frequency mapping and ablation of dominant frequency sites in atrial fibrillation with left-to-right frequency gradients predicts long-term maintenance of sinus rhythm. Heart Rhythm, 6(1), 33-40. doi:10.1016/j.hrthm.2008.10.024Narayan, S. M., Krummen, D. E., Shivkumar, K., Clopton, P., Rappel, W.-J., & Miller, J. M. (2012). Treatment of Atrial Fibrillation by the Ablation of Localized Sources. Journal of the American College of Cardiology, 60(7), 628-636. doi:10.1016/j.jacc.2012.05.022Guillem, M. S., Climent, A. M., Rodrigo, M., Fernández-Avilés, F., Atienza, F., & Berenfeld, O. (2016). Presence and stability of rotors in atrial fibrillation: evidence and therapeutic implications. Cardiovascular Research, 109(4), 480-492. doi:10.1093/cvr/cvw011Alday, E. A. P., Colman, M. A., Langley, P., & Zhang, H. (2017). Novel non-invasive algorithm to identify the origins of re-entry and ectopic foci in the atria from 64-lead ECGs: A computational study. PLOS Computational Biology, 13(3), e1005270. doi:10.1371/journal.pcbi.1005270Guillem, M. S., Climent, A. M., Millet, J., Arenal, Á., Fernández-Avilés, F., Jalife, J., … Berenfeld, O. (2013). Noninvasive Localization of Maximal Frequency Sites of Atrial Fibrillation by Body Surface Potential Mapping. Circulation: Arrhythmia and Electrophysiology, 6(2), 294-301. doi:10.1161/circep.112.000167Rodrigo, M., Guillem, M. S., Climent, A. M., Pedrón-Torrecilla, J., Liberos, A., Millet, J., … Berenfeld, O. (2014). Body surface localization of left and right atrial high-frequency rotors in atrial fibrillation patients: A clinical-computational study. Heart Rhythm, 11(9), 1584-1591. doi:10.1016/j.hrthm.2014.05.013Vanheusden, F. J., Chu, G. S., Li, X., Salinet, J., Almeida, T. P., Dastagir, N., … Schlindwein, F. S. (2019). Systematic differences of non-invasive dominant frequency estimation compared to invasive dominant frequency estimation in atrial fibrillation. Computers in Biology and Medicine, 104, 299-309. doi:10.1016/j.compbiomed.2018.11.017Ng, J., Kadish, A. H., & Goldberger, J. J. (2006). Effect of electrogram characteristics on the relationship of dominant frequency to atrial activation rate in atrial fibrillation. Heart Rhythm, 3(11), 1295-1305. doi:10.1016/j.hrthm.2006.07.027Atienza, F., Almendral, J., Ormaetxe, J. M., Moya, Á., Martínez-Alday, J. D., Hernández-Madrid, A., … Jalife, J. (2014). Comparison of Radiofrequency Catheter Ablation of Drivers and Circumferential Pulmonary Vein Isolation in Atrial Fibrillation. Journal of the American College of Cardiology, 64(23), 2455-2467. doi:10.1016/j.jacc.2014.09.053Rodrigo, M., Climent, A. M., Liberos, A., Fernández-Avilés, F., Berenfeld, O., Atienza, F., & Guillem, M. S. (2017). Technical Considerations on Phase Mapping for Identification of Atrial Reentrant Activity in Direct- and Inverse-Computed Electrograms. Circulation: Arrhythmia and Electrophysiology, 10(9). doi:10.1161/circep.117.005008Dössel, O., Krueger, M. W., Weber, F. M., Wilhelms, M., & Seemann, G. (2012). Computational modeling of the human atrial anatomy and electrophysiology. Medical & Biological Engineering & Computing, 50(8), 773-799. doi:10.1007/s11517-012-0924-6Marques, V. G., Rodrigo, M., Guillem, M. S., & Salinet, J. (2020). A robust wavelet-based approach for dominant frequency analysis of atrial fibrillation in body surface signals. Physiological Measurement, 41(7), 075004. doi:10.1088/1361-6579/ab97c1Mallat, S., & Hwang, W. L. (1992). Singularity detection and processing with wavelets. IEEE Transactions on Information Theory, 38(2), 617-643. doi:10.1109/18.119727Vijayakumar, R., Vasireddi, S. K., Cuculich, P. S., Faddis, M. N., & Rudy, Y. (2016). Methodology Considerations in Phase Mapping of Human Cardiac Arrhythmias. Circulation: Arrhythmia and Electrophysiology, 9(11). doi:10.1161/circep.116.004409Everett, T. H., Moorman, J. R., Kok, L.-C., Akar, J. G., & Haines, D. E. (2001). Assessment of Global Atrial Fibrillation Organization to Optimize Timing of Atrial Defibrillation. Circulation, 103(23), 2857-2861. doi:10.1161/01.cir.103.23.2857Haissaguerre, M., Hocini, M., Denis, A., Shah, A. J., Komatsu, Y., Yamashita, S., … Dubois, R. (2014). Driver Domains in Persistent Atrial Fibrillation. Circulation, 130(7), 530-538. doi:10.1161/circulationaha.113.005421Van Oosterom, A., Ihara, Z., Jacquemet, V., & Hoekema, R. (2007). Vectorcardiographic lead systems for the characterization of atrial fibrillation. Journal of Electrocardiology, 40(4), 343.e1-343.e11. doi:10.1016/j.jelectrocard.2006.08.002Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transactions on Image Processing, 13(4), 600-612. doi:10.1109/tip.2003.819861Rodrigo, M., Climent, A. M., Liberos, A., Fernández-Avilés, F., Berenfeld, O., Atienza, F., & Guillem, M. S. (2017). Highest dominant frequency and rotor positions are robust markers of driver location during noninvasive mapping of atrial fibrillation: A computational study. Heart Rhythm, 14(8), 1224-1233. doi:10.1016/j.hrthm.2017.04.017Rodrigo, M., Climent, A. M., Liberos, A., Fernández-Aviles, F., Atienza, F., Guillem, M. S., & Berenfeld, O. (2017). Minimal configuration of body surface potential mapping for discrimination of left versus right dominant frequencies during atrial fibrillation. Pacing and Clinical Electrophysiology, 40(8), 940-946. doi:10.1111/pace.13133McGillivray, M. F., Cheng, W., Peters, N. S., & Christensen, K. (2018). Machine learning methods for locating re-entrant drivers from electrograms in a model of atrial fibrillation. Royal Society Open Science, 5(4), 172434. doi:10.1098/rsos.172434Cai, W., Chen, Y., Guo, J., Han, B., Shi, Y., Ji, L., … Luo, J. (2020). Accurate detection of atrial fibrillation from 12-lead ECG using deep neural network. Computers in Biology and Medicine, 116, 103378. doi:10.1016/j.compbiomed.2019.103378Yıldırım, Ö., Pławiak, P., Tan, R.-S., & Acharya, U. R. (2018). Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Computers in Biology and Medicine, 102, 411-420. doi:10.1016/j.compbiomed.2018.09.009Parvaneh, S., Rubin, J., Babaeizadeh, S., & Xu-Wilson, M. (2019). Cardiac arrhythmia detection using deep learning: A review. Journal of Electrocardiology, 57, S70-S74. doi:10.1016/j.jelectrocard.2019.08.004Kumar Sahoo, S., Wenmiao Lu, Teddy, S. D., Desok Kim, & Mengling Feng. (2011). Detection of Atrial fibrillation from non-episodic ECG data: A review of methods. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. doi:10.1109/iembs.2011.6091237NG, J., & GOLDBERGER, J. J. (2007). Understanding and Interpreting Dominant Frequency Analysis of AF Electrograms. Journal of Cardiovascular Electrophysiology, 18(6), 680-685. doi:10.1111/j.1540-8167.2007.00832.xSanders, P., Berenfeld, O., Hocini, M., Jaïs, P., Vaidyanathan, R., Hsu, L.-F., … Haïssaguerre, M. (2005). Spectral Analysis Identifies Sites of High-Frequency Activity Maintaining Atrial Fibrillation in Humans. Circulation, 112(6), 789-797. doi:10.1161/circulationaha.104.51701