Comparison of different electrocardiography with vectorcardiography transformations

This paper deals with transformations from electrocardiographic (ECG) to vectorcardiographic (VCG) leads. VCG provides better sensitivity, for example for the detection of myocardial infarction, ischemia, and hypertrophy. However, in clinical practice, measurement of VCG is not usually used because it requires additional electrodes placed on the patient's body. Instead, mathematical transformations are used for deriving VCG from 12-leads ECG. In this work, Kors quasi-orthogonal transformation, inverse Dower transformation, Kors regression transformation, and linear regression-based transformations for deriving P wave (PLSV) and QRS complex (QLSV) are implemented and compared. These transformation methods were not yet compared before, so we have selected them for this paper. Transformation methods were compared for the data from the Physikalisch-Technische Bundesanstalt (PTB) database and their accuracy was evaluated using a mean squared error (MSE) and a correlation coefficient (R) between the derived and directly measured Frank's leads. Based on the statistical analysis, Kors regression transformation was significantly more accurate for the derivation of the X and Y leads than the others. For the Z lead, there were no statistically significant differences in the medians between Kors regression transformation and the PLSV and QLSV methods. This paper thoroughly compared multiple VCG transformation methods to conventional VCG Frank's orthogonal lead system, used in clinical practice.Web of Science1914art. no. 307

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). An Extended Bayesian Framework for Atrial and Ventricular Activity Separation in Atrial Fibrillation. IEEE Journal of Biomedical and Health Informatics, 21(6), 1573-1580. doi:10.1109/jbhi.2016.2625338Krijthe, B. P., Kunst, A., Benjamin, E. J., Lip, G. Y. H., Franco, O. H., Hofman, A., … Heeringa, J. (2013). Projections on the number of individuals with atrial fibrillation in the European Union, from 2000 to 2060. European Heart Journal, 34(35), 2746-2751. doi:10.1093/eurheartj/eht280Langley, P. (2015). Wavelet Entropy as a Measure of Ventricular Beat Suppression from the Electrocardiogram in Atrial Fibrillation. Entropy, 17(12), 6397-6411. doi:10.3390/e17096397Langley, P., Rieta, J. J., Stridh, M., Millet, J., Sornmo, L., & Murray, A. (2006). Comparison of Atrial Signal Extraction Algorithms in 12-Lead ECGs With Atrial Fibrillation. IEEE Transactions on Biomedical Engineering, 53(2), 343-346. doi:10.1109/tbme.2005.862567Lee, J., Song, M., Shin, D., & Lee, K. (2012). 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. Physiological Measurement, 38(11), 2058-2080. doi:10.1088/1361-6579/aa9153Petrenas, A., Marozas, V., Sornmo, L., & Lukosevicius, A. (2012). An Echo State Neural Network for QRST Cancellation During Atrial Fibrillation. IEEE Transactions on Biomedical Engineering, 59(10), 2950-2957. doi:10.1109/tbme.2012.2212895Platonov, P. G., Corino, V. D. A., Seifert, M., Holmqvist, F., & Sornmo, L. (2014). Atrial fibrillatory rate in the clinical context: natural course and prediction of intervention outcome. Europace, 16(suppl 4), iv110-iv119. doi:10.1093/europace/euu249Sassi, R., Corino, V. D. A., & Mainardi, L. T. (2009). Analysis of Surface Atrial Signals: Time Series with Missing Data? Annals of Biomedical Engineering, 37(10), 2082-2092. doi:10.1007/s10439-009-9757-3Schotten, U., Dobrev, D., Platonov, P. G., Kottkamp, H., & Hindricks, G. (2016). Current controversies in determining the main mechanisms of atrial fibrillation. 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Automatic wide complex tachycardia differentiation using mathematically synthesized vectorcardiogram signals

BACKGROUND: Automated wide complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) may be accomplished using novel calculations that quantify the extent of mean electrical vector changes between the WCT and baseline electrocardiogram (ECG). At present, it is unknown whether quantifying mean electrical vector changes within three orthogonal vectorcardiogram (VCG) leads (X, Y, and Z leads) can improve automated VT and SWCT classification. METHODS: A derivation cohort of paired WCT and baseline ECGs was used to derive five logistic regression models: (i) one novel WCT differentiation model (i.e., VCG Model), (ii) three previously developed WCT differentiation models (i.e., WCT Formula, VT Prediction Model, and WCT Formula II), and (iii) one all-inclusive model (i.e., Hybrid Model). A separate validation cohort of paired WCT and baseline ECGs was used to trial and compare each model\u27s performance. RESULTS: The VCG Model, composed of WCT QRS duration, baseline QRS duration, absolute change in QRS duration, X-lead QRS amplitude change, Y-lead QRS amplitude change, and Z-lead QRS amplitude change, demonstrated effective WCT differentiation (area under the curve [AUC] 0.94) for the derivation cohort. For the validation cohort, the diagnostic performance of the VCG Model (AUC 0.94) was similar to that achieved by the WCT Formula (AUC 0.95), VT Prediction Model (AUC 0.91), WCT Formula II (AUC 0.94), and Hybrid Model (AUC 0.95). CONCLUSION: Custom calculations derived from mathematically synthesized VCG signals may be used to formulate an effective means to differentiate WCTs automatically

MRI-Based Computational Torso/Biventricular Multiscale Models to Investigate the Impact of Anatomical Variability on the ECG QRS Complex

Aims:Patient-to-patient anatomical differences are an important source of variability in the electrocardiogram, and they may compromise the identification of pathological electrophysiological abnormalities. This study aims at quantifying the contribution of variability in ventricular and torso anatomies to differences in QRS complexes of the 12-lead ECG using computer simulations. Methods:A computational pipeline is presented that enables computer simulations using human torso/biventricular anatomically based electrophysiological models from clinically standard magnetic resonance imaging (MRI). The ventricular model includes membrane kinetics represented by the biophysically detailed O’Hara Rudy model modified for tissue heterogeneity and includes fiber orientation based on the Streeter rule. A population of 265 torso/biventricular models was generated by combining ventricular and torso anatomies obtained from clinically standard MRIs, augmented with a statistical shape model of the body. 12-lead ECGs were simulated on the 265 human torso/biventricular electrophysiology models, and QRS morphology,duration and amplitude were quantified in each ECG lead for each of the human torso-biventricular models. Results:QRS morphologies in limb leads are mainly determined by ventricular anatomy,while in the precordial leads, and especially V1 to V4, they are determined by heart position within the torso. Differences in ventricular orientation within the torso can explain morphological variability from monophasic to biphasic QRS complexes. QRS duration ismainly influenced by myocardial volume, while it is hardly affected by the torso anatomyor position. An average increase of 0.12±0.05 ms in QRS duration is obtained for eachcm3of myocardial volume across all the leads while it hardly changed due to changes in torso volume. Conclusion:Computer simulations using populations of human torso/biventricular models based on clinical MRI enable quantification of anatomical causes of variability in the QRS complex of the 12-lead ECG. The human models presented also pave theway toward their use as testbeds in silico clinical trial

Electrocardiographic algorithms to guide a management strategy of idiopathic outflow tract ventricular arrhythmias

The current guidelines of the European Society of Cardiology outlined electrocardiographic (ECG) differentiation of the site of origin (SoO) in patients with idiopathic ventricular arrhythmias (IVAs). The aim of this study was to compare 3 ECG algorithms for differentiating the SoO and to determine their diagnostic value for the management of outflow tract IVA. We analyzed 202 patients (mean age [SD]: 45 [16.7] years; 133 women [66%]) with IVAs with the inferior axis (130 premature ventricular contractions or ventricular tachycardias from the right ventricular outflow tract [RVOT]; 72, from the left ventricular outflow tract [LVOT]), who underwent successful radiofrequency catheter ablation (RFCA) using the 3‑dimensional electroanatomical system. The ECGs before ablation were analyzed using custom‑developed software. Automated measurements were performed for the 3 algorithms: 1) novel transitional zone (TZ) index, 2) $V_{2}S/V_{3}R$, and 3) $V_{2}$ transition ratio. The results were compared with the SoO of acutely successful RFCA. The $V_{2}S/V_{3}R$ algorithm predicted the left‑sided SoO with a sensitivity and specificity close to 90%. The TZ index showed higher sensitivity (93%) with lower specificity (85%). In the subgroup with the transition zone in lead V3 (n = 44, 15 from the LVOT) the sensitivity and specificity of the V2– transition‑ratio algorithm were 100% and 45%, respectively. The combined TZ index+$V_{2}S/V_{3}R$ algorithm (LVOT was considered only when both algorithms suggested the LVOT SoO) can increase the specificity of the LVOT SoO prediction to 98% with a sensitivity of 88%. The combined TZ‑index and $V_{2}S/V_{3}R$ algorithm allowed an accurate and simple identification of the SoO of IVA. A prospective study is needed to determine the strategy for skipping the RVOT mapping in patients with LVOT arrhythmias indicated by the 2 combined algorithms

Towards a better understanding of the precordial leads : an engineering point of view

This thesis provides comprehensive literature review of the electrocardiography evolution to highlight the important theories behind the development of the electrocardiography device. More importantly, it discusses different electrode placement on the chest, and their clinical advantages. This work presents a technical detail of a new ECG device which was developed at MARCS institute and can record the Wilson Central Terminal (WCT) components in addition to the standard 12-lead ECG. This ECG device was used to record from 147 patients at Campbelltown hospital over three years. The first two years of recording contain 92 patients which was published in the Physionet platform under the name of Wilson Central Terminal ECG database (WCTECGdb). This novel dataset was used to demonstrate the WCT signal characterisation and investigate how WCT impacts the precordial leads. Furthermore, the clinical influence of the WCT on precordial leads in patients diagnosed with non-ST segment elevation myocardial infarction (NSTEMI) is discussed. The work presented in this research is intended to revisit some of the ECG theories and investigate the validity of them using the recorded data. Furthermore, the influence of the left leg potential on recording the precordial leads is presented, which lead to investigate whether the WCT and augmented vector foot (aVF) are proportional. Finally, a machine learning approach is proposed to minimise the Wilson Central Terminal

Synthesizing Skeletal Motion and Physiological Signals as a Function of a Virtual Human's Actions and Emotions

Round-the-clock monitoring of human behavior and emotions is required in many healthcare applications which is very expensive but can be automated using machine learning (ML) and sensor technologies. Unfortunately, the lack of infrastructure for collection and sharing of such data is a bottleneck for ML research applied to healthcare. Our goal is to circumvent this bottleneck by simulating a human body in virtual environment. This will allow generation of potentially infinite amounts of shareable data from an individual as a function of his actions, interactions and emotions in a care facility or at home, with no risk of confidentiality breach or privacy invasion. In this paper, we develop for the first time a system consisting of computational models for synchronously synthesizing skeletal motion, electrocardiogram, blood pressure, respiration, and skin conductance signals as a function of an open-ended set of actions and emotions. Our experimental evaluations, involving user studies, benchmark datasets and comparison to findings in the literature, show that our models can generate skeletal motion and physiological signals with high fidelity. The proposed framework is modular and allows the flexibility to experiment with different models. In addition to facilitating ML research for round-the-clock monitoring at a reduced cost, the proposed framework will allow reusability of code and data, and may be used as a training tool for ML practitioners and healthcare professionals