Parameterization and R-Peak Error Estimations of ECG Signals Using Independent Component Analysis

Principal component analysis (PCA) is used to reduce dimensionality of electrocardiogram (ECG) data prior to performing independent component analysis (ICA). A newly developed PCA variance estimator by the author has been applied for detecting true, actual and false peaks of ECG data files. In this paper, it is felt that the ability of ICA is also checked for parameterization of ECG signals, which is necessary at times. Independent components (ICs) of properly parameterized ECG signals are more readily interpretable than the measurements themselves, or their ICs. The original ECG recordings and the samples are corrected by statistical measures to estimate the noise statistics of ECG signals and find the reconstruction errors. The capability of ICA is justified by finding the true, false and actual peaks of around 25–50, CSE (common standards for electrocardiography) database ECG files. In the present work, joint approximation for diagonalization of the eigen matrices (Jade) algorithm is applied to 3-channel ECG. ICA processing of different cases is dealt with and the R-peak magnitudes of the ECG waveforms before and after applying ICA are found and marked. ICA results obtained indicate that in most of the cases, the percentage error in reconstruction is very small. The developed PCA variance estimator along with the quadratic spline wavelet gave a sensitivity of 97.47% before applying ICA and 98.07% after ICA processing

Development of a Real-Time Single-Lead Single-Beat Frequency-Independent Myocardial Infarction Detector

The central aim of this research is the development and deployment of a novel multilayer machine learning design with unique application for the diagnosis of myocardial infarctions (MIs) from individual heartbeats of single-lead electrocardiograms (EKGs) irrespective of their sampling frequencies over a given range. To the best of our knowledge, this design is the first to attempt inter-patient myocardial infarction detection from individual heartbeats of single-lead (lead II) electrocardiograms that achieves high accuracy and near real-time diagnosis. The processing time of 300 milliseconds to a diagnosis is just at the time range in between extremely fast heartbeats of around 300 milliseconds, or 200 beats per minute. The design achieves stable performance metrics over the frequency range of 202Hz to 2.8kHz with an accuracy of 77.12%, positive predictive value (PPV) of 75.85%, and a negative predictive value (NPV) of 83.02% over the entire PTB database; 85.07%, 81.54%, 87.31% over the PTB-XL (the largest EKG database available for research) validation set, and 84.17%, 78.37%, 87.55% over the PTB-XL test set. Major design contributions and findings of this work reveal (1) a method for the realtime detection of ventricular depolarization events in the PQRST complex from 12-lead electrocardiograms using Independent Component Analysis (ICA), with a slightly different use of ICA proposed for electrocardiogram analysis and R-peak detection/localization; (2) a multilayer Long-Short Term Memory (LSTM) neural network design that identifies infarcted patients from a single heartbeat of a single-lead (lead II) electrocardiogram; (3) and integrated LSTM neural network with an algorithm that detects the R-peaks in real time for instantaneous detection of myocardial infarctions and for effective monitoring of patients under cardiac stress and/or at risk of myocardial infarction; (4) a fully integrated 12-lead real-time classifier with even higher detection metrics and a deeper neural architecture, which could serve as a near real-time monitoring tool that could gauge disease progression and evaluate benefits gained from early intervention and treatment planning; (5) a real-time frequency-independent design based on a single-lead single-beat MI detector, which is of pivotal importance to deployment as there is no standard sampling frequency for EKGs, making them span a wider frequency spectrum. vi

Analysis of surface atrial signals using spectral methods for time series with missing data

In this work, the analysis of atrial signals recorded during atrial fibrillation was pursued using two spectral estimators designed for series with missing data: the Lomb periodogram (LP) and the Iterative Singular Spectrum Analysis (ISSA). The main aim is to verify if subtraction ofthe ventricular activity might be avoided by performing spectral analysis on those ECG intervals where such activity is absent, (i.e. the T-Q intervals), at least to estimate the dominant atrial Fibrillatory Frequency (FF). Recordings coming from the 2004 Computers in Cardiology Termination Challenge Database were analyzed. Fibrillatory frequencies were then compared with those obtained from the analysis ofthe correspondent atrial signals extracted using a modified Average Beat Substraction (ABS) technique. We observed that the mean absolute difference was 0.42 \ub1 0.66 Hz for LP, (mean\ub1SD), and 0.39 \ub1 0.64 Hz for ISSA. We concluded that estimation of FF is feasible without applying QRS-T subtraction

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. S., Roth, G. A., Gillum, R. F., & Mensah, G. A. (2014). Global Burden of Atrial Fibrillation in Developed and Developing Nations. Global Heart, 9(1), 113. doi:10.1016/j.gheart.2014.01.004Colilla, S., Crow, A., Petkun, W., Singer, D. E., Simon, T., & Liu, X. (2013). Estimates of Current and Future Incidence and Prevalence of Atrial Fibrillation in the U.S. Adult Population. The American Journal of Cardiology, 112(8), 1142-1147. doi:10.1016/j.amjcard.2013.05.063Cuculich, P. S., Wang, Y., Lindsay, B. D., Faddis, M. N., Schuessler, R. B., Damiano, R. J., … Rudy, Y. (2010). Noninvasive Characterization of Epicardial Activation in Humans With Diverse Atrial Fibrillation Patterns. Circulation, 122(14), 1364-1372. doi:10.1161/circulationaha.110.945709Dai, H., Jiang, S., & Li, Y. (2013). Atrial activity extraction from single lead ECG recordings: Evaluation of two novel methods. Computers in Biology and Medicine, 43(3), 176-183. doi:10.1016/j.compbiomed.2012.12.005Donoso, F. I., Figueroa, R. 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. Journal of Internal Medicine, 279(5), 428-438. doi:10.1111/joim.12492Shah, D., Yamane, T., Choi, K.-J., & Haissaguerre, M. (2004). QRS Subtraction and the ECG Analysis of Atrial Ectopics. Annals of Noninvasive Electrocardiology, 9(4), 389-398. doi:10.1111/j.1542-474x.2004.94555.xSörnmo, L., Alcaraz, R., Laguna, P., & Rieta, J. J. (2018). Characterization of f Waves. Series in BioEngineering, 221-279. doi:10.1007/978-3-319-68515-1_6Sörnmo, L., Petrėnas, A., Laguna, P., & Marozas, V. (2018). Extraction of f Waves. Series in BioEngineering, 137-220. doi:10.1007/978-3-319-68515-1_5Sterling, M., Huang, D. T., & Ghoraani, B. (2015). Developing a New Computer-Aided Clinical Decision Support System for Prediction of Successful Postcardioversion Patients with Persistent Atrial Fibrillation. Computational and Mathematical Methods in Medicine, 2015, 1-10. doi:10.1155/2015/527815Stridh, M., & Sommo, L. (2001). Spatiotemporal QRST cancellation techniques for analysis of atrial fibrillation. IEEE Transactions on Biomedical Engineering, 48(1), 105-111. doi:10.1109/10.900266Stridh, M., Sornmo, L., Meurling, C. J., & Olsson, S. B. (2004). Sequential Characterization of Atrial Tachyarrhythmias Based on ECG Time-Frequency Analysis. IEEE Transactions on Biomedical Engineering, 51(1), 100-114. doi:10.1109/tbme.2003.820331Wang, Y., & Jiang, Y. (2008). ISAR Imaging of Rotating Target with Equal Changing Acceleration Based on the Cubic Phase Function. EURASIP Journal on Advances in Signal Processing, 2008, 1-5. doi:10.1155/2008/49138

Applications of Signal Analysis to Atrial Fibrillation

This work was supported by projects TEC2010–20633 from the Spanish Ministry of Science and Innovation and PPII11–0194–8121 from Junta de Comunidades de Castilla-La ManchaRieta Ibañez, JJ.; Alcaraz Martínez, R. (2013). Applications of Signal Analysis to Atrial Fibrillation. En Atrial Fibrillation - Mechanisms and Treatment. InTech. 155-180. https://doi.org/10.5772/5340915518

A study on stability analysis of atrial repolarization variability using ARX model in sinus rhythm and atrial tachycardia ECGs

© 2016 Elsevier Ireland Ltd Background The interaction between the PTa and PP interval dynamics from the surface ECG is seldom explained. Mathematical modeling of these intervals is of interest in finding the relationship between the heart rate and repolarization variability. Objective The goal of this paper is to assess the bounded input bounded output (BIBO) stability in PTa interval (PTaI) dynamics using autoregressive exogenous (ARX) model and to investigate the reason for causing instability in the atrial repolarization process. Methods Twenty-five male subjects in normal sinus rhythm (NSR) and ten male subjects experiencing atrial tachycardia (AT) were included in this study. Five minute long, modified limb lead (MLL) ECGs were recorded with an EDAN SE-1010 PC ECG system. The number of minute ECGs with unstable segments (N us ) and the frequency of premature activation (PA) (i.e. atrial activation) were counted for each ECG recording and compared between AT and NSR subjects. Results The instability in PTaI dynamics was quantified by measuring the numbers of unstable segments in ECG data for each subject. The unstable segments in the PTaI dynamics were associated with the frequency of PA. The presence of PA is not the only factor causing the instability in PTaI dynamics in NSR subjects, and it is found that the cause of instability is mainly due to the heart rate variability (HRV). C onclusion The ARX model showed better prediction of PTa interval dynamics in both groups. The frequency of PA is significantly higher in AT patients than NSR subjects. A more complex model is needed to better identify and characterize healthy heart dynamics

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 171

This bibliography lists 186 reports, articles, and other documents introduced into the NASA scientific and technical information system in August 1977

Aerospace Medicine and Biology: A continuing bibliography (supplement 229)

This bibliography lists 109 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1982

A Novel Deep Learning Technique for Morphology Preserved Fetal ECG Extraction from Mother ECG using 1D-CycleGAN

Monitoring the electrical pulse of fetal heart through a non-invasive fetal electrocardiogram (fECG) can easily detect abnormalities in the developing heart to significantly reduce the infant mortality rate and post-natal complications. Due to the overlapping of maternal and fetal R-peaks, the low amplitude of the fECG, systematic and ambient noises, typical signal extraction methods, such as adaptive filters, independent component analysis, empirical mode decomposition, etc., are unable to produce satisfactory fECG. While some techniques can produce accurate QRS waves, they often ignore other important aspects of the ECG. Our approach, which is based on 1D CycleGAN, can reconstruct the fECG signal from the mECG signal while maintaining the morphology due to extensive preprocessing and appropriate framework. The performance of our solution was evaluated by combining two available datasets from Physionet, "Abdominal and Direct Fetal ECG Database" and "Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations", where it achieved an average PCC and Spectral-Correlation score of 88.4% and 89.4%, respectively. It detects the fQRS of the signal with accuracy, precision, recall and F1 score of 92.6%, 97.6%, 94.8% and 96.4%, respectively. It can also accurately produce the estimation of fetal heart rate and R-R interval with an error of 0.25% and 0.27%, respectively. The main contribution of our work is that, unlike similar studies, it can retain the morphology of the ECG signal with high fidelity. The accuracy of our solution for fetal heart rate and R-R interval length is comparable to existing state-of-the-art techniques. This makes it a highly effective tool for early diagnosis of fetal heart diseases and regular health checkups of the fetus.Comment: 24 pages, 11 figure