Sample Entropy Analysis of Noisy Atrial Electrograms during Atrial Fibrillation

[EN] Most cardiac arrhythmias can be classified as atrial flutter, focal atrial tachycardia, or atrial fibrillation. They have been usually treated using drugs, but catheter ablation has proven more effective. This is an invasive method devised to destroy the heart tissue that disturbs correct heart rhythm. In order to accurately localise the focus of this disturbance, the acquisition and processing of atrial electrograms form the usual mapping technique. They can be single potentials, double potentials, or complex fractionated atrial electrogram (CFAE) potentials, and last ones are the most effective targets for ablation. The electrophysiological substrate is then localised by a suitable signal processing method. Sample Entropy is a statistic scarcely applied to electrograms but can arguably become a powerful tool to analyse these time series, supported by its results in other similar biomedical applications. However, the lack of an analysis of its dependence on the perturbations usually found in electrogram data, such as missing samples or spikes, is even more marked. This paper applied SampEn to the segmentation between non-CFAE and CFAE records and assessed its class segmentation power loss at different levels of these perturbations. The results confirmed that SampEn was able to significantly distinguish between non-CFAE and CFAE records, even under very unfavourable conditions, such as 50% of missing data or 10% of spikes.This research was supported by Research Center for Informatics (no. CZ.02.1.01/0.0/0.0/16-019/0000765).Cirugeda Roldan, EM.; Molina Picó, A.; Novák, D.; Cuesta Frau, D.; Kremen, V. (2018). Sample Entropy Analysis of Noisy Atrial Electrograms during Atrial Fibrillation. Computational and Mathematical Methods in Medicine. https://doi.org/10.1155/2018/1874651SAhmed, S., Claughton, A., & Gould, P. A. (2015). Atrial Flutter — Diagnosis, Management and Treatment. Abnormal Heart Rhythms. doi:10.5772/60700Kirchhof, P., & Calkins, H. (2016). Catheter ablation in patients with persistent atrial fibrillation. European Heart Journal, 38(1), 20-26. doi:10.1093/eurheartj/ehw260Nademanee, K., Lockwood, E., Oketani, N., & Gidney, B. (2010). Catheter ablation of atrial fibrillation guided by complex fractionated atrial electrogram mapping of atrial fibrillation substrate. Journal of Cardiology, 55(1), 1-12. doi:10.1016/j.jjcc.2009.11.002NG, 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.xKottkamp, H., & Hindricks, G. (2007). Complex fractionated atrial electrograms in atrial fibrillation: A promising target for ablation, but why, when, and how? Heart Rhythm, 4(8), 1021-1023. doi:10.1016/j.hrthm.2007.05.011Křemen, V., Lhotská, L., Macaš, M., Čihák, R., Vančura, V., Kautzner, J., & Wichterle, D. (2008). A new approach to automated assessment of fractionation of endocardial electrograms during atrial fibrillation. Physiological Measurement, 29(12), 1371-1381. doi:10.1088/0967-3334/29/12/002Nademanee, K., McKenzie, J., Kosar, E., Schwab, M., Sunsaneewitayakul, B., Vasavakul, T., … Ngarmukos, T. (2004). A new approach for catheter ablation of atrial fibrillation: mapping of the electrophysiologic substrate. Journal of the American College of Cardiology, 43(11), 2044-2053. doi:10.1016/j.jacc.2003.12.054Scherr, D., Dalal, D., Cheema, A., Cheng, A., Henrikson, C. A., Spragg, D., … Dong, J. (2007). Automated detection and characterization of complex fractionated atrial electrograms in human left atrium during atrial fibrillation. Heart Rhythm, 4(8), 1013-1020. doi:10.1016/j.hrthm.2007.04.021Almeida, T. P., Chu, G. S., Salinet, J. L., Vanheusden, F. J., Li, X., Tuan, J. H., … Schlindwein, F. S. (2016). Minimizing discordances in automated classification of fractionated electrograms in human persistent atrial fibrillation. Medical & Biological Engineering & Computing, 54(11), 1695-1706. doi:10.1007/s11517-016-1456-2Molina-Picó, A., Cuesta-Frau, D., Aboy, M., Crespo, C., Miró-Martínez, P., & Oltra-Crespo, S. (2011). Comparative study of approximate entropy and sample entropy robustness to spikes. Artificial Intelligence in Medicine, 53(2), 97-106. doi:10.1016/j.artmed.2011.06.007Cuesta–Frau, D., Miró–Martínez, P., Jordán Núñez, J., Oltra–Crespo, S., & Molina Picó, A. (2017). Noisy EEG signals classification based on entropy metrics. Performance assessment using first and second generation statistics. Computers in Biology and Medicine, 87, 141-151. doi:10.1016/j.compbiomed.2017.05.028Demont-Guignard, S., Benquet, P., Gerber, U., & Wendling, F. (2009). Analysis of Intracerebral EEG Recordings of Epileptic Spikes: Insights From a Neural Network Model. IEEE Transactions on Biomedical Engineering, 56(12), 2782-2795. doi:10.1109/tbme.2009.2028015Molina–Picó, A., Cuesta–Frau, D., Miró–Martínez, P., Oltra–Crespo, S., & Aboy, M. (2013). Influence of QRS complex detection errors on entropy algorithms. Application to heart rate variability discrimination. Computer Methods and Programs in Biomedicine, 110(1), 2-11. doi:10.1016/j.cmpb.2012.10.014Ganesan, P., Cherry, E. M., Pertsov, A. M., & Ghoraani, B. (2015). Characterization of Electrograms from Multipolar Diagnostic Catheters during Atrial Fibrillation. BioMed Research International, 2015, 1-9. doi:10.1155/2015/272954Lake, D. E., Richman, J. S., Griffin, M. P., & Moorman, J. R. (2002). Sample entropy analysis of neonatal heart rate variability. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 283(3), R789-R797. doi:10.1152/ajpregu.00069.2002Kim, K. K., Baek, H. J., Lim, Y. G., & Park, K. S. (2012). Effect of missing RR-interval data on nonlinear heart rate variability analysis. Computer Methods and Programs in Biomedicine, 106(3), 210-218. doi:10.1016/j.cmpb.2010.11.011Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278(6), H2039-H2049. doi:10.1152/ajpheart.2000.278.6.h2039Cirugeda–Roldán, E., Novak, D., Kremen, V., Cuesta–Frau, D., Keller, M., Luik, A., & Srutova, M. (2015). Characterization of Complex Fractionated Atrial Electrograms by Sample Entropy: An International Multi-Center Study. Entropy, 17(12), 7493-7509. doi:10.3390/e17117493PORTER, M., SPEAR, W., AKAR, J. G., HELMS, R., BRYSIEWICZ, N., SANTUCCI, P., & WILBER, D. J. (2008). Prospective Study of Atrial Fibrillation Termination During Ablation Guided by Automated Detection of Fractionated Electrograms. Journal of Cardiovascular Electrophysiology, 19(6), 613-620. doi:10.1111/j.1540-8167.2008.01189.xKonings, K. T., Kirchhof, C. J., Smeets, J. R., Wellens, H. J., Penn, O. C., & Allessie, M. A. (1994). High-density mapping of electrically induced atrial fibrillation in humans. Circulation, 89(4), 1665-1680. doi:10.1161/01.cir.89.4.1665Fay, M. P., & Proschan, M. A. (2010). Wilcoxon-Mann-Whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules. Statistics Surveys, 4(0), 1-39. doi:10.1214/09-ss051Richman, J. S. (2007). Sample Entropy Statistics and Testing for Order in Complex Physiological Signals. Communications in Statistics - Theory and Methods, 36(5), 1005-1019. doi:10.1080/03610920601036481Pincus, S. M., Gladstone, I. M., & Ehrenkranz, R. A. (1991). A regularity statistic for medical data analysis. Journal of Clinical Monitoring, 7(4), 335-345. doi:10.1007/bf01619355Alcaraz, R., & Rieta, J. J. (2009). Non-invasive organization variation assessment in the onset and termination of paroxysmal atrial fibrillation. Computer Methods and Programs in Biomedicine, 93(2), 148-154. doi:10.1016/j.cmpb.2008.09.001Alcaraz, R., Abásolo, D., Hornero, R., & Rieta, J. J. (2010). Optimal parameters study for sample entropy-based atrial fibrillation organization analysis. Computer Methods and Programs in Biomedicine, 99(1), 124-132. doi:10.1016/j.cmpb.2010.02.009Costa, M., Goldberger, A. L., & Peng, C.-K. (2002). Multiscale Entropy Analysis of Complex Physiologic Time Series. Physical Review Letters, 89(6). doi:10.1103/physrevlett.89.06810

Electrical Stimulation Modulates High γ Activity and Human Memory Performance.

Direct electrical stimulation of the brain has emerged as a powerful treatment for multiple neurological diseases, and as a potential technique to enhance human cognition. Despite its application in a range of brain disorders, it remains unclear how stimulation of discrete brain areas affects memory performance and the underlying electrophysiological activities. Here, we investigated the effect of direct electrical stimulation in four brain regions known to support declarative memory: hippocampus (HP), parahippocampal region (PH) neocortex, prefrontal cortex (PF), and lateral temporal cortex (TC). Intracranial EEG recordings with stimulation were collected from 22 patients during performance of verbal memory tasks. We found that high γ (62-118 Hz) activity induced by word presentation was modulated by electrical stimulation. This modulatory effect was greatest for trials with poor memory encoding. The high γ modulation correlated with the behavioral effect of stimulation in a given brain region: it was negative, i.e., the induced high γ activity was decreased, in the regions where stimulation decreased memory performance, and positive in the lateral TC where memory enhancement was observed. Our results suggest that the effect of electrical stimulation on high γ activity induced by word presentation may be a useful biomarker for mapping memory networks and guiding therapeutic brain stimulation

Human Verbal Memory Encoding Is Hierarchically Distributed in a Continuous Processing Stream.

Processing of memory is supported by coordinated activity in a network of sensory, association, and motor brain regions. It remains a major challenge to determine where memory is encoded for later retrieval. Here, we used direct intracranial brain recordings from epilepsy patients performing free recall tasks to determine the temporal pattern and anatomical distribution of verbal memory encoding across the entire human cortex. High γ frequency activity (65-115 Hz) showed consistent power responses during encoding of subsequently recalled and forgotten words on a subset of electrodes localized in 16 distinct cortical areas activated in the tasks. More of the high γ power during word encoding, and less power before and after the word presentation, was characteristic of successful recall and observed across multiple brain regions. Latencies of the induced power changes and this subsequent memory effect (SME) between the recalled and forgotten words followed an anatomical sequence from visual to prefrontal cortical areas. Finally, the magnitude of the memory effect was unexpectedly found to be the largest in selected brain regions both at the top and at the bottom of the processing stream. These included the language processing areas of the prefrontal cortex and the early visual areas at the junction of the occipital and temporal lobes. Our results provide evidence for distributed encoding of verbal memory organized along a hierarchical posterior-to-anterior processing stream

Characterization of complex fractionated atrial electrograms by Sample Entropy: An international multi-center study

Atrial fibrillation (AF) is the most commonly clinically-encountered arrhythmia. Catheter ablation of AF is mainly based on trigger elimination and modification of the AF substrate. Substrate mapping ablation of complex fractionated atrial electrograms (CFAEs) has emerged to be a promising technique. To improve substrate mapping based on CFAE analysis, automatic detection algorithms need to be developed in order to simplify and accelerate the ablation procedures. According to the latest studies, the level of fractionation has been shown to be promisingly well estimated from CFAE measured during radio frequency (RF) ablation of AF. The nature of CFAE is generally nonlinear and nonstationary, so the use of complexity measures is considered to be the appropriate technique for the analysis of AF records. This work proposes the use of sample entropy (SampEn), not only as a way to discern between non-fractionated and fractionated atrial electrograms (A-EGM), but also as a tool for characterizing the degree of A-EGM regularity, which is linked to changes in the AF substrate and to heart tissue damage. The use of SampEn combined with a blind parameter estimation optimization process enables the classification between CFAE and non-CFAE with statistical significance (p < 0:001), 0.89 area under the ROC, 86% specificity and 77% sensitivity over a mixed database of A-EGM combined from two independent CFAE signal databases, recorded during RF ablation of AF in two EU countries (542 signals in total). On the basis of the results obtained in this study, it can be suggested that the use of SampEn is suitable for real-time support during navigation of RF ablation of AF, as only 1.5 seconds of signal segments need to be analyzed.This work has been supported by the Spanish Ministry of Science and Innovation, Research Project TEC 2009-14222, by the Ministry of Education Youth and Sports of the Czech Republic, the Grant Agency of the Czech Technical University in Prague No. SGS13/203/OHK3/3T/13 and by the Czech Science 300 Foundation post-doctoral GACR research project GACR #P103/11/P106.Cirugeda Roldán, EM.; Novak, D.; Kremen, V.; Cuesta Frau, D.; Keller, M.; Luik, A.; Srutova, M. (2015). Characterization of complex fractionated atrial electrograms by Sample Entropy: An international multi-center study. Entropy. 17(11):7493-7509. https://doi.org/10.3390/e17117493S749375091711Haïssaguerre, 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/nejm199809033391003Nademanee, K., Schwab, M., Porath, J., & Abbo, A. (2006). How to perform electrogram-guided atrial fibrillation ablation. Heart Rhythm, 3(8), 981-984. doi:10.1016/j.hrthm.2006.03.018PORTER, M., SPEAR, W., AKAR, J. G., HELMS, R., BRYSIEWICZ, N., SANTUCCI, P., & WILBER, D. J. (2008). Prospective Study of Atrial Fibrillation Termination During Ablation Guided by Automated Detection of Fractionated Electrograms. Journal of Cardiovascular Electrophysiology, 19(6), 613-620. doi:10.1111/j.1540-8167.2008.01189.xHaïssaguerre, M., Hocini, M., Sanders, P., Takahashi, Y., Rotter, M., Sacher, F., … Jaïs, P. (2006). Localized Sources Maintaining Atrial Fibrillation Organized by Prior Ablation. Circulation, 113(5), 616-625. doi:10.1161/circulationaha.105.546648Schmitt, C., Ndrepepa, G., Weber, S., Schmieder, S., Weyerbrock, S., Schneider, M., … Schömig, A. (2002). Biatrial multisite mapping of atrial premature complexes triggering onset of atrial fibrillation. The American Journal of Cardiology, 89(12), 1381-1387. doi:10.1016/s0002-9149(02)02350-0NDREPEPA, G., KARCH, M. R., SCHNEIDER, M. A. E., WEYERBROCK, S., SCHREIECK, J., DEISENHOFER, I., … SCHMITT, C. (2002). Characterization of Paroxysmal and Persistent Atrial Fibrillation in the Human Left Atrium During Initiation and Sustained Episodes. Journal of Cardiovascular Electrophysiology, 13(6), 525-532. doi:10.1046/j.1540-8167.2002.00525.xNademanee, K., McKenzie, J., Kosar, E., Schwab, M., Sunsaneewitayakul, B., Vasavakul, T., … Ngarmukos, T. (2004). A new approach for catheter ablation of atrial fibrillation: mapping of the electrophysiologic substrate. Journal of the American College of Cardiology, 43(11), 2044-2053. doi:10.1016/j.jacc.2003.12.054Oral, H., Chugh, A., Good, E., Wimmer, A., Dey, S., Gadeela, N., … Morady, F. (2007). Radiofrequency Catheter Ablation of Chronic Atrial Fibrillation Guided by Complex Electrograms. Circulation, 115(20), 2606-2612. doi:10.1161/circulationaha.107.691386Kumagai, K. (2007). Patterns of activation in human atrial fibrillation. Heart Rhythm, 4(3), S7-S12. doi:10.1016/j.hrthm.2006.12.013Mainardi, L. T., Corino, V. D., Lombardi, L., Tondo, C., Mantica, M., Lombardi, F., & Cerutti, S. (2004). BioMedical Engineering OnLine, 3(1), 37. doi:10.1186/1475-925x-3-37RAVELLI, F., FAES, L., SANDRINI, L., GAITA, F., ANTOLINI, R., SCAGLIONE, M., & NOLLO, G. (2005). Wave Similarity Mapping Shows the Spatiotemporal Distribution of Fibrillatory Wave Complexity in the Human Right Atrium During Paroxysmal and Chronic Atrial Fibrillation. Journal of Cardiovascular Electrophysiology, 16(10), 1071-1076. doi:10.1111/j.1540-8167.2005.50008.xNG, 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.xTakahashi, Y., O’Neill, M. D., Hocini, M., Dubois, R., Matsuo, S., Knecht, S., … Haïssaguerre, M. (2008). Characterization of Electrograms Associated With Termination of Chronic Atrial Fibrillation by Catheter Ablation. Journal of the American College of Cardiology, 51(10), 1003-1010. doi:10.1016/j.jacc.2007.10.056Křemen, V., Lhotská, L., Macaš, M., Čihák, R., Vančura, V., Kautzner, J., & Wichterle, D. (2008). A new approach to automated assessment of fractionation of endocardial electrograms during atrial fibrillation. Physiological Measurement, 29(12), 1371-1381. doi:10.1088/0967-3334/29/12/002Ciaccio, E. J., Biviano, A. B., Whang, W., Gambhir, A., & Garan, H. (2010). Different characteristics of complex fractionated atrial electrograms in acute paroxysmal versus long-standing persistent atrial fibrillation. Heart Rhythm, 7(9), 1207-1215. doi:10.1016/j.hrthm.2010.06.018LIN, Y.-J., LO, M.-T., LIN, C., CHANG, S.-L., LO, L.-W., HU, Y.-F., … CHEN, S.-A. (2012). Nonlinear Analysis of Fibrillatory Electrogram Similarity to Optimize the Detection of Complex Fractionated Electrograms During Persistent Atrial Fibrillation. Journal of Cardiovascular Electrophysiology, 24(3), 280-289. doi:10.1111/jce.12019NG, J., BORODYANSKIY, A. I., CHANG, E. T., VILLUENDAS, R., DIBS, S., KADISH, A. H., & GOLDBERGER, J. J. (2010). Measuring the Complexity of Atrial Fibrillation Electrograms. Journal of Cardiovascular Electrophysiology, 21(6), 649-655. doi:10.1111/j.1540-8167.2009.01695.xGanesan, A. N., Kuklik, P., Lau, D. H., Brooks, A. G., Baumert, M., Lim, W. W., … Sanders, P. (2013). Bipolar Electrogram Shannon Entropy at Sites of Rotational Activation. Circulation: Arrhythmia and Electrophysiology, 6(1), 48-57. doi:10.1161/circep.112.976654Jacquemet, V., & Henriquez, C. S. (2009). Genesis of complex fractionated atrial electrograms in zones of slow conduction: A computer model of microfibrosis. Heart Rhythm, 6(6), 803-810. doi:10.1016/j.hrthm.2009.02.026Jadidi, A. S., Duncan, E., Miyazaki, S., Lellouche, N., Shah, A. J., Forclaz, A., … Jaïs, P. (2012). Functional Nature of Electrogram Fractionation Demonstrated by Left Atrial High-Density Mapping. Circulation: Arrhythmia and Electrophysiology, 5(1), 32-42. doi:10.1161/circep.111.964197Ferrario, M., Signorini, M. G., Magenes, G., & Cerutti, S. (2006). Comparison of Entropy-Based Regularity Estimators: Application to the Fetal Heart Rate Signal for the Identification of Fetal Distress. IEEE Transactions on Biomedical Engineering, 53(1), 119-125. doi:10.1109/tbme.2005.859809Lewis, M. J., & Short, A. L. (2007). Sample entropy of electrocardiographic RR and QT time-series data during rest and exercise. Physiological Measurement, 28(6), 731-744. doi:10.1088/0967-3334/28/6/011Al-Angari, H. M., & Sahakian, A. V. (2007). Use of Sample Entropy Approach to Study Heart Rate Variability in Obstructive Sleep Apnea Syndrome. IEEE Transactions on Biomedical Engineering, 54(10), 1900-1904. doi:10.1109/tbme.2006.889772Lake, D. E., Richman, J. S., Griffin, M. P., & Moorman, J. R. (2002). Sample entropy analysis of neonatal heart rate variability. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 283(3), R789-R797. doi:10.1152/ajpregu.00069.2002Cervigón, R., Moreno, J., Reilly, R. B., Millet, J., Pérez-Villacastín, J., & Castells, F. (2010). Entropy measurements in paroxysmal and persistent atrial fibrillation. Physiological Measurement, 31(7), 1011-1020. doi:10.1088/0967-3334/31/7/010Alcaraz, R., & Rieta, J. J. (2009). The application of nonlinear metrics to assess organization differences in short recordings of paroxysmal and persistent atrial fibrillation. Physiological Measurement, 31(1), 115-130. doi:10.1088/0967-3334/31/1/008Orozco-Duque, A., Novak, D., Kremen, V., & Bustamante, J. (2015). Multifractal analysis for grading complex fractionated electrograms in atrial fibrillation. Physiological Measurement, 36(11), 2269-2284. doi:10.1088/0967-3334/36/11/2269Ugarte, J. P., Orozco-Duque, A., Tobón, C., Kremen, V., Novak, D., Saiz, J., … Bustamante, J. (2014). Dynamic Approximate Entropy Electroanatomic Maps Detect Rotors in a Simulated Atrial Fibrillation Model. PLoS ONE, 9(12), e114577. doi:10.1371/journal.pone.0114577Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278(6), H2039-H2049. doi:10.1152/ajpheart.2000.278.6.h2039STILES, M. K., BROOKS, A. G., JOHN, B., WILSON, L., KUKLIK, P., DIMITRI, H., … SANDERS, P. (2008). The Effect of Electrogram Duration on Quantification of Complex Fractionated Atrial Electrograms and Dominant Frequency. Journal of Cardiovascular Electrophysiology, 19(3), 252-258. doi:10.1111/j.1540-8167.2007.01034.xVerma, A., Novak, P., Macle, L., Whaley, B., Beardsall, M., Wulffhart, Z., & Khaykin, Y. (2008). A prospective, multicenter evaluation of ablating complex fractionated electrograms (CFEs) during atrial fibrillation (AF) identified by an automated mapping algorithm: Acute effects on AF and efficacy as an adjuvant strategy. Heart Rhythm, 5(2), 198-205. doi:10.1016/j.hrthm.2007.09.027Schilling, C., Keller, M., Scherr, D., Oesterlein, T., Haïssaguerre, M., Schmitt, C., … Luik, A. (2015). Fuzzy decision tree to classify complex fractionated atrial electrograms. Biomedical Engineering / Biomedizinische Technik, 60(3). doi:10.1515/bmt-2014-0110Garcia-Gonzalez, M. A., Fernandez-Chimeno, M., & Ramos-Castro, J. (2009). Errors in the Estimation of Approximate Entropy and Other Recurrence-Plot-Derived Indices Due to the Finite Resolution of RR Time Series. IEEE Transactions on Biomedical Engineering, 56(2), 345-351. doi:10.1109/tbme.2008.2005951Konings, K. T., Kirchhof, C. J., Smeets, J. R., Wellens, H. J., Penn, O. C., & Allessie, M. A. (1994). High-density mapping of electrically induced atrial fibrillation in humans. Circulation, 89(4), 1665-1680. doi:10.1161/01.cir.89.4.1665Lake, D. E., & Moorman, J. R. (2011). Accurate estimation of entropy in very short physiological time series: the problem of atrial fibrillation detection in implanted ventricular devices. American Journal of Physiology-Heart and Circulatory Physiology, 300(1), H319-H325. doi:10.1152/ajpheart.00561.2010HOEKSTRA, B. P. T., DIKS, C. G. H., ALLESSIE, M. A., & GOEDB, J. (1995). Nonlinear Analysis of Epicardial Atrial Electrograms of Electrically Induced Atrial Fibrillation in Man. Journal of Cardiovascular Electrophysiology, 6(6), 419-440. doi:10.1111/j.1540-8167.1995.tb00416.

Independent Component Analysis and Decision Trees for ECG Holter Recording De-Noising

<div><p>We have developed a method focusing on ECG signal de-noising using Independent component analysis (ICA). This approach combines JADE source separation and binary decision tree for identification and subsequent ECG noise removal. In order to to test the efficiency of this method comparison to standard filtering a wavelet- based de-noising method was used. Freely data available at Physionet medical data storage were evaluated. Evaluation criteria was root mean square error (RMSE) between original ECG and filtered data contaminated with artificial noise. Proposed algorithm achieved comparable result in terms of standard noises (power line interference, base line wander, EMG), but noticeably significantly better results were achieved when uncommon noise (electrode cable movement artefact) were compared.</p></div

Results for the power line interference artefact.

<p>Horizontal axis shows level of added noise. Boxplots shows RMSE for used database of signals. Filtered methods that were used are differentiated by colors. Those boxplots where notches are marked with bold line around borders means that they means are not significantly different. We can observe that our algorithm works similarly to wavelet based de-noising and significantly better than filtering algorithm.</p

Results for the base line wander artefact.

<p>Horizontal axis shows level of added noise. Boxplots shows RMSE for used database of signals. Filtered methods that were used are differentiated by colors. We can observe that our algorithm achieves good results. The wavelet filtering algorithm has difficulties with this type of the artefact due to its simulation as slow sinus wave.</p