nanoSTAIR: a new strategic proposal to impulse standardization in nanotechnology research

Nanotechnology is considered one of the key technologies of the 21st century within Europe and a Key-Enabling Technology (KET) by Horizon 2020. Standardization has been identified in H2020 as one of the innovation-support measures by bridging the gap between research and the market, and helping the fast and easy transfer of research results to the European and international market. The development of new and improved standards requires high quality technical information, creating a fundamental interdependency between the standardization and research communities. In the frame of project nanoSTAIR (GA 319092), the present paper describes the European scenario on research and standardization in nanotechnology and presents a proposal of a European strategy (nanoSTAIR) to impulse direct "pipelines" between research and standardization. In addition, strategic actions focused on integration of standardization in the R&D projects, from the early stages of the design of a future business (Project Proposal), are also described.European Commission, through the Seventh Framework Programme (GA 319092)

The stationary wavelet transform as an efficient reductor of powerline interference for atrial bipolar electrograms in cardiac electrophysiology

[EN] Objective :The most relevant source of signal contamination in the cardiac electrophysiology (EP) laboratory is the ubiquitous powerline interference (PLI). To reduce this perturbation, algorithms including common fixed-bandwidth and adaptive-notch filters have been proposed. Although such methods have proven to add artificial fractionation to intra-atrial electrograms (EGMs), they are still frequently used. However, such morphological alteration can conceal the accurate interpretation of EGMs, specially to evaluate the mechanisms supporting atrial fibrillation (AF), which is the most common cardiac arrhythmia. Given the clinical relevance of AF, a novel algorithm aimed at reducing PLI on highly contaminated bipolar EGMs and, simultaneously, preserving their morphology is proposed. Approach: The method is based on the wavelet shrinkage and has been validated through customized indices on a set of synthesized EGMs to accurately quantify the achieved level of PLI reduction and signal morphology alteration. Visual validation of the algorithm¿s performance has also been included for some real EGM excerpts. Main results: The method has outperformed common filtering-based and wavelet-based strategies in the analyzed scenario. Moreover, it possesses advantages such as insensitivity to amplitude and frequency variations in the PLI, and the capability of joint removal of several interferences. Significance: The use of this algorithm in routine cardiac EP studies may enable improved and truthful evaluation of AF mechanisms.Research supported by grants DPI2017-83952-C3 MINECO/AEI/FEDER, UE and SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha.Martinez-Iniesta, M.; Ródenas, J.; Rieta, JJ.; Alcaraz, R. (2019). The stationary wavelet transform as an efficient reductor of powerline interference for atrial bipolar electrograms in cardiac electrophysiology. Physiological Measurement. 40(7):1-17. https://doi.org/10.1088/1361-6579/ab2cb8S117407Addison, P. S. (2017). The Illustrated Wavelet Transform Handbook. doi:10.1201/9781315372556Allen, D. P. (2009). A frequency domain Hampel filter for blind rejection of sinusoidal interference from electromyograms. Journal of Neuroscience Methods, 177(2), 303-310. doi:10.1016/j.jneumeth.2008.10.019Atienza, 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.2008.10.024Atienza, F., Almendral, J., Moreno, J., Vaidyanathan, R., Talkachou, A., Kalifa, J., … Berenfeld, O. (2006). Activation of Inward Rectifier Potassium Channels Accelerates Atrial Fibrillation in Humans. Circulation, 114(23), 2434-2442. doi:10.1161/circulationaha.106.633735Bahoura, M., & Ezzaidi, H. (2011). FPGA-Implementation of Discrete Wavelet Transform with Application to Signal Denoising. Circuits, Systems, and Signal Processing, 31(3), 987-1015. doi:10.1007/s00034-011-9355-0Baumert, M., Sanders, P., & Ganesan, A. (2016). Quantitative-Electrogram-Based Methods for Guiding Catheter Ablation in Atrial Fibrillation. Proceedings of the IEEE, 104(2), 416-431. doi:10.1109/jproc.2015.2505318Castillo, E., Morales, D. P., García, A., Martínez-Martí, F., Parrilla, L., & Palma, A. J. (2013). Noise Suppression in ECG Signals through Efficient One-Step Wavelet Processing Techniques. Journal of Applied Mathematics, 2013, 1-13. doi:10.1155/2013/763903Chen, S.-W., & Chen, Y.-H. (2015). Hardware Design and Implementation of a Wavelet De-Noising Procedure for Medical Signal Preprocessing. Sensors, 15(10), 26396-26414. doi:10.3390/s151026396El B’charri, O., Latif, R., Elmansouri, K., Abenaou, A., & Jenkal, W. (2017). ECG signal performance de-noising assessment based on threshold tuning of dual-tree wavelet transform. BioMedical Engineering OnLine, 16(1). doi:10.1186/s12938-017-0315-1Everett, T. H., Lai-Chow Kok, Vaughn, R. H., Moorman, R., & Haines, D. E. (2001). Frequency domain algorithm for quantifying atrial fibrillation organization to increase defibrillation efficacy. IEEE Transactions on Biomedical Engineering, 48(9), 969-978. doi:10.1109/10.942586Ganesan, 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.112.976654Goldberger, 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.e215GOLDBERGER, J. J. (2017). Substrate Ablation for Treatment of Atrial Fibrillation: Back to Basics. Journal of Cardiovascular Electrophysiology, 28(2), 156-158. doi:10.1111/jce.13149Gutiérrez-Gnecchi, J. A., Morfin-Magaña, R., Lorias-Espinoza, D., Tellez-Anguiano, A. del C., Reyes-Archundia, E., Méndez-Patiño, A., & Castañeda-Miranda, R. (2017). DSP-based arrhythmia classification using wavelet transform and probabilistic neural network. Biomedical Signal Processing and Control, 32, 44-56. doi:10.1016/j.bspc.2016.10.005Hamilton, P. S. (1996). A comparison of adaptive and nonadaptive filters for reduction of power line interference in the ECG. IEEE Transactions on Biomedical Engineering, 43(1), 105-109. doi:10.1109/10.477707Heijman, J., Algalarrondo, V., Voigt, N., Melka, J., Wehrens, X. H. T., Dobrev, D., & Nattel, S. (2015). The value of basic research insights into atrial fibrillation mechanisms as a guide to therapeutic innovation: a critical analysis. Cardiovascular Research, 109(4), 467-479. doi:10.1093/cvr/cvv275Honarbakhsh, S., Schilling, R. J., Orini, M., Providencia, R., Keating, E., Finlay, M., … Hunter, R. J. (2019). Structural remodeling and conduction velocity dynamics in the human left atrium: Relationship with reentrant mechanisms sustaining atrial fibrillation. Heart Rhythm, 16(1), 18-25. doi:10.1016/j.hrthm.2018.07.019Jadidi, A. S., Lehrmann, H., Weber, R., Park, C.-I., & Arentz, T. (2013). Optimizing Signal Acquisition and Recording in an Electrophysiology Laboratory. Cardiac Electrophysiology Clinics, 5(2), 137-142. doi:10.1016/j.ccep.2013.01.005JARMAN, J. W. E., WONG, T., KOJODJOJO, P., SPOHR, H., DAVIES, J. E. R., ROUGHTON, M., … PETERS, N. S. (2014). Organizational Index Mapping to Identify Focal Sources During Persistent Atrial Fibrillation. Journal of Cardiovascular Electrophysiology, 25(4), 355-363. doi:10.1111/jce.12352KOTTKAMP, H., BERG, J., BENDER, R., RIEGER, A., & SCHREIBER, D. (2015). Box Isolation of Fibrotic Areas (BIFA): A Patient-Tailored Substrate Modification Approach for Ablation of Atrial Fibrillation. Journal of Cardiovascular Electrophysiology, 27(1), 22-30. doi:10.1111/jce.12870Krijthe, 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/eht280Lau, D. H., Schotten, U., Mahajan, R., Antic, N. A., Hatem, S. N., Pathak, R. K., … Sanders, P. (2015). Novel mechanisms in the pathogenesis of atrial fibrillation: practical applications. European Heart Journal, 37(20), 1573-1581. doi:10.1093/eurheartj/ehv375Lenis, G., Pilia, N., Loewe, A., Schulze, W. H. W., & Dössel, O. (2017). Comparison of Baseline Wander Removal Techniques considering the Preservation of ST Changes in the Ischemic ECG: A Simulation Study. Computational and Mathematical Methods in Medicine, 2017, 1-13. doi:10.1155/2017/9295029Levkov, C., Mihov, G., Ivanov, R., Daskalov, I., Christov, I., & Dotsinsky, I. (2005). Removal of power-line interference from the ECG: a review of the subtraction procedure. BioMedical Engineering OnLine, 4(1). doi:10.1186/1475-925x-4-50Lian, J., Garner, G., Muessig, D., & Lang, V. (2010). A simple method to quantify the morphological similarity between signals. Signal Processing, 90(2), 684-688. doi:10.1016/j.sigpro.2009.07.010Liu, H., Li, Y., Zhou, Y., Jing, X., & Truong, T.-K. (2018). Joint power line interference suppression and ECG signal recovery in transform domains. Biomedical Signal Processing and Control, 44, 58-66. doi:10.1016/j.bspc.2018.04.001Mamun, M., Al-Kadi, M., & Marufuzzaman, M. (2013). Effectiveness of Wavelet Denoising on Electroencephalogram Signals. Journal of Applied Research and Technology, 11(1), 156-160. doi:10.1016/s1665-6423(13)71524-4Martens, S. M. M., Mischi, M., Oei, S. G., & Bergmans, J. W. M. (2006). An Improved Adaptive Power Line Interference Canceller for Electrocardiography. IEEE Transactions on Biomedical Engineering, 53(11), 2220-2231. doi:10.1109/tbme.2006.883631Mart�nez, M., R�denas, J., Alcaraz, R., & Rieta, J. J. (2017). Application of the Stationary Wavelet Transform to Reduce Power-line Interference in Atrial Electrograms. 2017 Computing in Cardiology Conference (CinC). doi:10.22489/cinc.2017.112-049Martínez-Iniesta, M., Ródenas, J., Alcaraz, R., & Rieta, J. J. (2017). Waveform Integrity in Atrial Fibrillation: The Forgotten Issue of Cardiac Electrophysiology. Annals of Biomedical Engineering, 45(8), 1890-1907. doi:10.1007/s10439-017-1832-6Metting van Rijn, A. C., Peper, A., & Grimbergen, C. A. (1990). High-quality recording of bioelectric events. Medical & Biological Engineering & Computing, 28(5), 389-397. doi:10.1007/bf02441961Nademanee, 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.2003.12.054Naseri, H., & Homaeinezhad, M. R. (2012). Computerized quality assessment of phonocardiogram signal measurement-acquisition parameters. Journal of Medical Engineering & Technology, 36(6), 308-318. doi:10.3109/03091902.2012.684832NG, 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.xNg, J., Sehgal, V., Ng, J. K., Gordon, D., & Goldberger, J. J. (2014). Iterative Method to Detect Atrial Activations and Measure Cycle Length From Electrograms During Atrial Fibrillation. IEEE Transactions on Biomedical Engineering, 61(2), 273-278. doi:10.1109/tbme.2013.2290003Oesterlein, T. G., Lenis, G., Rudolph, D.-T., Luik, A., Verma, B., Schmitt, C., & Dössel, O. (2015). Removing ventricular far-field signals in intracardiac electrograms during stable atrial tachycardia using the periodic component analysis. Journal of Electrocardiology, 48(2), 171-180. doi:10.1016/j.jelectrocard.2014.12.004Platonov, 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/euu249Poornachandra, S., & Kumaravel, N. (2008). A novel method for the elimination of power line frequency in ECG signal using hyper shrinkage function. Digital Signal Processing, 18(2), 116-126. doi:10.1016/j.dsp.2007.03.011Quintanilla, J. G., Pérez-Villacastín, J., Pérez-Castellano, N., Pandit, S. V., Berenfeld, O., Jalife, J., & Filgueiras-Rama, D. (2016). Mechanistic Approaches to Detect, Target, and Ablate the Drivers of Atrial Fibrillation. Circulation: Arrhythmia and Electrophysiology, 9(1). doi:10.1161/circep.115.002481Ravelli, F., Masè, M., Cristoforetti, A., Marini, M., & Disertori, M. (2014). The logical operator map identifies novel candidate markers for critical sites in patients with atrial fibrillation. Progress in Biophysics and Molecular Biology, 115(2-3), 186-197. doi:10.1016/j.pbiomolbio.2014.07.006Schanze, T. (2017). Removing noise in biomedical signal recordings by singular value decomposition. Current Directions in Biomedical Engineering, 3(2), 253-256. doi:10.1515/cdbme-2017-0052Schotten, 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.12492Singh, B. N., & Tiwari, A. K. (2006). Optimal selection of wavelet basis function applied to ECG signal denoising. Digital Signal Processing, 16(3), 275-287. doi:10.1016/j.dsp.2005.12.003Van Wagoner, D. R., Piccini, J. P., Albert, C. M., Anderson, M. E., Benjamin, E. J., Brundel, B., … Wehrens, X. H. T. (2015). Progress toward the prevention and treatment of atrial fibrillation: A summary of the Heart Rhythm Society Research Forum on the Treatment and Prevention of Atrial Fibrillation, Washington, DC, December 9–10, 2013. Heart Rhythm, 12(1), e5-e29. doi:10.1016/j.hrthm.2014.11.011Venkatachalam, K. L., Herbrandson, J. E., & Asirvatham, S. J. (2011). Signals and Signal Processing for the Electrophysiologist. Circulation: Arrhythmia and Electrophysiology, 4(6), 965-973. doi:10.1161/circep.111.96430

Assessing Safety-Critical Systems from Operational Testing: A Study on Autonomous Vehicles

Context: Demonstrating high reliability and safety for safety-critical systems (SCSs) remains a hard problem. Diverse evidence needs to be combined in a rigorous way: in particular, results of operational testing with other evidence from design and verification. Growing use of machine learning in SCSs, by precluding most established methods for gaining assurance, makes evidence from operational testing even more important for supporting safety and reliability claims. Objective: We revisit the problem of using operational testing to demonstrate high reliability. We use Autonomous Vehicles (AVs) as a current example. AVs are making their debut on public roads: methods for assessing whether an AV is safe enough are urgently needed. We demonstrate how to answer 5 questions that would arise in assessing an AV type, starting with those proposed by a highly-cited study. Method: We apply new theorems extending our Conservative Bayesian Inference (CBI) approach, which exploit the rigour of Bayesian methods while reducing the risk of involuntary misuse associated (we argue) with now-common applications of Bayesian inference; we define additional conditions needed for applying these methods to AVs. Results: Prior knowledge can bring substantial advantages if the AV design allows strong expectations of safety before road testing. We also show how naive attempts at conservative assessment may lead to over-optimism instead; why extrapolating the trend of disengagements (take-overs by human drivers) is not suitable for safety claims; use of knowledge that an AV has moved to a “less stressful” environment. Conclusion: While some reliability targets will remain too high to be practically verifiable, our CBI approach removes a major source of doubt: it allows use of prior knowledge without inducing dangerously optimistic biases. For certain ranges of required reliability and prior beliefs, CBI thus supports feasible, sound arguments. Useful conservative claims can be derived from limited prior knowledge

Safety demonstration for a rail signaling application in nominal and degraded modes using formal proof

International audienceThis chapter presents the proof process used by Thales and Autonomous Operator of Parisian Transports (RATP) to demonstrate the safety of the signaling systems used for the RATP network in Paris. It introduces the rail application concerned by the author's proof activities, the Thales system used for the metro. The chapter then presents the models used in the formal proof process, before describing the proof suite designed by Prover Technology. The results of application of the proof process to the Thales signaling system for RATP line are described and discussed in detail, before considering a number of potential improvements. The chapter presents a brief overview of the architecture of the PMI system. It discusses the computerized interlocking module (CIM) subsystem, which constitutes the operational core of the signaling syste