Variational quantum circuits for machine learning. An application for the detection of weak signals

[EN] Featured Application Quantum classifier to detect weak signals. Quantum computing is a new paradigm for a multitude of computing applications. This study presents the technologies that are currently available for the physical implementation of qubits and quantum gates, establishing their main advantages and disadvantages and the available frameworks for programming and implementing quantum circuits. One of the main applications for quantum computing is the development of new algorithms for machine learning. In this study, an implementation of a quantum circuit based on support vector machines (SVMs) is described for the resolution of classification problems. This circuit is specially designed for the noisy intermediate-scale quantum (NISQ) computers that are currently available. As an experiment, the circuit is tested on a real quantum computer based on superconducting qubits for an application to detect weak signals of the future. Weak signals are indicators of incipient changes that will have a future impact. Even for experts, the detection of these events is complicated since it is too early to predict this impact. The data obtained with the experiment shows promising results but also confirms that ongoing technological development is still required to take full advantage of quantum computing.Griol-Barres, I.; Milla, S.; Cebrián Ferriols, AJ.; Mansoori, Y.; Millet Roig, J. (2021). Variational quantum circuits for machine learning. An application for the detection of weak signals. Applied Sciences. 11(14):1-22. https://doi.org/10.3390/app11146427S122111

Detecting Weak Signals of the Future: A System Implementation Based on Text Mining and Natural Language Processing

[EN] Organizations, companies and start-ups need to cope with constant changes on the market which are difficult to predict. Therefore, the development of new systems to detect significant future changes is vital to make correct decisions in an organization and to discover new opportunities. A system based on business intelligence techniques is proposed to detect weak signals, that are related to future transcendental changes. While most known solutions are based on the use of structured data, the proposed system quantitatively detects these signals using heterogeneous and unstructured information from scientific, journalistic and social sources, applying text mining to analyze the documents and natural language processing to extract accurate results. The main contributions are that the system has been designed for any field, using different input datasets of documents, and with an automatic classification of categories for the detected keywords. In this research paper, results from the future of remote sensors are presented. Remote sensing services are providing new applications in observation and analysis of information remotely. This market is projected to witness a significant growth due to the increasing demand for services in commercial and defense industries. The system has obtained promising results, evaluated with two different methodologies, to help experts in the decision-making process and to discover new trends and opportunities.This research is partially supported by EIT Climate-KIC of the European Institute of Technology (project EIT Climate-KIC Accelerator-TC_3.1.5_190607_P066-1A) and InnoCENS from Erasmus + (573965-EPP-1-2016-1-SE-EPPKA2-CBHE-JP).Griol-Barres, I.; Milla, S.; Cebrián Ferriols, AJ.; Fan, H.; Millet Roig, J. (2020). Detecting Weak Signals of the Future: A System Implementation Based on Text Mining and Natural Language Processing. 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Detecting weak signals for long-term business opportunities using text mining of Web news. Expert Systems with Applications, 39(16), 12543-12550. doi:10.1016/j.eswa.2012.04.059Yoo, S., & Won, D. (2018). Simulation of Weak Signals of Nanotechnology Innovation in Complex System. Sustainability, 10(2), 486. doi:10.3390/su10020486Suh, J. (2018). Generating Future-Oriented Energy Policies and Technologies from the Multidisciplinary Group Discussions by Text-Mining-Based Identification of Topics and Experts. Sustainability, 10(10), 3709. doi:10.3390/su10103709Kwon, L.-N., Park, J.-H., Moon, Y.-H., Lee, B., Shin, Y., & Kim, Y.-K. (2018). Weak signal detecting of industry convergence using information of products and services of global listed companies - focusing on growth engine industry in South Korea -. Journal of Open Innovation: Technology, Market, and Complexity, 4(1). doi:10.1186/s40852-018-0083-6Ben-Porat, O., Hirsch, S., Kuchi, L., Elad, G., Reichart, R., & Tennenholtz, M. (2020). Predicting Strategic Behavior from Free Text. Journal of Artificial Intelligence Research, 68. doi:10.1613/jair.1.11849Fink, L., Yogev, N., & Even, A. (2017). Business intelligence and organizational learning: An empirical investigation of value creation processes. Information & Management, 54(1), 38-56. doi:10.1016/j.im.2016.03.009Ilmola, L., & Kuusi, O. (2006). Filters of weak signals hinder foresight: Monitoring weak signals efficiently in corporate decision-making. Futures, 38(8), 908-924. doi:10.1016/j.futures.2005.12.019Doulamis, N. D., Doulamis, A. D., Kokkinos, P., & Varvarigos, E. M. (2016). Event Detection in Twitter Microblogging. IEEE Transactions on Cybernetics, 46(12), 2810-2824. doi:10.1109/tcyb.2015.2489841Atefeh, F., & Khreich, W. (2013). A Survey of Techniques for Event Detection in Twitter. Computational Intelligence, 31(1), 132-164. doi:10.1111/coin.12017Mehmood, N. Q., Culmone, R., & Mostarda, L. (2017). Modeling temporal aspects of sensor data for MongoDB NoSQL database. Journal of Big Data, 4(1). doi:10.1186/s40537-017-0068-5Bjeladinovic, S. (2018). A fresh approach for hybrid SQL/NoSQL database design based on data structuredness. Enterprise Information Systems, 12(8-9), 1202-1220. doi:10.1080/17517575.2018.1446102Čerešňák, R., & Kvet, M. (2019). Comparison of query performance in relational a non-relation databases. Transportation Research Procedia, 40, 170-177. doi:10.1016/j.trpro.2019.07.027Yangui, R., Nabli, A., & Gargouri, F. (2016). Automatic Transformation of Data Warehouse Schema to NoSQL Data Base: Comparative Study. Procedia Computer Science, 96, 255-264. doi:10.1016/j.procs.2016.08.138Willett, P. (2006). The Porter stemming algorithm: then and now. Program, 40(3), 219-223. doi:10.1108/00330330610681295Griol-Barres, I., Milla, S., & Millet, J. (2019). Implementación de un sistema de detección de señales débiles de futuro mediante técnicas de minería de textos. 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Ecosystem Model Proposal in the Tourism Sector to Enhance Sustainable Competitiveness

[EN] Service companies in developed countries represent 70-80% of the Gross Domestic Product (GDP). In Spain, within the service sector, tourism is the main contributor and is growing annually. This is obviously an opportunity for the country due to its benefits and economic e¿ects but at the same time a well-structured, sustainable and competitive model for its continued development is needed in order to adopt best practices and reference innovative models from other sectors. A qualitative approach using Case Study, Grounded Theory and Delphi Method has been conducted to study the tourism sector in the city of Gandia, Valencia (Spain). Results show that a tourist destination with its different components and stakeholders involved in its value chain can be interpreted as an ecosystem and so reference ecosystem models could be adopted to boost the development of a region. Considering the results obtained, this study can contribute to the development of a tourist destination in a sustainable and innovative way.Morant-Martínez, O.; Santandreu Mascarell, C.; Canós-Darós, L.; Millet Roig, J. (2019). Ecosystem Model Proposal in the Tourism Sector to Enhance Sustainable Competitiveness. Sustainability. 11(23):1-15. https://doi.org/10.3390/su11236652S115112

Pulmonary Vein Activity Organization to Determine Atrial Fibrillation Recurrence: Preliminary Data from a Pilot Study

[EN] Ablation of pulmonary veins has emerged as a key procedure for normal rhythm restoration in atrial fibrillation patients. However, up to half of ablated Atrial fibrillation (AF) patients suffer recurrences during the first year. In this article, simultaneous intra-atrial recordings registered at pulmonary veins previous to the ablation procedure were analyzed. Spatial cross-correlation and transfer entropy were computed in order to estimate spatial organization. Results showed that, in patients with arrhythmia recurrence, pulmonary vein electrical activity was less correlated than in patients that maintained sinus rhythm. Moreover, correlation function between dipoles showed higher delays in patients with AF recurrence. Results with transfer entropy were consistent with spatial cross-correlation measurements. These results show that arrhythmia drivers located at the pulmonary veins are associated with a higher organization of the electrical activations after the ablation of these sites.This research was funded by Spanish Ministry of Research and Innovation : PID2019-109547RB-I00. This research was supported by the PID2019-109547RB-I00 National Research Program RETOS from the Spanish Ministry of Research and Innovation and partialy by GVA (PROMETEO/2018/078) & ISCIII (CB16/11/00486). Thanks to Michael Charles Willoughby for English language and scientific editing services.Cervigón, R.; Moreno, J.; Millet Roig, J.; Pérez-Villacastín, J.; Castells, F. (2020). Pulmonary Vein Activity Organization to Determine Atrial Fibrillation Recurrence: Preliminary Data from a Pilot Study. Mathematics. 8(10):1-13. https://doi.org/10.3390/math8101813S113810Andrade, J., Khairy, P., Dobrev, D., & Nattel, S. (2014). The Clinical Profile and Pathophysiology of Atrial Fibrillation. 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From 12 to 1 ECG lead: multiple cardiac condition detection mixing a hybrid machine learning approach with a one-vs-rest classification strategy

[EN] Objective: Detecting different cardiac diseases using a single or reduced number of leads is still challenging. This work aims to provide and validate an automated method able to classify ECG recordings. Performance using complete 12-lead systems, reduced lead sets, and single-lead ECGs is evaluated and compared. Approach: Seven different databases with 12-lead ECGs were provided during the PhysioNet/Computing in Cardiology Challenge 2021, where 88,253 annotated samples associated with none, one, or several cardiac conditions among 26 different classes were released for training, whereas 42,896 hidden samples were used for testing. After signal preprocessing, 81 features per ECG-lead were extracted, mainly based on heart rate variability, QRST patterns and spectral domain. Next, a One-vs-Rest classification approach made of independent binary classifiers for each cardiac condition was trained. This strategy allowed each ECG to be classified as belonging to none, one or several classes. For each class, a classification model among two binary Supervised Classifiers and one Hybrid Unsupervised-Supervised classification system was selected. Finally, we performed a 3-fold cross-validation to assess the system's performance. Main results: Our classifiers received scores of 0.39, 0.38, 0.39, 0.38, and 0.37 for the 12, 6, 4, 3 and 2-lead versions of the hidden test set with the Challenge evaluation metric (CM). Also, we obtained a mean G-score of 0.80, 0.78, 0.79, 0.79, 0.77 and 0.74 for the 12, 6, 4, 3, 2 and 1-lead subsets with the public training set during our 3-fold cross-validation. Significance: We proposed and tested a machine learning approach focused on flexibility for identifying multiple cardiac conditions using one or more ECG leads. Our minimal-lead approach may be beneficial for novel portable or wearable ECG devices used as screening tools, as it can also detect multiple and concurrent cardiac conditions.This work was supported by PID2019-109547RB-I00 (National Research Program, Ministerio de Ciencia e Innovación, Spanish Government) and CIBERCV CB16/11/00486 (Instituto de Salud Carlos III).Jiménez-Serrano, S.; Rodrigo, M.; Calvo Saiz, CJ.; Millet Roig, J.; Castells, F. (2022). From 12 to 1 ECG lead: multiple cardiac condition detection mixing a hybrid machine learning approach with a one-vs-rest classification strategy. Physiological Measurement. 43(6):1-17. https://doi.org/10.1088/1361-6579/ac72f511743

A new method for manufacturing dry electrodes on textiles. Validation for wearable ECG monitoring

[EN] This paper presents a new dry ECG electrode printed on a textile substrate. The proposed manufacturing process permits cost-effective mass production. The ECG dry electrode is obtained through screen printing a conductive silver ink coated with a biocompatible carbon layer. Three different designs combining two shapes (circular and square) and two sizes were developed. The resulting measured impedances are similar to those obtained via a conventional electrode. The prototypes were attached to a bracelet and used with a commercial electrocardiogram (ECG) device to register ECG signals. The dry electrodes were validated via ECG monitoring and compared with a conventional wet electrode. The clinical interest intervals reported similar results and the QRS morphology presented slight differences. Noise evaluation showed no notable differences for all the analyzed parameters.The work presented was funded by the Conselleria d'Economia Sostenible, Sectors Productius i Treball, through IVACE. HYBRID II Project, IMAMCI/2021/1. This work was also supported by PID2019-109547RB-I00 (National Research Program, Ministerio de Ciencia e Innovacion, Spanish Government) & CIBERCV CB16/11/00486 (Instituto de Salud Carlos III)Ferri, J.; Llinares Llopis, R.; Segarra, I.; Cebrián Ferriols, AJ.; Garcia-Breijo, E.; Millet Roig, J. (2022). A new method for manufacturing dry electrodes on textiles. Validation for wearable ECG monitoring. Electrochemistry Communications. 136:1-8. https://doi.org/10.1016/j.elecom.2022.1072441813

Multiple Cardiac Disease Detection from Minimal-Lead ECG Combining Feedforward Neural Networks with a One-vs-Rest Approach

[EN] Although standard 12-lead ECG is the primary technique in cardiac diagnostic, detecting different cardiac diseases using single or reduced number of leads is still challenging. The purpose of our team, itaca-UPV, is to provide a method able to classify ECG records using minimal lead information in the context of the 2021 PhysioNet/Computing in Cardiology Challenge, also using only a single-lead. We resampled and filtered the ECG signals, and extracted 109 features mostly based on Hearth Rhythm Variability (HRV). Then, we used selected features to train one feed-forward neural network (FFNN) with one hidden layer for each class using a One-vs-Rest approach, thus allowing each ECG to be classified as belonging to none or more than one class. Finally, we performed a 3-fold cross validation to assess the model performance. Our classifiers received scores of 0.34, 0.34, 0.27, 0.30, and 0.34 (ranked 26th, 21th, 29th, 25th, and 22th out of 39 teams) for the 12, 6, 4, 3 and 2-lead versions of the hidden test set with the Challenge evaluation metric. Our minimal-lead approach may be beneficial for novel portable or wearable ECG devices used as screening tools, as it can also detect multiple and concurrent cardiac conditions. Accuracy in detection can be improved adding more disease-specific features.Jiménez-Serrano, S.; Rodrigo Bort, M.; Calvo Saiz, CJ.; Castells, F.; Millet Roig, J. (2021). Multiple Cardiac Disease Detection from Minimal-Lead ECG Combining Feedforward Neural Networks with a One-vs-Rest Approach. 1-4. https://doi.org/10.22489/CinC.2021.1091

Combined estimation scheme for blind source separation with arbitrary source PDFs,”

An alternative closed-form estimator for blind source separation based on fourth-order statistics is presented. In contrast to other estimators, the new estimator works well when the source kurtosis sum is zero. Arbitrary source PDFs are successfully treated through a combined estimation scheme based on a heuristic decision rule for choosing between the new estimator and an existing estimator

Fetal electrocardiogram extraction using hybrid BSS technique: COMBI and MULTICOMBI algorithms

En este artículo se emplean dos  algoritmos  para obtener el ECG fetal a partir del ECG abdominal.  Los algoritmos son MULTICOMBI and COMBI, los cuales son una combinación de los algoritmos EFICA  y  WASOBI. Se  compara  el  desempeño  de  los  algoritmos  COMBI,  MULTICOMBI, WASOBI,  EFICA  y el tradicional algoritmo de JADE. Para comparar el desempeño de los algoritmos se usa una base de datos semi-sintética y dos bases de datos reales usando como parámetro la relación señal a error SER. Se encuentra que los algoritmos COMBI y MULTICOMBI muestran mejor desempeño que los algoritmos JADE, EFICA y WASOBI.In this paper, we use two algorithms for obtaining fetal ECG from abdominal ECG. The algorithms are MULTICOMBI and COMBI, which are a combination of EFICA and WASOBI algorithms. The performance of the algorithms COMBI, MULTICOMBI, WASOBI, EFICA and traditional JADE algorithm are compared. A semi synthetic database and two actual databases are used to compare the performance of algorithms using as parameter the signal to error ratio SER. It is found that the COMBI and MULTICOMBI algorithms show better performance than the JADE, EFICA and WASOBI algorithms

Classification model based on strain measurements to identify patients with arrhythmogenic cardiomyopathy with left ventricular involvement

[EN] Background and objective: A heterogenous expression characterizes arrhythmogenic cardiomyopathy (AC). The evaluation of regional wall movement included in the current Task Force Criteria is only qualitative and restricted to the right ventricle. However, a strain-based approach could precisely quantify myocardial deformation in both ventricles. We aim to define and modelize the strain behavior of the left ventricle in AC patients with left ventricular (LV) involvement by applying algorithms such as Principal Component Analysis (PCA), clustering and naive Bayes (NB) classifiers. Methods: Thirty-six AC patients with LV involvement and twenty-three non-affected family members (controls) were enrolled. Feature-tracking analysis was applied to cine cardiac magnetic resonance imaging to assess strain time series from a 3D approach, to which PCA was applied. A Two-Step clustering algorithm separated the patients' group into clusters according to their level of LV strain impairment. A statistical characterization between controls and the new AC subgroups was done. Finally, a NB classifier was built and new data from a small evolutive dataset was predicted. Results: 60% of AC-LV patients showed mildly affected strain and 40% severely affected strain. Both groups and controls exhibited statistically significant differences, especially when comparing controls and severely affected AC-LV patients. The classification accuracy of the strain NB classifier reached 82.76%. The model performance was as good as to classify the individuals with a 100% sensitivity and specificity for severely impaired strain patients, 85.7% and 81.1% for mildly impaired strain patients, and 69.9% and 91.4% for normal strain, respectively. Even when the severely affected LV-AC group was excluded, LV strain showed a good accuracy to differentiate patients and controls. The prediction of the evolutive dataset revealed a progressive alteration of strain in time. Conclusions: Our LV strain classification model may help to identify AC patients with LV involvement, at least in a setting of a high pretest probability, such as family screening.This work was supported by grants from the "Ministerio de Economia y Competitividad"[DPI2015-70821-R], "Instituto de Salud Carlos III " and FEDER "Union Europea, Una forma de hacer Europa"[PI14/01477, PI15/00748, PI18/01582, CIBERCV] and La Fe Biobank [PT17/0 015/0043].Vives-Gilabert, Y.; Zorio, E.; Sanz-Sánchez, J.; Calvillo-Batllés, P.; Millet Roig, J.; Castells, F. (2020). Classification model based on strain measurements to identify patients with arrhythmogenic cardiomyopathy with left ventricular involvement. Computer Methods and Programs in Biomedicine. 188:1-9. https://doi.org/10.1016/j.cmpb.2019.105296S19188Bielza, C., & Larrañaga, P. (2014). Discrete Bayesian Network Classifiers. ACM Computing Surveys, 47(1), 1-43. doi:10.1145/2576868Bourfiss, M., Vigneault, D. 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