ECG Quality Assessment via Deep Learning and Data Augmentation

[EN] Quality assessment of ECG signals acquired with wearable devices is essential to avoid misdiagnosis of some cardiac disorders. For that purpose, novel deep learning algorithms have been recently proposed. However, training of these methods require large amount of data and public databases with annotated ECG samples are limited. Hence, the present work aims at validating the usefulness of a well-known data augmentation approach in this context of ECG quality assessment. Precisely, classification between high- and low-quality ECG excerpts achieved by a common convolutional neural network (CNN) trained on two databases has been compared. On the one hand, 2,000 5 second-length ECG excerpts were initially selected from a freely available database. Half of the segments were extracted from noisy ECG recordings and the other half from high-quality signals. On the other hand, using a data augmentation approach based on time-scale modification, noise addition, and pitch shifting of the original noisy ECG experts, 1,000 additional low-quality intervals were generated. These surrogate noisy signals and the original highquality ones formed the second dataset. The results for both cases were compared using a McNemar test and no statistically significant differences were noticed, thus suggesting that the synthesized noisy signals could be used for reliable training of CNN-based ECG quality indices.Huerta, Á.; Martínez-Rodrigo, A.; Rieta, JJ.; Alcaraz, R. (2021). ECG Quality Assessment via Deep Learning and Data Augmentation. 1-4. https://doi.org/10.22489/CinC.2021.2431

Multi-lag analysis of symbolic entropies on EEG recordings for distress recognition

Distress is a critical problem in developed societies given its long-term negative effects on physical and mental health. The interest in studying this emotion has notably increased during last years, being electroencephalography (EEG) signals preferred over other physiological variables in this research field. In addition, the non-stationary nature of brain dynamics has impulsed the use of non-linear metrics, such as symbolic entropies in brain signal analysis. Thus, the influence of time-lag on brain patterns assessment has not been tested. Hence, in the present study two permutation entropies denominated Delayed Permutation Entropy and Permutation Min-Entropy have been computed for the first time at different time-lags to discern between emotional states of calmness and distress from EEG signals. Moreover, a number of curve-related features were also calculated to assess brain dynamics across different temporal intervals. Complementary information among these variables was studied through sequential forward selection and 10-fold cross-validation approaches. According to the results obtained, the multi-lag entropy analysis has been able to reveal new significant insights so far undiscovered, thus notably improving the process of distress recognition from EEG recordings.Fil: Martínez Rodrigo, Arturo. Universidad de Castilla-La Mancha; EspañaFil: García Martínez, Beatriz. Universidad de Castilla-La Mancha; EspañaFil: Zunino, Luciano José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones Ópticas. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones Ópticas. Universidad Nacional de La Plata. Centro de Investigaciones Ópticas; Argentina. Universidad Nacional de La Plata. Facultad de Ingeniería; ArgentinaFil: Alcaraz, Raúl. Universidad de Castilla-La Mancha; EspañaFil: Fernández Caballero, Antonio. Biomedical Research Networking Centre in Mental Health; España. Universidad de Castilla-La Mancha; Españ

Evolution of the public problem of depopulation in Spain: longitudinal analysis of the media agenda

According to the sociology of public problems, the construction, visibility, and stabilization of a public problem require the mobilization of collectives of citizens interested in the issue, which act as an active entity in demanding actions and policies. One such issue is the depopulation of rural areas in Spain, which has shifted from a geographically localized problem to a matter of state policy. This article investigates the influence of framing in this process, analyzing a corpus of 5,980 headlines from newspapers in the Aragon, Castile-La Mancha, and Castile and Leon regions of Spain as well as from two national media outlets, corresponding to the period 2012–2021. Through the application of statistical analyses of lexical frequency in stages, the most significant terms in the evolution of the media’s coverage of the issue have been identified, which has made it possible to observe the appearance and displacement of concepts and their relationship to the most important milestones of social and political mobilization. In addition, its power to stir up sentiment in socio-political discourse has been explored. The consolidation of depopulation as a stable element in the Spanish media agenda, going beyond the regional sphere and having a presence dissociated from specific events through time, in contrast to what occurred some years ago, stands out. Finding a media-friendly frame -empty Spain [España vacía] and emptied Spain [España vaciada]- may have been a key element in this.

Inovação, design e sustentabilidade social: novas tendencias para o desenvolvimento local na contemporaneidade

This paper takes as its point of departure the “counter-currents” envisioned by Gui Bonsiepe in 1978 regarding the practice of design. The development of these ideas is reviewed based on the evolution of the concept of sustainability, as well as its social approach and relationship with innovation and design. In line with Bonsiepe’s ideas, the paper identifies changes in the role of design in order to meet new social needs; as examples, it presents interventions based on participatory design and social innovation, which generate alternatives for local development in the contemporary city. Due to new realities, the ability to construct intangible products is ever more important. The political action of design is more and more urgent; designers are called to play a leading role in the critical construction of new citizenship identities. A new field of action: social design.Toma-se como ponto de partida as “contracorrentes” que Gui Bonsiepe visionava em 1978 sobre a prática do design. Revisa-se o aperfeiçoamento dessas ideias a partir da evolução do conceito de sustentabilidade, seu enfoque social e sua relação com a inovação e o design. Em correspondência com as proposições de Bonsiepe, identificam-se mudanças no papel do design para atender a novas necessidades sociais; como exemplos, apresentam-se intervenções de design participativo e inovação social, que geram alternativas de desenvolvimento local na cidade contemporânea. Novas realidades tornam mais importantes as capacidades de construção de produtos intangíveis. A ação política do design é cada vez mais urgente; os designers são convocados a cumprir um papel protagonista na construção crítica da nova cidadania. Um novo campo de ação, o design social.Se toman como punto de partida las “contracorrientes” que visionaba Gui Bonsiepe en 1978, sobre la práctica del design. Se revisa el perfeccionamiento de esas ideas a partir de la evolución del concepto de sostenibilidad, su enfoque social y su relación con la innovación y el design. En correspondencia con los planteamientos de Bonsiepe, se identifican cambios en el papel del design para atender nuevas necesidades sociales; como ejemplos se presentan intervenciones de design participativo e innovación social, que generan alternativas de desarrollo local en la ciudad contemporánea. Nuevas realidades hacen más importantes las capacidades de construcción de productos intangibles. La acción política del design es cada vez más urgente; los designers están llamados a cumplir un papel protagónico en la construcción crítica de la nueva ciudadanía. Un nuevo campo de acción, el design socia

On the Generalization of Sleep Apnea Detection Methods Based on Heart Rate Variability and Machine Learning

[EN] Obstructive sleep apnea (OSA) is a respiratory disorder highly correlated with severe cardiovascular diseases that has unleashed the interest of hundreds of experts aiming to overcome the elevated requirements of polysomnography, the gold standard for its detection. In this regard, a variety of algorithms based on heart rate variability (HRV) features and machine learning (ML) classifiers have been recently proposed for epoch-wise OSA detection from the surface electrocardiogram signal. Many researchers have employed freely available databases to assess their methods in a reproducible way, but most were purely tested with cross-validation approaches and even some using solely a single database for training and testing procedures. Hence, although promising values of diagnostic accuracy have been reported by some of these methods, they are suspected to be overestimated and the present work aims to analyze the actual generalization ability of several epoch-wise OSA detectors obtained through a common ML pipeline and typical HRV features. Precisely, the performance of the generated OSA detectors has been compared on two validation approaches, i.e., the widely used epoch-wise, k-fold cross-validation and the highly recommended external validation, both considering different combinations of well-known public databases. Regardless of the used ML classifiers and the selected HRV-based features, the external validation results have been 20 to 40% lower than those obtained with cross-validation in terms of accuracy, sensitivity, and specificity. Consequently, these results suggest that ML-based OSA detectors trained with public databases are still not sufficiently general to be employed in clinical practice, as well as that larger, more representative public datasets and the use of external validation are mandatory to improve the generalization ability and to obtain reliable assessment of the true predictive power of these algorithms, respectively.This research has received financial support from public grants PID2021-00X128525-IV0 and PID2021-123804OB-I00 of the Spanish Government 10.13039/501100011033 jointly with the European Regional Development Fund, SBPLY/17/180501/000411 and SBPLY/21/180501/000186 from Junta de Comunidades de Castilla-La Mancha, and AICO/2021/286 from Generalitat Valenciana. Moreover, Daniele Padovano holds a predoctoral scholarship 2022-PRED-20642, which is cofinanced by the operating program of European Social Fund (ESF) 2014-2020 of Castilla-La Mancha.Padovano, D.; Martínez-Rodrigo, A.; Pastor, JM.; Rieta, JJ.; Alcaraz, R. (2022). On the Generalization of Sleep Apnea Detection Methods Based on Heart Rate Variability and Machine Learning. IEEE Access. 10:92710-92725. https://doi.org/10.1109/ACCESS.2022.320191192710927251

Improved Discrimination Between Healthy and Hypertensive Individuals Combining Photoplethysmography and Electrocardiography

[EN] Cardiovascular disease is one of the leading causes of death, with hypertension (HT) being its main risk factor. Its complications can be avoided with early treatment, but since these patients do not present any symptoms, HT is often detected at very advanced stages. This work presents a model for estimating blood pressure (BP) from electrocardiographic (ECG) and photoplethysmographic (PPG) signals, which can be easily obtained by means of wearable continuous monitoring devices. ECG, PPG and BP recordings from 86 patients were analyzed.A total of 34 standard and new features based on previous works were defined, such as pulse arrival times (PAT), and morphological characteristics of PPG signal. 37 classification models, ranging from Logistic Regression, Support Vector Machines (SVM), Nearest Neighbors, Naive Bayes or Coarse Trees were trained to compare discrimination results. The classifier that provided the highest performance when comparing normotensive patients with prehypertensive and hypertensive patients were Coarse Tree, providing an F1 score of 85.44% (Se of 86.27% and Sp of 77.14%). The use of PPG and ECG features has successfully discriminated between healthy and hypertensive individuals and, thus, could be used to detect HT by embedding these techniques in wearable devices.Research supported by grants DPI2017¿83952¿C3 from MINECO/AEI/FEDER UE, SBPLY/17/180501/000411 from JCCLM and AICO/2021/286 from GVA.Cano, J.; Hornero, F.; Quesada, A.; Martínez-Rodrigo, A.; Alcaraz, R.; Rieta, JJ. (2021). Improved Discrimination Between Healthy and Hypertensive Individuals Combining Photoplethysmography and Electrocardiography. 1-4. https://doi.org/10.22489/CinC.2021.0301

Recognition of Emotional States from EEG Signals with Nonlinear Regularity- and Predictability-Based Entropy Metrics

Recently, the recognition of emotions with electroencephalographic (EEG) signals has received increasing attention. Furthermore, the nonstationarity of brain has intensified the application of nonlinear methods. Nonetheless, metrics like quadratic sample entropy (QSE), amplitude-aware permutation entropy (AAPE) and permutation min-entropy (PME) have never been applied to discern between more than two emotions. Therefore, this study computes for the first time QSE, AAPE and PME for recognition of four groups of emotions. After preprocessing the EEG recordings, the three entropy metrics were computed. Then, a tenfold classification approach based on a sequential forward selection scheme and a support vector machine classifier was implemented. This procedure was applied in a multi-class scheme including the four groups of study simultaneously, and in a binary-class approach for discerning emotions two by two, regarding their levels of arousal and valence. For both schemes, QSE+AAPE and QSE+PME were combined. In both multi-class and binary-class schemes, the best results were obtained in frontal and parietal brain areas. Furthermore, in most of the cases channels from QSE and AAPE/PME were selected in the classification models, thus highlighting the complementarity between those different types of entropy indices and achieving global accuracy results higher than 90% in multi-class and binary-class schemes. The combination of regularity- and predictability-based entropy indices denoted a high degree of complementarity between those nonlinear methods. Finally, the relevance of frontal and parietal areas for recognition of emotions has revealed the essential role of those brain regions in emotional processes.Facultad de IngenieríaCentro de Investigaciones Óptica

A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices

[EN] Atrial fibrillation (AF) is the most common heart rhythm disturbance in clinical practice. It often starts with asymptomatic and very short episodes, which are extremely difficult to detect without long-term monitoring of the patient's electrocardiogram (ECG). Although recent portable and wearable devices may become very useful in this context, they often record ECG signals strongly corrupted with noise and artifacts. This impairs automatized ulterior analyses that could only be conducted reliably through a previous stage of automatic identification of high-quality ECG intervals. So far, a variety of techniques for ECG quality assessment have been proposed, but poor performances have been reported on recordings from patients with AF. This work introduces a novel deep learning-based algorithm to robustly identify high-quality ECG segments within the challenging environment of single-lead recordings alternating sinus rhythm, AF episodes and other rhythms. The method is based on the high learning capability of a convolutional neural network, which has been trained with 2-D images obtained when turning ECG signals into wavelet scalograms. For its validation, almost 100,000 ECG segments from three different databases have been analyzed during 500 learning-testing iterations, thus involving more than 320,000 ECGs analyzed in total. The obtained results have revealed a discriminant ability to detect high-quality and discard low-quality ECG excerpts of about 93%, only misclassifying around 5% of clean AF segments as noisy ones. In addition, the method has also been able to deal with raw ECG recordings, without requiring signal preprocessing or feature extraction as previous stages. Consequently, it is particularly suitable for portable and wearable devices embedding, facilitating early detection of AF as well as other automatized diagnostic facilities by reliably providing high-quality ECG excerpts to further processing stages.This research has been supported by grants DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha and AICO/2019/036 from Generalitat Valenciana.Huerta Herraiz, Á.; Martínez-Rodrigo, A.; Bertomeu-González, V.; Quesada, A.; Rieta, JJ.; Alcaraz, R. (2020). A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices. Entropy. 22(7):1-17. https://doi.org/10.3390/e22070733S117227Lippi, G., Sanchis-Gomar, F., & Cervellin, G. (2020). Global epidemiology of atrial fibrillation: An increasing epidemic and public health challenge. International Journal of Stroke, 16(2), 217-221. doi:10.1177/1747493019897870Krijthe, 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/eht280Colilla, 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.2013.05.063Khoo, C. W., Krishnamoorthy, S., Lim, H. S., & Lip, G. Y. H. (2012). Atrial fibrillation, arrhythmia burden and thrombogenesis. International Journal of Cardiology, 157(3), 318-323. doi:10.1016/j.ijcard.2011.06.088Warmus, P., Niedziela, N., Huć, M., Wierzbicki, K., & Adamczyk-Sowa, M. (2020). Assessment of the manifestations of atrial fibrillation in patients with acute cerebral stroke – a single-center study based on 998 patients. Neurological Research, 42(6), 471-476. doi:10.1080/01616412.2020.1746508Sposato, L. A., Cipriano, L. E., Saposnik, G., Vargas, E. R., Riccio, P. M., & Hachinski, V. (2015). Diagnosis of atrial fibrillation after stroke and transient ischaemic attack: a systematic review and meta-analysis. The Lancet Neurology, 14(4), 377-387. doi:10.1016/s1474-4422(15)70027-xSchotten, 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.12492Ferrari, R., Bertini, M., Blomstrom-Lundqvist, C., Dobrev, D., Kirchhof, P., Pappone, C., … Vicedomini, G. G. (2016). An update on atrial fibrillation in 2014: From pathophysiology to treatment. International Journal of Cardiology, 203, 22-29. doi:10.1016/j.ijcard.2015.10.089Meyre, P., Blum, S., Berger, S., Aeschbacher, S., Schoepfer, H., Briel, M., … Conen, D. (2019). Risk of Hospital Admissions in Patients With Atrial Fibrillation: A Systematic Review and Meta-analysis. Canadian Journal of Cardiology, 35(10), 1332-1343. doi:10.1016/j.cjca.2019.05.024Van 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.2014.11.011De Vos, C. B., Pisters, R., Nieuwlaat, R., Prins, M. H., Tieleman, R. G., Coelen, R.-J. S., … Crijns, H. J. G. M. (2010). Progression From Paroxysmal to Persistent Atrial Fibrillation. 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Accuracy and usability of single-lead ECG from smartphones - A clinical study. Indian Pacing and Electrophysiology Journal, 19(4), 145-149. doi:10.1016/j.ipej.2019.02.006Nagai, S., Anzai, D., & Wang, J. (2017). Motion artefact removals for wearable ECG using stationary wavelet transform. Healthcare Technology Letters, 4(4), 138-141. doi:10.1049/htl.2016.0100Satija, U., Ramkumar, B., & Manikandan, M. S. (2018). A Review of Signal Processing Techniques for Electrocardiogram Signal Quality Assessment. IEEE Reviews in Biomedical Engineering, 11, 36-52. doi:10.1109/rbme.2018.2810957Aboukhalil, A., Nielsen, L., Saeed, M., Mark, R. G., & Clifford, G. D. (2008). Reducing false alarm rates for critical arrhythmias using the arterial blood pressure waveform. Journal of Biomedical Informatics, 41(3), 442-451. doi:10.1016/j.jbi.2008.03.003Bashar, S. K., Ding, E., Walkey, A. J., McManus, D. D., & Chon, K. H. (2019). Noise Detection in Electrocardiogram Signals for Intensive Care Unit Patients. IEEE Access, 7, 88357-88368. doi:10.1109/access.2019.2926199Yoon, D., Lim, H. S., Jung, K., Kim, T. Y., & Lee, S. (2019). Deep Learning-Based Electrocardiogram Signal Noise Detection and Screening Model. Healthcare Informatics Research, 25(3), 201. doi:10.4258/hir.2019.25.3.201Oster, J., Behar, J., Sayadi, O., Nemati, S., Johnson, A. E. W., & Clifford, G. D. (2015). Semisupervised ECG Ventricular Beat Classification With Novelty Detection Based on Switching Kalman Filters. IEEE Transactions on Biomedical Engineering, 62(9), 2125-2134. doi:10.1109/tbme.2015.2402236Levkov, 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-4-50Luo, S., & Johnston, P. (2010). A review of electrocardiogram filtering. Journal of Electrocardiology, 43(6), 486-496. doi:10.1016/j.jelectrocard.2010.07.007Martínez, A., Alcaraz, R., & Rieta, J. J. (2010). Application of the phasor transform for automatic delineation of single-lead ECG fiducial points. Physiological Measurement, 31(11), 1467-1485. doi:10.1088/0967-3334/31/11/005Manikandan, M. S., & Ramkumar, B. (2014). Straightforward and robust QRS detection algorithm for wearable cardiac monitor. Healthcare Technology Letters, 1(1), 40-44. doi:10.1049/htl.2013.0019Satija, U., Ramkumar, B., & Manikandan, M. S. (2018). An automated ECG signal quality assessment method for unsupervised diagnostic systems. Biocybernetics and Biomedical Engineering, 38(1), 54-70. doi:10.1016/j.bbe.2017.10.002Satija, U., Ramkumar, B., & Manikandan, M. S. (2018). Automated ECG Noise Detection and Classification System for Unsupervised Healthcare Monitoring. IEEE Journal of Biomedical and Health Informatics, 22(3), 722-732. doi:10.1109/jbhi.2017.2686436Zhang, Q., Fu, L., & Gu, L. (2019). A Cascaded Convolutional Neural Network for Assessing Signal Quality of Dynamic ECG. Computational and Mathematical Methods in Medicine, 2019, 1-12. doi:10.1155/2019/7095137Xu, X., Wei, S., Ma, C., Luo, K., Zhang, L., & Liu, C. (2018). Atrial Fibrillation Beat Identification Using the Combination of Modified Frequency Slice Wavelet Transform and Convolutional Neural Networks. Journal of Healthcare Engineering, 2018, 1-8. doi:10.1155/2018/2102918Al Rahhal, M. M., Bazi, Y., Al Zuair, M., Othman, E., & BenJdira, B. (2018). Convolutional Neural Networks for Electrocardiogram Classification. Journal of Medical and Biological Engineering, 38(6), 1014-1025. doi:10.1007/s40846-018-0389-7He, R., Wang, K., Zhao, N., Liu, Y., Yuan, Y., Li, Q., & Zhang, H. (2018). Automatic Detection of Atrial Fibrillation Based on Continuous Wavelet Transform and 2D Convolutional Neural Networks. Frontiers in Physiology, 9. doi:10.3389/fphys.2018.01206Yildirim, O., Talo, M., Ay, B., Baloglu, U. B., Aydin, G., & Acharya, U. R. (2019). Automated detection of diabetic subject using pre-trained 2D-CNN models with frequency spectrum images extracted from heart rate signals. Computers in Biology and Medicine, 113, 103387. doi:10.1016/j.compbiomed.2019.103387SINGH, S. A., & MAJUMDER, S. (2019). A NOVEL APPROACH OSA DETECTION USING SINGLE-LEAD ECG SCALOGRAM BASED ON DEEP NEURAL NETWORK. Journal of Mechanics in Medicine and Biology, 19(04), 1950026. doi:10.1142/s021951941950026xByeon, Y.-H., Pan, S.-B., & Kwak, K.-C. (2019). Intelligent Deep Models Based on Scalograms of Electrocardiogram Signals for Biometrics. Sensors, 19(4), 935. doi:10.3390/s19040935Clifford, G., Liu, C., Moody, B., Lehman, L., Silva, I., Li, Q., … Mark, R. (2017). AF Classification from a Short Single Lead ECG Recording: the Physionet Computing in Cardiology Challenge 2017. 2017 Computing in Cardiology Conference (CinC). doi:10.22489/cinc.2017.065-469Redmond, S. J., Xie, Y., Chang, D., Basilakis, J., & Lovell, N. H. (2012). Electrocardiogram signal quality measures for unsupervised telehealth environments. Physiological Measurement, 33(9), 1517-1533. doi:10.1088/0967-3334/33/9/1517Li, T., & Zhou, M. (2016). ECG Classification Using Wavelet Packet Entropy and Random Forests. Entropy, 18(8), 285. doi:10.3390/e18080285Khorrami, H., & Moavenian, M. (2010). A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification. Expert Systems with Applications, 37(8), 5751-5757. doi:10.1016/j.eswa.2010.02.033Lyon, A., Mincholé, A., Martínez, J. P., Laguna, P., & Rodriguez, B. (2018). Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances. Journal of The Royal Society Interface, 15(138), 20170821. doi:10.1098/rsif.2017.0821Mincholé, A., & Rodriguez, B. (2019). Artificial intelligence for the electrocardiogram. Nature Medicine, 25(1), 22-23. doi:10.1038/s41591-018-0306-1Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187, 27-48. doi:10.1016/j.neucom.2015.09.116Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. doi:10.1145/3065386Li, Q., Rajagopalan, C., & Clifford, G. D. (2014). A machine learning approach to multi-level ECG signal quality classification. Computer Methods and Programs in Biomedicine, 117(3), 435-447. doi:10.1016/j.cmpb.2014.09.002Zhao, Z., & Zhang, Y. (2018). SQI Quality Evaluation Mechanism of Single-Lead ECG Signal Based on Simple Heuristic Fusion and Fuzzy Comprehensive Evaluation. Frontiers in Physiology, 9. doi:10.3389/fphys.2018.00727Moeyersons, J., Smets, E., Morales, J., Villa, A., De Raedt, W., Testelmans, D., … Varon, C. (2019). Artefact detection and quality assessment of ambulatory ECG signals. Computer Methods and Programs in Biomedicine, 182, 105050. doi:10.1016/j.cmpb.2019.105050Clifford, G. D., Behar, J., Li, Q., & Rezek, I. (2012). Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms. Physiological Measurement, 33(9), 1419-1433. doi:10.1088/0967-3334/33/9/1419Orphanidou, C., Bonnici, T., Charlton, P., Clifton, D., Vallance, D., & Tarassenko, L. (2014). Signal Quality Indices for the Electrocardiogram and Photoplethysmogram: Derivation and Applications to Wireless Monitoring. IEEE Journal of Biomedical and Health Informatics, 1-1. doi:10.1109/jbhi.2014.2338351Hayn, D., Jammerbund, B., & Schreier, G. (2012). QRS detection based ECG quality assessment. Physiological Measurement, 33(9), 1449-1461. doi:10.1088/0967-3334/33/9/1449Casey, S., Avalos, G., & Dowling, M. (2018). Critical care nurses’ knowledge of alarm fatigue and practices towards alarms: A multicentre study. Intensive and Critical Care Nursing, 48, 36-41. doi:10.1016/j.iccn.2018.05.004Nattel, S., Guasch, E., Savelieva, I., Cosio, F. G., Valverde, I., Halperin, J. L., … Camm, A. J. (2014). Early management of atrial fibrillation to prevent cardiovascular complications. European Heart Journal, 35(22), 1448-1456. doi:10.1093/eurheartj/ehu028Zhao, Z., Liu, C., Li, Y., Li, Y., Wang, J., Lin, B.-S., & Li, J. (2019). Noise Rejection for Wearable ECGs Using Modified Frequency Slice Wavelet Transform and Convolutional Neural Networks. IEEE Access, 7, 34060-34067. doi:10.1109/access.2019.2900719Petrėnas, A., Marozas, V., & Sörnmo, L. (2015). Low-complexity detection of atrial fibrillation in continuous long-term monitoring. Computers in Biology and Medicine, 65, 184-191. doi:10.1016/j.compbiomed.2015.01.01

Innovation, design, and social sustainability: New trends for local development in the contemporary world

ResumenSe toman como punto de partida las “contracorrientes” que visionaba Gui Bonsiepe en 1978, sobre la práctica del design. Se revisa el perfeccionamiento de esas ideas a partir de la evolución del concepto de sostenibilidad, su enfoque social y su relación con la innovación y el design. En correspondencia con los planteamientos de Bonsiepe, se identifican cambios en el papel del design para atender nuevas necesidades sociales; como ejemplos se presentan intervenciones de design participativo e innovación social, que generan alternativas de desarrollo local en la ciudad contemporánea. Nuevas realidades hacen más importantes las capacidades de construcción de productos intangibles. La acción política del design es cada vez más urgente; los designers están llamados a cumplir un papel protagónico en la construcción crítica de la nueva ciudadanía. Un nuevo campo de acción, el design social.Palabras clave: design, design participativo, innovación social, sostenibilidad.Innovation, design, and social sustainability: New trends for local development in the contemporary worldAbstractThis paper takes as its point of departure the “counter-currents” envisioned by Gui Bonsiepe in 1978 regarding the practice of design. The development of these ideas is reviewed based on the evolution of the concept of sustainability, as well as its social approach and relationship with innovation and design. In line with Bonsiepe’s ideas, the paper identifies changes in the role of design in order to meet new social needs; as examples, it presents interventions based on participatory design and social innovation, which generate alternatives for local development in the contemporary city. Due to new realities, the ability to construct intangible products is ever more important. The political action of design is more and more urgent; designers are called to play a leading role in the critical construction of new citizenship identities. A new field of action: social design.Keywords: Design, participatory design, social innovation, sustainability.Inovação, design e sustentabilidade social: novas tendências para o desenvolvimento local na contemporaneidadeResumoToma-se como ponto de partida as “contracorrentes” que Gui Bonsiepe visionava em 1978 sobre a prática do design. Revisa-se o aperfeiçoamento dessas ideias a partir da evolução do conceito de sustentabilidade, seu enfoque social e sua relação com a inovação e o design. Em correspondência com as proposições de Bonsiepe, identificam-se mudanças no papel do design para atender a novas necessidades sociais; como exemplos, apresentam-se intervenções de design participativo e inovação social, que geram alternativas de desenvolvimento local na cidade contemporânea. Novas realidades tornam mais importantes as capacidades de construção de produtos intangíveis. A ação política do design é cada vez mais urgente; os designers são convocados a cumprir um papel protagonista na construção crítica da nova cidadania. Um novo campo de ação, o design social.Palavras-chave: design, design participativo, inovação social, sustentabilidade. Recibido: mayo 15 / 2017   Evaluado: agosto 17 / 2017   Aceptado: septiembre 4 / 2017</p

Perspectivas sobre la continuidad, calidad de leche y entorno en unidades de producción de leche en el estado de Aguascalientes, México

The objective was to evaluate the productivity, the sale price of milk, the size and the perceptions of their owners about their environment, quality and permanence in milk production farms in the state of Aguascalientes. Forty milk production units, with similar conditions of age (30 years), zootechnical management, availability of inputs and customers, were evaluated. The productive characteristics of the farms in relation to the herd size factor were compared through a MANOVA. A structural model was formulated to evaluate the effect of environmental factors on milk quality and farmers’ intention to continue production units in the dairy activity. A positive influence was found on the productive scale of dairy farms, the obtaining of higher daily productivity per cow, better perception of quality and the sale price of milk. In the model, environmental factors were significantly associated with the assessment of milk quality by producers and their permanence in the dairy activity (14.2 and 22.7 %, respectively). This confirms that the perception of environmental factors could be considered as a crucial variable to increase milk quality, productivity and for the meeting between the interests of producers and the agribusiness, as well as to favor the performance and integration of the different links in the dairy production chain and boost the global competitiveness of the Mexican agri-food sector.El objetivo fue evaluar la productividad, el precio de venta de leche, el tamaño y las percepciones de sus propietarios sobre su entorno, calidad y permanencia en explotaciones de producción de leche del estado de Aguascalientes. Se evaluaron 40 unidades de producción de leche, con condiciones semejantes de antigüedad (30 años), manejo zootécnico, disponibilidad de insumos y clientes. A través de un MANOVA se compararon las características productivas de las explotaciones con relación al factor del tamaño del hato. Se formuló un modelo estructural para evaluar el efecto de los factores del entorno sobre la calidad de la leche y la intención de los granjeros en dar continuidad a las unidades de producción en la actividad lechera. Se encontró influencia positiva en la escala productiva de las explotaciones lecheras, la obtención de mayor productividad diaria por vaca, mejor percepción de calidad y el precio de venta de la leche. En el modelo, los factores del entorno se asociaron significativamente con la valoración de la calidad de la leche por los productores y su permanencia de la actividad lechera (14.2 y 22.7 %, respectivamente). Lo anterior confirma que la percepción de factores del entorno pudiera ser considerado como variable crucial para incrementar la calidad de la leche, la productividad y para el encuentro entre los intereses de los productores y de la agroindustria, así como para favorecer el desempeño e integración de los diferentes eslabones de la cadena productiva lechera e impulsar la competitividad global del sector agroalimentario mexicano