Clasificación automática de registros ECG para la detección de Fibrilación Auricular y otros ritmos cardiacos

La importancia clínica de las arritmias cardiacas está aumentando, junto con su incidencia y prevalencia, principalmente asociadas con el envejecimiento de la población. Entre estas enfermedades destaca la Fibrilación Auricular (FA) ya que es el tipo de arritmia sostenida más común en adultos con una tendencia creciente más significativa, siendo en muchas ocasiones difícil de diagnosticar debido a un comportamiento paroxístico y/o la ausencia de síntomas en algunos casos. Por otro lado, hoy en día estamos en un escenario en el que los dispositivos portátiles o ¿wearables¿ están ganando gran interés como dispositivos de monitorización, tanto en investigación como en ámbitos clínicos. Sin embargo, los métodos automáticos para proporcionar un diagnóstico fiable de la FA utilizando las señales de electrocardiograma (ECG) proporcionadas por dispositivos portátiles son todavía un reto, especialmente si también se consideran otros ritmos normales o patológicos. El objetivo de este Trabajo Final de Máster es proporcionar diversos modelos de clasificación junto con su rendimiento para discriminar registros cortos de ECG de una única derivación entre cuatro grupos: ritmo normal (N), FA (A), otros ritmos (O) y ruidoso (~). Como base de datos para este estudio se utilizaron 8.528 registros de ECG de una única derivación con duraciones entre 9 y 60 segundos, proporcionados en el contexto de la competición 2017 PhysioNet/Computing in Cardiology Challenge. La estrategia propuesta en este trabajo se basa inicialmente en la extracción automática de características derivadas de la actividad ventricular de las señales ECG. Posteriormente se realizó una selección de características utilizando dos metodologías distintas: Backward Elimination y Forward Selection. Finalmente, las características seleccionadas se utilizaron para entrenar y evaluar mediante validación cruzada el rendimiento de diferentes modelos de clasificación, principalmente redes neuronales de tipo feedforward (FFNN), así como modelos Naïve Bayes y Support Vector Machine (SVM). A cada uno de estos modelos se le realizó un ajuste de parámetros de entrenamiento mediante grid-search durante la fase de validación. Los resultados mostraron que los modelos que presentaban mejor rendimiento de clasificación fueron las redes neuronales (F1=0.75), seguidas de cerca por los modelos SVM (F1=0.73), mientras que Naïve Bayes presentó el menor rendimiento (F1=0.67). Asimismo, también se demostró que la mayor dificultad de este problema se encuentra en la identificación de otros ritmos anómalos distintos a la fibrilación auricular, así como de los registros ruidosos. Dado que las señales utilizadas comparten muchas características con las adquiridas con dispositivos móviles, los modelos de clasificación resultantes podrían ser buenos candidatos para ser implementados en sistemas de gestión de pacientes con dispositivos wearables, ya que este enfoque tiene un bajo consumo computacional durante la clasificación.The clinical importance of cardiac arrhythmias is increasing, along with its incidence and prevalence, mainly associated with the aging of the population. Among these diseases Atrial Fibrillation (AF) stands out since it is the type of sustained arrhythmia most common in adults with a more significant growing tendency, being in many cases difficult to diagnose due to a paroxysmal behavior and/or the absence of symptoms in some patients. On the other hand, today we are in a scenario in which mobile devices or ¿wearables¿ are gaining great interest as monitoring devices, both in research and in clinical settings. However, automatic methods to provide a reliable diagnosis of AF using electrocardiogram signals (ECG) provided by mobile devices are still a challenge, especially if other normal or pathological rhythms are also considered. The main objective of this Final Master's Thesis is to provide different classification models together with their performance to discriminate short ECG single-lead records among four different groups: normal rhythm (N), FA (A), other rhythms (O) and noisy (~). As database for this study, 8,528 single-lead ECG records lasting among 9 and 60 seconds were used, provided in the context of the 2017 PhysioNet/Computing in Cardiology Challenge. The proposed strategy in this work is initially based on the automatic extraction of features mainly derived from the ventricular activity of the ECG signals. Next, a selection of characteristics was made using two different methodologies: Backward Elimination and Forward Selection. Finally, the selected features were used to train and evaluate through cross-validation the performance of different classification models, mainly feedforward neural networks (FFNN), as well as Naïve Bayes and Support Vector Machine (SVM) models. The training parameters for each of these models were tuned though a grid-search validation process. Results showed that the models with the best classification performance were the neural networks (F_1=0.75), followed closely by the SVM models (F_1=0.73), while Naïve Bayes presented the lowest performance (F_1=0.67). Likewise, it was also proved that the greatest difficulty of this problem lies on the identification of other anomalous rhythms other than atrial fibrillation, as well as in the noisy registers. Since the signals used share many characteristics with those acquired with mobile devices, the resulting classification models could be good candidates to be implemented in patient management systems with wearable devices, since this approach has a low computational consumption during classification.Jiménez Serrano, S. (2018). Clasificación automática de registros ECG para la detección de Fibrilación Auricular y otros ritmos cardiacos. http://hdl.handle.net/10251/111113TFG

Desarrollo de modelos predictivos y una aplicación móvil para la predicción de la depresión postparto

[EN] Postpartum depression is a disabling universal disease that affects many women from different countries during a key life stage for her and her newborn. The aim of this research involved the creation of classification models for postpartum depression based on clinical and patient questionnaires. We analyzed these data and applied a methodology of experimentation, validation and evaluation of different classification models. The classifiers with the best balance between sensitivity and specificity were integrated into a clinical decision support system for Android mobile platforms. It is intended, therefore, to put in clinicians and patients hands, a tool that aims to prevent the disease as well as to detect the risk population. That¿s why the final application comes in two versions, one for medical experts, and another one simpler for women who have just given birth.[ES] La depresión postparto es una enfermedad universal incapacitante que afecta a muchas mujeres de diferentes países durante una etapa vital clave para ella y su recién nacido. El objetivo del presente trabajo ha consistido en la creación de modelos de clasificación de la depresión postparto basados en datos clínicos y cuestionarios a pacientes. Se analizaron dichos datos y se aplicó una metodología de experimentación para el desarrollo, validación y evaluación de diferentes modelos de clasificación. Los clasificadores que presentaron un mejor balance entre sensibilidad y especificidad se integraron en un sistema de ayuda a la decisión clínica para plataformas móviles Android. Se ha pretendido pues, poner en manos tanto de personal clínico como de pacientes una herramienta que ayude en la prevención de la enfermedad y en la detección de población de riesgo. Es por eso que la aplicación final se presenta en dos versiones, una para expertos médicos, y otra más sencilla para las mujeres que acaban de dar a luz.Jiménez Serrano, S. (2013). Desarrollo de modelos predictivos y una aplicación móvil para la predicción de la depresión postparto. http://hdl.handle.net/10251/37483Archivo delegad

Desarrollo de nuevos marcadores y clasificadores de bajo coste computacional para identificar afecciones cardiacas en registros ECG

[ES] Las enfermedades cardiovasculares son una de las principales causas de mortalidad y morbilidad en el mundo. Entre las arritmias más comunes en adultos destaca la Fibrilación Auricular (FA), presentando una tendencia de crecimiento muy significativa, sobre todo en población de edad avanzada o con trastornos de obesidad. En el otro extremo, nos encontramos con la Miocardiopatía Arritmogénica (MCA), considerada una enfermedad rara con una prevalencia de 1:2000-5000 pero con gran afectación entre familiares directos, causante de muerte súbita cardiaca (MSC), y con un diagnóstico clínico complicado. Más allá de la FA o la MCA, existe una amplia variedad de patologías derivadas de una disfunción en la activación y conducción eléctrica del corazón. Para todas ellas, el electrocardiograma (ECG) continúa figurando como la primera y principal técnica de diagnóstico clínico, siendo una herramienta fundamental de cribado y detección de patologías relativamente económica y ampliamente accesible. Sin embargo, el diagnóstico preciso a partir de la interpretación del ECG requiere de médicos experimentados, siendo ésta una tarea que consume recursos, tiempo y que además está sujeta a la variabilidad entre observadores. Respecto a las afecciones cardiacas más comunes, conseguir un diagnóstico de forma automática que sea fiable, utilizando tanto 12 como un número reducido o único de derivaciones, sigue presentándose como un desafío. Este aspecto cobra especial relevancia con el uso cada vez más extendido de dispositivos portátiles o wearables, los cuales están ganando un gran interés para la detección temprana y preventiva de enfermedades cardiacas, registrando normalmente un número reducido de derivaciones ECG. Dicho uso masivo les confiere un gran potencial para facilitar el cribado y seguimiento de distintas afecciones en una amplia variedad de escenarios, a pesar de registrar señales de peor calidad en comparación con equipos certificados para uso clínico. El principal reto con estos dispositivos es encontrar un equilibrio adecuado entre la sensibilidad y la especificidad en la detección de ritmos cardiacos susceptibles de ser patológicos. En consecuencia, es indispensable diseñar e implementar algoritmos precisos adecuados para dispositivos móviles o portátiles capaces de detectar distintas afecciones cardiacas en registros de ECG. Respecto las afecciones cardiacas menos comunes como el caso de la MCA, es necesario incrementar la sensibilidad en la detección durante los cribados intra-familiares realizados tras una MSC. Para ello, sería posible explorar biomarcadores propios a esta enfermedad obtenidos mediante técnicas de procesado de señales ECG, además de modelos de clasificación que hagan uso de ellos, contribuyendo así a reducir el número de casos de muerte súbita. En base a lo descrito anteriormente, la presente tesis estudia las posibilidades de diagnóstico basadas en técnicas de aprendizaje y clasificación automática en dos escenarios principales. El primero aborda la detección de la FA, así como un amplio abanico de otras patologías cardiacas comunes, donde proponemos y validamos distintos modelos de clasificación de bajo consumo computacional. Todo esto, utilizando extensas bases de datos de acceso abierto, y haciendo énfasis en enfoques de derivación única, ya que son los más utilizados en dispositivos móviles e inteligentes. El segundo escenario se centra en la detección de MCA mediante las 12 derivaciones estándar del ECG, donde proponemos y validamos nuevos biomarcadores y modelos de clasificación que tratan de incrementar la sensibilidad de los cribados intra-familiares realizados tras una MSC. Para ello, utilizamos una base de datos específica de la Unidad de Cardiopatías Familiares del Hospital Universitario y Politécnico La Fe de València.[CA] Les malalties cardiovasculars són una de les principals causes de mortalitat i morbiditat en el món. Entre les arrítmies més comunes en adults destaca la Fibril·lació Auricular (FA), presentant una tendència de creixement molt significativa, sobretot en població d'edat avançada o amb trastorns d'obesitat. En l'altre extrem, ens trobem amb la Miocardiopatia Arritmogènica (MCA), considerada una malaltia rara amb una prevalença de 1:2000-5000 però amb gran afectació entre familiars directes, causant de mort sobtada cardíaca (MSC), i amb un diagnòstic clínic complicat. Més enllà de la FA o la MCA, existeix una àmplia varietat de patologies derivades d'una disfunció en l'activació i conducció elèctrica del cor. Per a totes elles, l'electrocardiograma (ECG) continua figurant com la primera i principal tècnica de diagnòstic clínic, sent una eina fonamental de cribratge i detecció de patologies relativament econòmica i àmpliament accessible. No obstant això, el diagnòstic precís a partir de la interpretació del ECG requereix de metges experimentats, sent aquesta una tasca que consumeix recursos, temps i que a més està subjecta a la variabilitat entre observadors. Respecte a les afeccions cardíaques més comunes, aconseguir un diagnòstic de manera automàtica que siga fiable, utilitzant tant 12 com un número reduït o únic de derivacions, continua presentant-se com un desafiament. Aquest aspecte cobra especial rellevància amb l'ús cada vegada més estés de dispositius portàtils o wearables, els quals estan guanyant un gran interés per a la detecció precoç i preventiva de malalties cardíaques, registrant normalment un nombre reduït de derivacions ECG. Aquest ús massiu els confereix un gran potencial per a facilitar el cribratge i seguiment de diferents afeccions en una àmplia varietat d'escenaris, malgrat registrar senyals de pitjor qualitat en comparació amb equips certificats per a ús clínic. El principal repte amb aquests dispositius és trobar un equilibri adequat entre la sensibilitat i l'especificitat en la detecció de ritmes cardíacs susceptibles de ser patològics. En conseqüència, és indispensable dissenyar i implementar algorismes precisos adequats per a dispositius mòbils o portàtils capaços de detectar diferents afeccions cardíaques en registres de ECG. Respecte les afeccions cardíaques menys comunes com el cas de la MCA, és necessari incrementar la sensibilitat en la detecció durant els cribratges intra-familiars realitzats després d'una MSC. Per a això, seria possible explorar biomarcadors propis a aquesta malaltia obtinguts mitjançant tècniques de processament de senyals ECG, a més de models de classificació que facen ús d'ells, contribuint així a reduir el nombre de casos de mort sobtada. Sobre la base del descrit anteriorment, la present tesi estudia les possibilitats de diagnòstic basades en tècniques d'aprenentatge i classificació automàtica en dos escenaris principals. El primer aborda la detecció de la FA, així com un ampli ventall d'altres patologies cardíaques comunes, on proposem i validem diferents models de classificació de baix consum computacional. Tot això, utilitzant extenses bases de dades d'accés obert, i fent èmfasi en enfocaments de derivació única, ja que són els més utilitzats en dispositius mòbils i intel·ligents. El segon escenari se centra en la detecció de MCA mitjançant les 12 derivacions estàndard de l'ECG, on proposem i validem nous biomarcadors i models de classificació que tracten d'incrementar la sensibilitat dels cribratges intra-familiars realitzats després d'una MSC. Per a això, utilitzem una base de dades específica de la Unitat de Cardiopaties Familiars de l'Hospital Universitari i Politècnic La Fe de València.[EN] Cardiovascular diseases are one of the leading causes of mortality and morbidity worldwide. Atrial Fibrillation (AF) stands out among adults' most common arrhythmias, presenting a very significant growth trend, especially in the elderly population or those with obesity disorders. At the other extreme, we find Arrhythmogenic Cardiomyopathy (ACM), a rare disease with a prevalence of 1:2000-5000 but great affectation among direct relatives, causing sudden cardiac death (SCD), and with a complicated clinical diagnosis. Beyond AF or ACM, there is a wide variety of pathologies derived from dysfunctions in the activation or electrical conduction of the heart. For all of them, the electrocardiogram (ECG) continues to appear as the first and foremost clinical diagnostic technique, being a fundamental tool for screening and detecting pathologies that is relatively cheap and widely accessible. However, accurate diagnosis based on ECG interpretation requires experienced physicians, as this task consumes resources, time and is subject to variability between observers. For the most common cardiac conditions, achieving a reliable diagnosis automatically, using either 12 or a smaller or single number of leads, remains a challenge. This aspect is especially relevant with the increasingly widespread use of portable or wearable devices, which are gaining significant interest for the early and preventive detection of heart disease, typically recording a reduced number of ECG leads. Such massive use gives them great potential to facilitate screening and monitoring different conditions in different scenarios, despite registering signals of lower quality compared to equipment certified for clinical use. The main challenge with these devices is finding the right balance between sensitivity and specificity in detecting pathologic heart rhythms. Consequently, designing and implementing accurate algorithms suitable for mobile or portable devices capable of detecting different cardiac conditions in ECG recordings is essential. Concerning less common cardiac conditions such as the case of ACM, it is necessary to increase the sensitivity in detection during intra-family screenings carried out after an SCD. Hence, it would be possible to explore specific biomarkers to this disease obtained through ECG signal processing techniques, as well as classification models that use them, thus contributing to reduce the number of cases of sudden death. Based on the previously described, this thesis studies the diagnostic possibilities based on machine learning and classification techniques in two main scenarios. The first deals with detecting AF and a wide range of other common cardiac pathologies, where we propose and validate different classification models with low computational consumption. All this, using extensive open access databases, and emphasizing single-lead approaches, since they are the most used in mobile and smart devices. The second scenario focuses on detecting ACM using the standard 12-lead ECG, where we propose and validate new biomarkers and classification models that try to increase the sensitivity of intra-family screenings carried out after an MSC. For this task, we used a specific database of the Familial Cardiopathies Unit of the Hospital Universitario y Politécnico La Fe de València.Jiménez Serrano, S. (2023). Desarrollo de nuevos marcadores y clasificadores de bajo coste computacional para identificar afecciones cardiacas en registros ECG [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19682

Atrial location optimization by electrical measures for Electrocardiographic Imaging

[EN] Background: The Electrocardiographic Imaging (ECGI) technique, used to non-invasively reconstruct the epicardial electrical activity, requires an accurate model of the atria and torso anatomy. Here we evaluate a new automatic methodology able to locate the atrial anatomy within the torso based on an intrinsic electrical parameter of the ECGI solution. Methods: In 28 realistic simulations of the atrial electrical activity, we randomly displaced the atrial anatomy for +/- 2.5 cm and +/- 30 degrees on each axis. An automatic optimization method based on the L-curve curvature was used to estimate the original position using exclusively non-invasive data. Results: The automatic optimization algorithm located the atrial anatomy with a deviation of 0.5 +/- 0.5 cm in position and 16.0 +/- 10.7 degrees in orientation. With these approximate locations, the obtained electrophysiological maps reduced the average error in atrial rate measures from 1.1 +/- 1.1 Hz to 0.5 +/- 1.0 Hz and in the phase singularity position from 7.2 +/- 4.0 cm to 1.6 +/- 1.7 cm (p < 0.01). Conclusions: This proposed automatic optimization may help to solve spatial inaccuracies provoked by cardiac motion or respiration, as well as to use ECGI on torso and atrial anatomies from different medical image systems.This work was supported in part by: Generalitat Valenciana Grants [APOSTD/2017] and projects [GVA/2018/103]; Nvidia Corporation with GPU QUADRO P6000 donation.Gisbert Soler, V.; Jiménez-Serrano, S.; Roses-Albert, E.; Rodrigo Bort, M. (2020). Atrial location optimization by electrical measures for Electrocardiographic Imaging. Computers in Biology and Medicine. 127:1-8. https://doi.org/10.1016/j.compbiomed.2020.104031S18127Cuculich, P. S., Zhang, J., Wang, Y., Desouza, K. A., Vijayakumar, R., Woodard, P. K., & Rudy, Y. 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Solving Inaccuracies in Anatomical Models for Electrocardiographic Inverse Problem Resolution by Maximizing Reconstruction Quality. IEEE Transactions on Medical Imaging, 37(3), 733-740. doi:10.1109/tmi.2017.2707413Dössel, O., Krueger, M. W., Weber, F. M., Wilhelms, M., & Seemann, G. (2012). Computational modeling of the human atrial anatomy and electrophysiology. Medical & Biological Engineering & Computing, 50(8), 773-799. doi:10.1007/s11517-012-0924-6Koivumäki, J. T., Seemann, G., Maleckar, M. M., & Tavi, P. (2014). In Silico Screening of the Key Cellular Remodeling Targets in Chronic Atrial Fibrillation. PLoS Computational Biology, 10(5), e1003620. doi:10.1371/journal.pcbi.1003620Garcia-Molla, V. M., Liberos, A., Vidal, A., Guillem, M. S., Millet, J., Gonzalez, A., … Climent, A. M. (2014). Adaptive step ODE algorithms for the 3D simulation of electric heart activity with graphics processing units. 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Reproducibility of heart and thoracic wall position in repeated deep inspiration breath holds for radiotherapy of left-sided breast cancer patients. Acta Oncologica, 57(10), 1318-1324. doi:10.1080/0284186x.2018.1490027Messinger-Rapport, B. J., & Rudy, Y. (1986). The Inverse Problem in Electrocardiography: A Model Study of the Effects of Geometry and Conductivity Parameters on the Reconstruction of Epicardial Potentials. IEEE Transactions on Biomedical Engineering, BME-33(7), 667-676. doi:10.1109/tbme.1986.325756Messinger-Rapport, B. J., & Rudy, Y. (1990). Noninvasive recovery of epicardial potentials in a realistic heart-torso geometry. Normal sinus rhythm. Circulation Research, 66(4), 1023-1039. doi:10.1161/01.res.66.4.1023Coll-Font, J., & Brooks, D. H. (2018). Tracking the Position of the Heart From Body Surface Potential Maps and Electrograms. Frontiers in Physiology, 9. doi:10.3389/fphys.2018.01727Van der Waal, J., Meijborg, V., Schuler, S., Coronel, R., & Oostendorp, T. (2020). In silico validation of electrocardiographic imaging to reconstruct the endocardial and epicardial repolarization pattern using the equivalent dipole layer source model. Medical & Biological Engineering & Computing, 58(8), 1739-1749. doi:10.1007/s11517-020-02203-yChamorro-Servent, J., Dubois, R., & Coudière, Y. (2019). Considering New Regularization Parameter-Choice Techniques for the Tikhonov Method to Improve the Accuracy of Electrocardiographic Imaging. Frontiers in Physiology, 10. doi:10.3389/fphys.2019.0027

Predicción de saliencia audiovisual en contenido 360º.

Como consecuencia de la disminución de los costes y de las mejoras tecnológicas, el campo de la Realidad Virtual (RV) está experimentando un crecimiento en los últimos años. Debido a dicho crecimiento, cada vez está apareciendo más contenido y aplicaciones desarrolladas específicamente para ser reproducidas o utilizadas en entornos de RV. Esto engloba desde contenido audiovisual en 360º, hasta aplicaciones que aprovechan este nuevo paradigma para ofrecer experiencias más inmersivas que las que se podían ofrecer con la anterior tecnología. Esto, a su vez, abre diversos campos de estudio centrados en tratar de comprender cómo se comportan los usuarios dentro de un entorno de RV, con el objetivo de ofrecer cada vez experiencias más satisfactorias y realistas. Uno de estos campos de estudio es el tratar de predecir en qué zonas de la escena 360º va a centrar el usuario su atención. Para lograr este objetivo, se han estudiado multitud de alternativas, las cuales incluyen desde diferentes maneras de representar internamente la escena, a utilizar diversos elementos de la misma. En este trabajo, se han analizado diferentes sistemas de predicción de la atención del usuario, los cuales se diferencian por usar tanto la información visual de la escena como la sonora espacial de la misma. El objetivo principal de este trabajo de fin de grado es el estudio de diferentes modificaciones que se les pueden aplicar a estos modelos, evaluando qué impacto tienen sobre los resultados finales. Para conseguir este objetivo, lo primero que se ha hecho ha sido estudiar el estado del arte de la materia, además de las herramientas que se van a utilizar a lo largo del trabajo. Después se han estudiado algunos trabajos que se han tomado como referencia, aplicándoles diferentes modificaciones. Por último, se han evaluado los diferentes resultados obtenidos y se han expuesto las conclusiones sacadas.<br /

From 12 to 1 ECG lead: multiple cardiac condition detection mixing a hybrid machine learning approach with a one-versus-rest classification strategy

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 thePhysioNet/Computing in Cardiology Challenge2021, 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-versus-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 meanG-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

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

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

Exploiting multiple ASR outputs for a spoken language understanding task

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-01931-4_19In this paper, we present an approach to Spoken Language Understanding, where the input to the semantic decoding process is a composition of multiple hypotheses provided by the Automatic Speech Recognition module. This way, the semantic constraints can be applied not only to a unique hypothesis, but also to other hypotheses that could represent a better recognition of the utterance. To do this, we have developed an algorithm to combine multiple sentences into a weighted graph of words, which is the input to the semantic decoding process. It has also been necessary to develop a specific algorithm to process these graphs of words according to the statistical models that represent the semantics of the task. This approach has been evaluated in a SLU task in Spanish. Results, considering different configurations of ASR outputs, show the better behavior of the system when a combination of hypotheses is considered.This work is partially supported by the Spanish MICINN under contract TIN2011-28169-C05-01, and under FPU Grant AP2010-4193Calvo Lance, M.; García Granada, F.; Hurtado Oliver, LF.; Jiménez Serrano, S.; Sanchís Arnal, E. (2013). Exploiting multiple ASR outputs for a spoken language understanding task. En Speech and Computer. Springer Verlag (Germany). 8113:138-145. https://doi.org/10.1007/978-3-319-01931-4_19S1381458113Tür, G., Mori, R.D.: Spoken Language Understanding: Systems for Extracting Semantic Information from Speech, 1st edn. Wiley (2011)Fiscus, J.G.: A post-processing system to yield reduced word error rates: Recognizer output voting error reduction (ROVER). In: Proceedings of the 1997 IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 347–354. IEEE (1997)Larkin, M.A., Blackshields, G., Brown, N.P., Chenna, R., McGettigan, P.A., McWilliam, H., Valentin, F., Wallace, I.M., Wilm, A., Lopez, R., Thompson, J.D., Gibson, T.J., Higgins, D.G.: ClustalW and ClustalX version 2.0. Bioinformatics 23, 2947–2948 (2007)Sim, K.C., Byrne, W.J., Gales, M.J.F., Sahbi, H., Woodland, P.C.: Consensus network decoding for statistical machine translation system combination. In: IEEE Int. Conference on Acoustics, Speech, and Signal Processing (2007)Bangalore, S., Bordel, G., Riccardi, G.: Computing Consensus Translation from Multiple Machine Translation Systems. In: Proceedings of IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2001), pp. 351–354 (2001)Calvo, M., Hurtado, L.-F., García, F., Sanchís, E.: A Multilingual SLU System Based on Semantic Decoding of Graphs of Words. In: Torre Toledano, D., Ortega Giménez, A., Teixeira, A., González Rodríguez, J., Hernández Gómez, L., San Segundo Hernández, R., Ramos Castro, D. (eds.) IberSPEECH 2012. CCIS, vol. 328, pp. 158–167. Springer, Heidelberg (2012)Hakkani-Tür, D., Béchet, F., Riccardi, G., Tür, G.: Beyond ASR 1-best: Using word confusion networks in spoken language understanding. Computer Speech & Language 20, 495–514 (2006)Benedí, J.M., Lleida, E., Varona, A., Castro, M.J., Galiano, I., Justo, R., López de Letona, I., Miguel, A.: Design and acquisition of a telephone spontaneous speech dialogue corpus in Spanish: DIHANA. In: Proceedings of LREC 2006, Genoa, Italy, pp. 1636–1639 (2006

An Ontology-Based Approach for Consolidating Patient Data Standardized With European Norm/International Organization for Standardization 13606 (EN/ISO 13606) Into Joint Observational Medical Outcomes Partnership (OMOP) Repositories: Description of a Methodology

Background: To discover new knowledge from data, they must be correct and in a consistent format. OntoCR, a clinical repository developed at Hospital Clínic de Barcelona, uses ontologies to represent clinical knowledge and map locally defined variables to health information standards and common data models. Objective: The aim of the study is to design and implement a scalable methodology based on the dual-model paradigm and the use of ontologies to consolidate clinical data from different organizations in a standardized repository for research purposes without loss of meaning. Methods: First, the relevant clinical variables are defined, and the corresponding European Norm/International Organization for Standardization (EN/ISO) 13606 archetypes are created. Data sources are then identified, and an extract, transform, and load process is carried out. Once the final data set is obtained, the data are transformed to create EN/ISO 13606-normalized electronic health record (EHR) extracts. Afterward, ontologies that represent archetyped concepts and map them to EN/ISO 13606 and Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) standards are created and uploaded to OntoCR. Data stored in the extracts are inserted into its corresponding place in the ontology, thus obtaining instantiated patient data in the ontology-based repository. Finally, data can be extracted via SPARQL queries as OMOP CDM-compliant tables. Results: Using this methodology, EN/ISO 13606-standardized archetypes that allow for the reuse of clinical information were created, and the knowledge representation of our clinical repository by modeling and mapping ontologies was extended. Furthermore, EN/ISO 13606-compliant EHR extracts of patients (6803), episodes (13,938), diagnosis (190,878), administered medication (222,225), cumulative drug dose (222,225), prescribed medication (351,247), movements between units (47,817), clinical observations (6,736,745), laboratory observations (3,392,873), limitation of life-sustaining treatment (1,298), and procedures (19,861) were created. Since the creation of the application that inserts data from extracts into the ontologies is not yet finished, the queries were tested and the methodology was validated by importing data from a random subset of patients into the ontologies using a locally developed Protégé plugin ("OntoLoad"). In total, 10 OMOP CDM-compliant tables ("Condition_occurrence," 864 records; "Death," 110; "Device_exposure," 56; "Drug_exposure," 5609; "Measurement," 2091; "Observation," 195; "Observation_period," 897; "Person," 922; "Visit_detail," 772; and "Visit_occurrence," 971) were successfully created and populated. Conclusions: This study proposes a methodology for standardizing clinical data, thus allowing its reuse without any changes in the meaning of the modeled concepts. Although this paper focuses on health research, our methodology suggests that the data be initially standardized per EN/ISO 13606 to obtain EHR extracts with a high level of granularity that can be used for any purpose. Ontologies constitute a valuable approach for knowledge representation and standardization of health information in a standard-agnostic manner. With the proposed methodology, institutions can go from local raw data to standardized, semantically interoperable EN/ISO 13606 and OMOP repositories.This work was supported by the ISCIII and cofunded by the European Union (grant PI18/00890, PI18/00981, and PI18CIII/00019).S