Machine learning and signal processing contributions to identify circulation states during out-of-hospital cardiac arrest

212 p. (eusk) 216 p. (eng.)Bat-bateko bihotz geldialdia (BBG) ustekabeko bihotz jardueraren etenaldi gisa definitzen da [9], non odol perfusioa ez baita iristenez burmuinera, ez beste ezinbesteko organoetara. BBGa ahalik eta azkarren tratatu behar da berpizte terapien bidez bat-bateko bihotz heriotza (BBH) ekiditeko [10, 11]. Ohikoena BBGa ospitalez kanpoko inguruneetan gertatzea da [12] eta kasu gehienetan ez da lekukorik egoten [13]. Horregatik, berpizte terapien aplikazio goiztiarra erronka mediku eta soziala da gaur egun

Noninvasive Monitoring of Manual Ventilation during Out-of- Hospital Cardiopulmonary Resuscitation

Cardiopulmonary resuscitation (CPR) consisting of chest compressions and assisted ventilation is crucial to treat out-of-hospital cardiac arrest (OHCA). It is well reported that quality of manual ventilations, in terms of rate and volume, is suboptimal, with a high incidence of hyperventilation, which is linked to poor outcomes. The lack of a noninvasive technology to monitor ventilations during out-of-hospital CPR precludes feedback on ventilations to the rescuer, and it handicaps the evaluation of the effect of ventilations on the outcome of the patient. This chapter addresses the possibilities and challenges of monitoring the quality of manual ventilations in current defibrillators. Methods are proposed to monitor ventilations based on the thoracic impedance and the capnogram. These methods can be integrated in defibrillators used in both basic and advanced life support. The algorithms are described, and the accuracy of the methods to monitor the ventilation rate and the quality metrics of the ventilations is reported using real OHCA episodes. The accuracy and limitations of the methods as well as the implications of integrating them in the treatment of patients in cardiac arrest are discussed

Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest

The automatic detection of pulse during out-of-hospital cardiac arrest (OHCA) is necessary for the early recognition of the arrest and the detection of return of spontaneous circulation (end of the arrest). The only signal available in every single defibrillator and valid for the detection of pulse is the electrocardiogram (ECG). In this study we propose two deep neural network (DNN) architectures to detect pulse using short ECG segments (5 s), i.e., to classify the rhythm into pulseless electrical activity (PEA) or pulse-generating rhythm (PR). A total of 3914 5-s ECG segments, 2372 PR and 1542 PEA, were extracted from 279 OHCA episodes. Data were partitioned patient-wise into training (80%) and test (20%) sets. The first DNN architecture was a fully convolutional neural network, and the second architecture added a recurrent layer to learn temporal dependencies. Both DNN architectures were tuned using Bayesian optimization, and the results for the test set were compared to state-of-the art PR/PEA discrimination algorithms based on machine learning and hand crafted features. The PR/PEA classifiers were evaluated in terms of sensitivity (Se) for PR, specificity (Sp) for PEA, and the balanced accuracy (BAC), the average of Se and Sp. The Se/Sp/BAC of the DNN architectures were 94.1%/92.9%/93.5% for the first one, and 95.5%/91.6%/93.5% for the second one. Both architectures improved the performance of state of the art methods by more than 1.5 points in BAC.This work was supported by: The Spanish Ministerio de Economía y Competitividad, TEC2015-64678-R, jointly with the Fondo Europeo de Desarrollo Regional (FEDER), UPV/EHU via GIU17/031 and the Basque Government through the grant PRE_2018_2_0260

Development of AI-Based Tools for Power Generation Prediction

This study presents a model for predicting photovoltaic power generation based on meteorological, temporal and geographical variables, without using irradiance values, which have traditionally posed challenges and difficulties for accurate predictions. Validation methods and evaluation metrics are used to analyse four different approaches that vary in the distribution of the training and test database, and whether or not location-independent modelling is performed. The coefficient of determination,R2, is used to measure the proportion of variation in photovoltaic power generation that can be explained by the model’s variables, while gCO2eq represents the amount of CO2 emissions equivalent to each unit of power generation. Both are used to compare model performance and environmental impact. The results show significant differences between the locations, with substantial improvements in some cases, while in others improvements are limited. The importance of customising the predictive model for each specific location is emphasised. Furthermore, it is concluded that environmental impact studies in model production are an additional step towards the creation of more sustainable and efficient models. Likewise, this research considers both the accuracy of solar energy predictions and the environmental impact of the computational resources used in the process, thereby promoting the responsible and sustainable progress of data science.This research is supported by the Bulgarian National Science Fund in the scope of the project ”Exploration the application of statistics and machine learning in electronics” under contract number κπ-06-H42/1

A Machine Learning Model for the Prognosis of Pulseless Electrical Activity during Out-of-Hospital Cardiac Arrest

Pulseless electrical activity (PEA) is characterized by the disassociation of the mechanical and electrical activity of the heart and appears as the initial rhythm in 20–30% of out-of-hospital cardiac arrest (OHCA) cases. Predicting whether a patient in PEA will convert to return of spontaneous circulation (ROSC) is important because different therapeutic strategies are needed depending on the type of PEA. The aim of this study was to develop a machine learning model to differentiate PEA with unfavorable (unPEA) and favorable (faPEA) evolution to ROSC. An OHCA dataset of 1921 5s PEA signal segments from defibrillator files was used, 703 faPEA segments from 107 patients with ROSC and 1218 unPEA segments from 153 patients with no ROSC. The solution consisted of a signal-processing stage of the ECG and the thoracic impedance (TI) and the extraction of the TI circulation component (ICC), which is associated with ventricular wall movement. Then, a set of 17 features was obtained from the ECG and ICC signals, and a random forest classifier was used to differentiate faPEA from unPEA. All models were trained and tested using patientwise and stratified 10-fold cross-validation partitions. The best model showed a median (interquartile range) area under the curve (AUC) of 85.7(9.8)% and a balance accuracy of 78.8(9.8)% , improving the previously available solutions at more than four points in the AUC and three points in balanced accuracy. It was demonstrated that the evolution of PEA can be predicted using the ECG and TI signals, opening the possibility of targeted PEA treatment in OHCA.This work was supported by the Spanish Ministerio de Ciencia, Innovacion y Universidades through Grant RTI2018-101475-BI00, jointly with the Fondo Europeo de Desarrollo Regional (FEDER), by the Basque Government through Grant IT1229-19 and Grant PRE2020_1_0177, and by the university of the Basque Country (UPV/EHU) under Grant COLAB20/01

Arquitecturas de aprendizaje profundo para la detección de pulso en la parada cardiaca extrahospitalaria utilizando el ECG

La detección de la presencia de pulso durante la parada cardiorrespiratoria extrahospitalaria es crucial para la supervivencia del paciente. Se ha demostrado que la toma manual del pulso no es muy fiable y que consume demasiado tiempo, por lo que es necesario desarrollar métodos automáticos que ayuden en la identificación del retorno de la circulación espontánea del paciente en parada. En este trabajo se propone utilizar técnicas de aprendizaje profundo para la discriminación automática de ritmos con pulso (PR) y sin pulso (PEA) utilizando solamente información proveniente del ECG. Se ha utilizado una base de datos que contiene 3914 segmentos de 5 segundos (3372 PR y 1542 PEA), que se dividieron en dos bases de datos con pacientes disjuntos para la optimización y evaluación de los métodos. Los mejores resultados se han obtenido utilizando una red neuronal profunda que contiene dos etapas de convolución y una etapa recurrente para la extracción de características y a continuación un clasificador. El modelo se evalúa en términos de sensibilidad (SE, porcentaje de PRs correctamente detectados) y especificidad (SP, proporción de PEAs correctamente detectados). Sobre la base de evaluación se obtuvieron una SE/SP de 94.2%/91.0%, por lo que puede concluirse que la detección automática del pulso utilizando sólo el ECG es viable mediante técnicas de aprendizaje profundo.Este trabajo ha recibido apoyo económico conjunto del Ministerio de Economía y Competitividad Español y del Fondo Europeo de Desarrollo Regional (FEDER) a través del proyecto (TEC2015-64678-R), de la Universidad del País Vasco/Euskal Herriko Unibertsitatea mediante la ayuda a grupos de investigación GIU17/031, y del Gobierno Vasco a través de la beca PRE_2017_1_0112

Towards the Prediction of Rearrest during Out-of-Hospital Cardiac Arrest

A secondary arrest is frequent in patients that recover spontaneous circulation after an out-of-hospital cardiac arrest (OHCA). Rearrest events are associated to worse patient outcomes, but little is known on the heart dynamics that lead to rearrest. The prediction of rearrest could help improve OHCA patient outcomes. The aim of this study was to develop a machine learning model to predict rearrest. A random forest classifier based on 21 heart rate variability (HRV) and electrocardiogram (ECG) features was designed. An analysis interval of 2 min after recovery of spontaneous circulation was used to compute the features. The model was trained and tested using a repeated cross-validation procedure, on a cohort of 162 OHCA patients (55 with rearrest). The median (interquartile range) sensitivity (rearrest) and specificity (no-rearrest) of the model were 67.3% (9.1%) and 67.3% (10.3%), respectively, with median areas under the receiver operating characteristics and the precision–recall curves of 0.69 and 0.53, respectively. This is the first machine learning model to predict rearrest, and would provide clinically valuable information to the clinician in an automated way.This work was supported by the Spanish Ministerio de Ciencia, Innovación y Universidades through grant RTI2018-101475-BI00, jointly with the Fondo Europeo de Desarrollo Regional (FEDER), and by the Basque Government through grants IT1229-19, PRE_2019_2_0100 and PRE_2019_1_0262. A.I. receives research grants from the US National Institutes of Health (NIH)

Autofluorescence image reconstruction and virtual staining for in-vivo optical biopsying

Modern photonic technologies are emerging, allowing the acquisition of in-vivo endoscopic tissue imaging at a microscopic scale, with characteristics comparable to traditional histological slides, and with a label-free modality. This raises the possibility of an ‘optical biopsy’ to aid clinical decision making. This approach faces barriers for being incorporated into clinical practice, including the lack of existing images for training, unfamiliarity of clinicians with the novel image domains and the uncertainty of trusting ‘black-box’ machine learned image analysis, where the decision making remains inscrutable. In this paper, we propose a new method to transform images from novel photonics techniques (e.g. autofluorescence microscopy) into already established domains such as Hematoxilyn-Eosin (H-E) microscopy through virtual reconstruction and staining. We introduce three main innovations: 1) we propose a transformation method based on a Siamese structure that simultaneously learns the direct and inverse transformation ensuring domain back-transformation quality of the transformed data. 2) We also introduced an embedding loss term that ensures similarity not only at pixel level, but also at the image embedding description level. This drastically reduces the perception distortion trade-off problem existing in common domain transfer based on generative adversarial networks. These virtually stained images can serve as reference standard images for comparison with the already known H-E images. 3) We also incorporate an uncertainty margin concept that allows the network to measure its own confidence, and demonstrate that these reconstructed and virtually stained images can be used on previously-studied classification models of H-E images that have been computationally degraded and de-stained. The three proposed methods can be seamlessly incorporated on any existing architectures. We obtained balanced accuracies of 0.95 and negative predictive values of 1.00 over the reconstructed and virtually stained image-set on the detection of color-rectal tumoral tissue. This is of great importance as we reduce the need for extensive labeled datasets for training, which are normally not available on the early studies of a new imaging technology.The authors would like to thank all pathologists that generated the BIOPOOL dataset (FP7-ICT-296162) that has been used for this work and specially to M. Saiz, A. Gaafar, S. Fernandez, A. Saiz, E. de Miguel, B. Catón, J. J. Aguirre, R. Ruiz, Ma A. Viguri, and R. Rezola

Modelo predictivo del retorno de circulación espontánea en la parada cardiorrespiratoria utilizando el ECG y la impedancia torácica

El análisis de los diferentes tipos de ritmo cardíaco durante la parada cardiorrespiratoria y la predicción de su evolución permitiría ajustar la terapia de resucitación a cada paciente. El ritmo con actividad eléctrica sin pulso (AESP) es el ritmo inicial predominante durante la parada cardiorrespiratoria extrahospitalaria, y es de gran interés disponer de modelos que predigan el retorno espontáneo de circulación (RCE). En este trabajo se propone un método automático que discrimina los casos de AESP que evolucionan a RCE de los que no recuperan el pulso. El modelo combina parámetros de las señales de electrocardiograma (ECG) e impedancia torácica (IT) adquiridas con los parches del desfibrilador. La base de datos consiste en 185 pacientes (73 con RCE) de los que se extrajeron 1600 segmentos (432 con RCE). Aplicando una validación cruzada de 10 particiones y un clasificador de máquinas de vectores de soporte (SVM), se demuestra que la IT añade valor discriminativo al modelo basado en ECG. Para un clasificador SVM con un núcleo polinómico de orden 2 se obtuvo una sensibilidad del 79.8%, una especificidad del 85.5% y un área bajo la curva ROC de 0.91.Este trabajo ha sido parcialmente financiado por el Ministerio de Ciencia, Innovación y Universidades a través del proyecto RTI2018-101475-BI00, en conjunto con el Fondo Europeo de Desarrollo Regional (FEDER), y en parte por el Gobierno Vasco por medio del proyecto IT- 1229-19

Arquitecturas de aprendizaje profundo para la detección de pulso en la parada cardiaca extrahospitalaria utilizando el ECG

La detección de la presencia de pulso durante la parada cardiorrespiratoria extrahospitalaria es crucial para la supervivencia del paciente. Se ha demostrado que la toma manual del pulso no es muy fiable y que consume demasiado tiempo, por lo que es necesario desarrollar métodos automáticos que ayuden en la identificación del retorno de la circulación espontánea del paciente en parada. En este trabajo se propone utilizar técnicas de aprendizaje profundo para la discriminación automática de ritmos con pulso (PR) y sin pulso (PEA) utilizando solamente información proveniente del ECG. Se ha utilizado una base de datos que contiene 3914 segmentos de 5 segundos (3372 PR y 1542 PEA), que se dividieron en dos bases de datos con pacientes disjuntos para la optimización y evaluación de los métodos. Los mejores resultados se han obtenido utilizando una red neuronal profunda que contiene dos etapas de convolución y una etapa recurrente para la extracción de características y a continuación un clasificador. El modelo se evalúa en términos de sensibilidad (SE, porcentaje de PRs correctamente detectados) y especificidad (SP, proporción de PEAs correctamente detectados). Sobre la base de evaluación se obtuvieron una SE/SP de 94.2%/91.0%, por lo que puede concluirse que la detección automática del pulso utilizando sólo el ECG es viable mediante técnicas de aprendizaje profundo.Este trabajo ha recibido apoyo económico conjunto del Ministerio de Economía y Competitividad Español y del Fondo Europeo de Desarrollo Regional (FEDER) a través del proyecto (TEC2015-64678-R), de la Universidad del País Vasco/Euskal Herriko Unibertsitatea mediante la ayuda a grupos de investigación GIU17/031, y del Gobierno Vasco a través de la beca PRE_2017_1_0112