Seinale prozesaketan eta ikasketa automatikoan oinarritutako ekarpenak bihotz-erritmoen analisirako bihotz-biriketako berpiztean

Tesis inglés 218 p. -- Tesis euskera 220 p.Out-of-hospital cardiac arrest (OHCA ) is characterized by the sudden loss of the cardiac function, andcauses around 10% of the total mortality in developed countries. Survival from OHCA depends largelyon two factors: early defibrillation and early cardiopulmonary resuscitation (CPR). The electrical shock isdelivered using a shock advice algorithm (SAA) implemented in defibrillators. Unfortunately, CPR mustbe stopped for a reliable SAA analysis because chest compressions introduce artefacts in the ECG. Theseinterruptions in CPR have an adverse effect on OHCA survival. Since the early 1990s, many efforts havebeen made to reliably analyze the rhythm during CPR. Strategies have mainly focused on adaptive filtersto suppress the CPR artefact followed by SAAs of commercial defibrillators. However, these solutionsdid not meet the American Heart Association¿s (AHA) accuracy requirements for shock/no-shockdecisions. A recent approach, which replaces the commercial SAA by machine learning classifiers, hasdemonstrated that a reliable rhythm analysis during CPR is possible. However, defibrillation is not theonly treatment needed during OHCA, and depending on the clinical context a finer rhythm classificationis needed. Indeed, an optimal OHCA scenario would allow the classification of the five cardiac arrestrhythm types that may be present during resuscitation. Unfortunately, multiclass classifiers that allow areliable rhythm analysis during CPR have not yet been demonstrated. On all of these studies artefactsoriginate from manual compressions delivered by rescuers. Mechanical compression devices, such as theLUCAS or the AutoPulse, are increasingly used in resuscitation. Thus, a reliable rhythm analysis duringmechanical CPR is becoming critical. Unfortunately, no AHA compliant algorithms have yet beendemonstrated during mechanical CPR. The focus of this thesis work is to provide new or improvedsolutions for rhythm analysis during CPR, including shock/no-shock decision during manual andmechanical CPR and multiclass classification during manual CPR

Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia

Early defibrillation by an automated external defibrillator (AED) is key for the survival of out-of-hospital cardiac arrest (OHCA) patients. ECG feature extraction and machine learning have been successfully used to detect ventricular fibrillation (VF) in AED shock decision algorithms. Recently, deep learning architectures based on 1D Convolutional Neural Networks (CNN) have been proposed for this task. This study introduces a deep learning architecture based on 1D-CNN layers and a Long Short-Term Memory (LSTM) network for the detection of VF. Two datasets were used, one from public repositories of Holter recordings captured at the onset of the arrhythmia, and a second from OHCA patients obtained minutes after the onset of the arrest. Data was partitioned patient-wise into training (80%) to design the classifiers, and test (20%) to report the results. The proposed architecture was compared to 1D-CNN only deep learners, and to a classical approach based on VF-detection features and a support vector machine (SVM) classifier. The algorithms were evaluated in terms of balanced accuracy (BAC), the unweighted mean of the sensitivity (Se) and specificity (Sp). The BAC, Se, and Sp of the architecture for 4-s ECG segments was 99.3%, 99.7%, and 98.9% for the public data, and 98.0%, 99.2%, and 96.7% for OHCA data. The proposed architecture outperformed all other classifiers by at least 0.3-points in BAC in the public data, and by 2.2-points in the OHCA data. The architecture met the 95% Sp and 90% Se requirements of the American Heart Association in both datasets for segment lengths as short as 3-s. This is, to the best of our knowledge, the most accurate VF detection algorithm to date, especially on OHCA data, and it would enable an accurate shock no shock diagnosis in a very short time.This study was supported by the Ministerio de Economía, Industria y Competitividad, Gobierno de España (ES) (TEC-2015-64678-R) to UI and EA and by Euskal Herriko Unibertsitatea (ES) (GIU17/031) to UI and EA. The funders, Tecnalia Research and Innovation and Banco Bilbao Vizcaya Argentaria (BBVA), provided support in the form of salaries for authors AP, AA, FAA, CF, EG, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the author contributions section

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

Reporting and improving quality of cardiopulmonary resuscitation (CPR) during out of hospital cardiac arrest.

Cand.med Jo Kramer-Johansen (f.1969) has studied how quality of CPR can be measured and modified by automated feedback during out of hospital cardiac arrest. The results from 284 episodes of cardiac arrest treated in the ambulance services of Akershus, London, and Stockholm show variable and poor quality of CPR characterized by too shallow chest compressions and too many and too long pauses. In the thesis he discusses and recommends standards for measuring and reporting CPR quality for the purposes of avoiding confounded clinical trials and for quality assurance and improvement in all professional services. The Norwegian Air Ambulance Foundation supported this work with a full time scholarship. Supervisors have been Professor Petter Andreas Steen and Lars Wik (NAKOS)

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

Improving Outcome After Cardiac Arrest: New Pharmacological And Electrophysiological Approaches During Cardiopulmonary Resuscitation

Cardiac arrest (CA) represents a leading cause of death in the western world. CA is a dramatic clinical event that can occur suddenly and often without premonitory signs. This condition is characterized by sudden loss of consciousness caused by the lack of cerebral blood flow, which occurs when the heart ceases to pump. Chest compressions (CCs) and early defibrillation (DF) are the cornerstones of cardiopulmonary resuscitation (CPR) in CA, while the only definitive treatment for ventricular fibrillation (VF) remains prompt DF. The present thesis includes both experimental and clinical studies directed to evaluate new pharmacological and electrophysiological approaches that have a potential benefit in the outcome of patients affected from CA. In order to achieve such aims, we performed two separate studies concurrently. The first study was directed to investigate experimentally the role of β1-blockade during CPR, while the second series of study evaluated prospectively the feasibility of a real time VF waveform analysis, in particular Amplitude Spectrum Area (AMSA), to guide interventions during resuscitation and to potentially diagnose underlying cardiac ischemia. Both the studies therefore concurred to the same goal of identify new tools to improve the outcome of CA and are presented together in this thesis as a single study with an introduction, methods, results, and discussion section. Nevertheless, in order to help the readers going throughout the work, each section is divided into sub-sections presenting separately the studies

Sensors for Vital Signs Monitoring

Sensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems, as well as traditional medical purposes, such as disease indication judgments and predictions. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies include contact sensors such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiography (BCG), and invasive/non-invasive sensors for diagnoses of variations in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, the design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, machine-learning-based diagnostic technology can be used for extracting meaningful information from continuous monitoring data

Out-of-hospital cardiac arrest (OHCA) in Queensland, Australia: epidemiology and predictors of outcome

Katherine Pemberton studied adults who suffered out-of-hospital cardiac arrest over a 13-year period in Queensland and investigated survival. She identified some important population subsets at risk of reduced survival and identified factors to predict increased survival. Her work will be used by Ambulance Services to inform ways to improve resuscitation practices and policy