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

Diagnóstico del Ritmo Cardíaco durante la Resucitación Cardiopulmonar Administrada mediante una Banda de Distribución de Carga

Las compresiones torácicas (CTs) administradas durante la resucitación cardiopulmonar (RCP) mediante una banda de distribución de carga (LDB en inglés) inducen artefactos en el ECG que interfieren en el diagnóstico de los algoritmos de decisión shock/no-shock implementados en los desfibriladores. Esto obliga al rescatador a detener la terapia RCP reduciendo la probabilidad de supervivencia del paciente. El objetivo de este trabajo es diseñar un método que diagnostique con precisión el ritmo durante el uso de una LDB evitando así tener que interrumpir la terapia. El método se compone de un filtro de supresión de artefacto basado en un algoritmo recursivo de mínimos cuadrados (RLS) seguido de un algoritmo de decisión shock/no-shock basado en técnicas de aprendizaje automático. Se usó una base de datos compuesta por 235 ritmos desfibrilables y 1451 no-desfibrilables adquiridos de pacientes en parada cardiorrespiratoria extra-hospitalaria (PCREH). Los ritmos de los pacientes fueron anotados en intervalos libres de artefacto. Los diagnósticos shock/no-shock obtenidos mediante el algoritmo de decisión fueron comparados con las anotaciones del ritmo para obtener la Sensibilidad (Se), Especificidad (Sp) y precisión balanceada (BAC) de la solución. Los resultados obtenidos fueron: 91.6% (Se), 95.4% (Sp) y 93.5% (BAC).Este trabajo ha recibido ayuda financiera del Ministerio de Economía y Competitividad, proyecto TEC2015- 64678-R, junto con el Fondo Europeo de Desarrollo Regional (FEDER). Ha recibido también financiación de la UPV/EHU mediante el proyecto GIU 17/031 y del Gobierno Vasco mediante la beca PRE-2017-2-0137

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

Acoustic sensing as a novel approach for cardiovascular monitoring at the wrist

Cardiovascular diseases are the number one cause of deaths globally. An increased cardiovascular risk can be detected by a regular monitoring of the vital signs including the heart rate, the heart rate variability (HRV) and the blood pressure. For a user to undergo continuous vital sign monitoring, wearable systems prove to be very useful as the device can be integrated into the user's lifestyle without affecting the daily activities. However, the main challenge associated with the monitoring of these cardiovascular parameters is the requirement of different sensing mechanisms at different measurement sites. There is not a single wearable device that can provide sufficient physiological information to track the vital signs from a single site on the body. This thesis proposes a novel concept of using acoustic sensing over the radial artery to extract cardiac parameters for vital sign monitoring. A wearable system consisting of a microphone is designed to allow the detection of the heart sounds together with the pulse wave, an attribute not possible with existing wrist-based sensing methods. Methods: The acoustic signals recorded from the radial artery are a continuous reflection of the instantaneous cardiac activity. These signals are studied and characterised using different algorithms to extract cardiovascular parameters. The validity of the proposed principle is firstly demonstrated using a novel algorithm to extract the heart rate from these signals. The algorithm utilises the power spectral analysis of the acoustic pulse signal to detect the S1 sounds and additionally, the K-means method to remove motion artifacts for an accurate heartbeat detection. The HRV in the short-term acoustic recordings is found by extracting the S1 events using the relative information between the short- and long-term energies of the signal. The S1 events are localised using three different characteristic points and the best representation is found by comparing the instantaneous heart rate profiles. The possibility of measuring the blood pressure using the wearable device is shown by recording the acoustic signal under the influence of external pressure applied on the arterial branch. The temporal and spectral characteristics of the acoustic signal are utilised to extract the feature signals and obtain a relationship with the systolic blood pressure (SBP) and diastolic blood pressure (DBP) respectively. Results: This thesis proposes three different algorithms to find the heart rate, the HRV and the SBP/ DBP readings from the acoustic signals recorded at the wrist. The results obtained by each algorithm are as follows: 1. The heart rate algorithm is validated on a dataset consisting of 12 subjects with a data length of 6 hours. The results demonstrate an accuracy of 98.78%, mean absolute error of 0.28 bpm, limits of agreement between -1.68 and 1.69 bpm, and a correlation coefficient of 0.998 with reference to a state-of-the-art PPG-based commercial device. A high statistical agreement between the heart rate obtained from the acoustic signal and the photoplethysmography (PPG) signal is observed. 2. The HRV algorithm is validated on the short-term acoustic signals of 5-minutes duration recorded from each of the 12 subjects. A comparison is established with the simultaneously recorded electrocardiography (ECG) and PPG signals respectively. The instantaneous heart rate for all the subjects combined together achieves an accuracy of 98.50% and 98.96% with respect to the ECG and PPG signals respectively. The results for the time-domain and frequency-domain HRV parameters also demonstrate high statistical agreement with the ECG and PPG signals respectively. 3. The algorithm proposed for the SBP/ DBP determination is validated on 104 acoustic signals recorded from 40 adult subjects. The experimental outputs when compared with the reference arm- and wrist-based monitors produce a mean error of less than 2 mmHg and a standard deviation of error around 6 mmHg. Based on these results, this thesis shows the potential of this new sensing modality to be used as an alternative, or to complement existing methods, for the continuous monitoring of heart rate and HRV, and spot measurement of the blood pressure at the wrist.Open Acces

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

Mechanical and electrical interrelations in normal and ischaemic heart

Imperial Users onl

Biochemical and electrophysiological markers predictive of return of spontaneous circulation and post-resuscitation outcome

The majority of patients resuscitated from cardiac arrest (CA) subsequently die due to post-cardiac arrest syndrome (PCAS), whose mechanisms are only partially understood. We adopted an approach of untargeted/targeted plasma metabolomics in rats to identify metabolites involved in the mechanisms of PCAS to be tested as predictors of outcome. Activation of the kynurenine pathway (KP) for tryptophan (TRP) degradation was demonstrated in rats, pigs and in a small cohort of patients. Decreases in TRP occurred during the post-CA period and were accompanied by significant increases in KP metabolites, 3-hydroxyanthranilic acid (3 -HAA) and kynurenic acid in each species, that persisted up to 3-5 days post-CA (p<0.01). KP metabolites changes were significantly related to the severity of myocardial and cerebral injuries and survival. Finally, when tested in 155 patients resuscitated from CA, KP metabolites were significantly higher in patients with poor outcomes. The quality of chest compression (CC) is another major issue for cardiopulmonary resuscitation (CPR) success and survival. The decision whether to interrupt CC to deliver a defibrillation (DF) is difficult. The potential benefit of a DF guided by a real time ventricular fibrillation (YF) waveform analysis would maximize DF success, minimize CC interruptions and myocardial damage by repetitive and unnecessary DFs. We evaluated amplitude spectrum area (AMSA) as predictor of DF outcome in two large databases of out-of-hospital VFs, from US (609 patients) and Italy (1.617 patients). AMSA was significantly higher prior to a successful DF than prior to an unsuccessful one (p<0.0001). Thresholds for prediction of successful and unsuccessful DFs were 16-17 mV-Hz for success and <7 mV-Hz for failure, with a positive predictive value of 80% and a negative predictive value of 97%. AMSA was a better predictor of DF outcome (AUC 0.86, p<0.0001) compared to other VF parameters, i.e. amplitude and frequencies. In conclusion, AMSA would be a useful tool for guiding CPR

Shear-promoted drug encapsulation into red blood cells: a CFD model and μ-PIV analysis

The present work focuses on the main parameters that influence shear-promoted encapsulation of drugs into erythrocytes. A CFD model was built to investigate the fluid dynamics of a suspension of particles flowing in a commercial micro channel. Micro Particle Image Velocimetry (μ-PIV) allowed to take into account for the real properties of the red blood cell (RBC), thus having a deeper understanding of the process. Coupling these results with an analytical diffusion model, suitable working conditions were defined for different values of haematocrit

Medical Robotics for use in MRI Guided Endoscopy

Interventional Magnetic Resonance Imaging (MRI) is a developing field that aims to provide intra-operative MRI to a clinician to guide diagnostic or therapeutic medical procedures. MRI provides excellent soft tissue contrast at sub-millimetre resolution in both 2D and 3D without the need for ionizing radiation. Images can be acquired in near real-time for guidance purposes. Operating in the MR environment brings challenges due to the high static magnetic field, switching magnetic field gradients and RF excitation pulses. In addition high field closed bore scanners have spatial constraints that severely limit access to the patient. This thesis presents a system for MRI-guided Endoscopic Retrograde Cholangio-pancreatography (ERCP). This includes a remote actuation system that enables an MRI-compatible endoscope to be controlled whilst the patient is inside the MRI scanner, overcoming the spatial and procedural constraints imposed by the closed scanner bore. The modular system utilises non-magnetic ultrasonic motors and is designed for image-guided user-in-the-loop control. A novel miniature MRI compatible clutch has been incorporated into the design to reduce the need for multiple parallel motors. The actuation system is MRI compatible does not degrade the MR images below acceptable levels. User testing showed that the actuation system requires some degree of training but enables completion of a simulated ERCP procedure with no loss of performance. This was demonstrated using a tailored ERCP simulator and kinematic assessment tool, which was validated with users from a range of skill levels to ensure that it provides an objective measurement of endoscopic skill. Methods of tracking the endoscope in real-time using the MRI scanner are explored and presented here. Use of the MRI-guided ERCP system was shown to improve the operator’s ability to position the endoscope in an experimental environment compared with a standard fluoroscopic-guided system.Open Acces