Feasibility of waveform capnography as a non-invasive monitoring tool during cardiopulmonary resuscitation

178 p.Sudden cardiac arrest (SCA) is one of the leading causes of death in the industrialized world and it includes the sudden cessation of circulation and consciousness, confirmed by the absence of pulse and breathing. Cardiopulmonary resuscitation (CPR) is one of the key interventions for patient survival after SCA, a life-saving procedure that combines chest compressions and ventilations to maintain a minimal oxygenated blood flow.To deliver oxygen, an adequate blood flow must be generated, by effective CPR, during the majority of the cardiac arrest time. Although monitoring the quality of CPR performed by rescuers during cardiac arrest has been a huge step forward in resuscitation science, in 2013, a consensus statement from the American Heart Association prioritized a new type of CPR quality monitoring focused on the physiological response of the patient instead of how the rescuer is doing.To that end, current resuscitation guidelines emphasize the use of waveform capnography during CPR for patient monitoring. Among several advantages such as ensure correct tube placement, one of its most important roles is to monitor ventilation rate, helping to avoid potentially harmful over-ventilation. In addition, waveform capnography would enable monitoring CPR quality, early detection of ROSC and determining patient prognosis. However, several studies have reported the appearance of fast oscillations superimposed on the capnogram, hereinafter CC-artifact, which may hinder a feasible use of waveform capnography during CPR. In addition to the possible lack of reliability, several factors need to be taken into account when interpreting ETCO2 measurements. Chest compressions and ventilation have opposing effects on ETCO2 levels. Chest compressions increase CO2 concentration, delivering CO2 from the tissues to the lungs, whilst ventilations remove CO2 from the lungs, decreasing ETCO2. Thus, ventilation rate acts as a significant confounding factor.This thesis analyzes the feasibility of waveform capnography as non-invasive monitoring tool of the physiological response of the patient to resuscitation efforts. A set of four intermediate goals was defined.First, we analyzed the incidence and morphology of the CC-artifact and assessed its negative influence in the detection of ventilations and in ventilation rate and ETCO2 measurement. Second, several artifact suppression techniques were used to improve ventilation detection and to enhance capnography waveform. Third, we applied a novel strategy to model the impact of ventilations and ventilation rate on the exhaled CO2 measured in out-of-hospital cardiac arrest capnograms, which could allow to measure the change in ETCO2 attributable to chest compressions by removing the influence of concurrent ventilations. Finally, we studied if the assessment of the ETCO2 trends during chest compressions pauses could allow to detect return of spontaneous circulation, a metric that could be useful as an adjunct to other decision tool

Feasibility of waveform capnography as a non-invasive monitoring tool during cardiopulmonary resuscitation

178 p.Sudden cardiac arrest (SCA) is one of the leading causes of death in the industrialized world and it includes the sudden cessation of circulation and consciousness, confirmed by the absence of pulse and breathing. Cardiopulmonary resuscitation (CPR) is one of the key interventions for patient survival after SCA, a life-saving procedure that combines chest compressions and ventilations to maintain a minimal oxygenated blood flow.To deliver oxygen, an adequate blood flow must be generated, by effective CPR, during the majority of the cardiac arrest time. Although monitoring the quality of CPR performed by rescuers during cardiac arrest has been a huge step forward in resuscitation science, in 2013, a consensus statement from the American Heart Association prioritized a new type of CPR quality monitoring focused on the physiological response of the patient instead of how the rescuer is doing.To that end, current resuscitation guidelines emphasize the use of waveform capnography during CPR for patient monitoring. Among several advantages such as ensure correct tube placement, one of its most important roles is to monitor ventilation rate, helping to avoid potentially harmful over-ventilation. In addition, waveform capnography would enable monitoring CPR quality, early detection of ROSC and determining patient prognosis. However, several studies have reported the appearance of fast oscillations superimposed on the capnogram, hereinafter CC-artifact, which may hinder a feasible use of waveform capnography during CPR. In addition to the possible lack of reliability, several factors need to be taken into account when interpreting ETCO2 measurements. Chest compressions and ventilation have opposing effects on ETCO2 levels. Chest compressions increase CO2 concentration, delivering CO2 from the tissues to the lungs, whilst ventilations remove CO2 from the lungs, decreasing ETCO2. Thus, ventilation rate acts as a significant confounding factor.This thesis analyzes the feasibility of waveform capnography as non-invasive monitoring tool of the physiological response of the patient to resuscitation efforts. A set of four intermediate goals was defined.First, we analyzed the incidence and morphology of the CC-artifact and assessed its negative influence in the detection of ventilations and in ventilation rate and ETCO2 measurement. Second, several artifact suppression techniques were used to improve ventilation detection and to enhance capnography waveform. Third, we applied a novel strategy to model the impact of ventilations and ventilation rate on the exhaled CO2 measured in out-of-hospital cardiac arrest capnograms, which could allow to measure the change in ETCO2 attributable to chest compressions by removing the influence of concurrent ventilations. Finally, we studied if the assessment of the ETCO2 trends during chest compressions pauses could allow to detect return of spontaneous circulation, a metric that could be useful as an adjunct to other decision tool

Removing piston-driven mechanical chest compression artefacts from the ECG

Piston-driven mechanical chest compression (CC) devices induce a quasi-periodic artefact in the ECG, making rhythm diagnosis unreliable. Data from 230 out-of-hospital cardiac arrest (OHCA) patients were collected in which CCs were delivered using the piston driven LUCAS-2 device. Underlying rhythms were annotated by expert reviewers in artefact-free intervals. Two artefact removal methods (filters) were introduced: a static solution based on Goertzel’s algorithm, and an adaptive solution based on a Recursive Least Squares (RLS) filter. The filtered ECG was diagnosed by a shock/no-shock decision algorithm used in a commercial defibrillator and compared with the rhythm annotations. Filter performance was evaluated in terms of balanced accuracy (BAC), the mean of sensitivity (shockable) and specificity (nonshockable). Compared to the unfiltered signal, the static filter increased BAC by 20 points, and the RLS filter by 25 points. Adaptive filtering results in 99.0% sensitivity and 87.3% specificity

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

An Accurate Shock Advise Algorithm for Use During Piston-Driven Chest Compressions

Mechanically delivered chest compressions induce artifacts in the ECG that can lead to an incorrect diagnosis of the shock advice algorithms implemented in the defibrillators. This forces the rescuer to stop cardiopulmonary resuscitation (CPR) compromising circulation and thus reducing the probability of survival. This paper introduces a new approach for a reliable rhythm analysis during mechanical compressions which consists of an artifact supression filter based on the recursive least squares algorithm, and a shock/no-shock decision algorithm based on machine learning techniques that uses features obtained from the filtered ECG. Data were collected from 230 out-of-hospital cardiac arrest patients treated with the LUCAS CPR device. The underlying rhythms were annotated in artifact-free intervals by consesus of expert resuscitation rhythm reviewers. Shock/no-shock diagnoses obtained through the decision algorithm were compared with the rhythm annotations to obtain the sensitivity (Se), specificity (Sp) and balanced accuracy (BAC) of the method. The results obtained were: 94.7% (Se), 97.1% (Sp) and 95.9% (BAC)

Reduction of CPR artifacts in the ventricular fibrillation ECG by coherent line removal

<p>Abstract</p> <p>Background</p> <p>Interruption of cardiopulmonary resuscitation (CPR) impairs the perfusion of the fibrillating heart, worsening the chance for successful defibrillation. Therefore ECG-analysis <it>during ongoing chest compression </it>could provide a considerable progress in comparison with standard analysis techniques working only during "hands-off" intervals.</p> <p>Methods</p> <p>For the reduction of CPR-related artifacts in ventricular fibrillation ECG we use a localized version of the <it>coherent line removal </it>algorithm developed by Sintes and Schutz. This method can be used for removal of periodic signals with sufficiently coupled harmonics, and can be adapted to specific situations by optimal choice of its parameters (e.g., the number of harmonics considered for analysis and reconstruction). Our testing was done with 14 different human ventricular fibrillation (VF) ECGs, whose fibrillation band lies in a frequency range of [1 Hz, 5 Hz]. The VF-ECGs were mixed with 12 different ECG-CPR-artifacts recorded in an animal experiment during asystole. The length of each of the ECG-data was chosen to be 20 sec, and testing was done for all 168 = 14 × 12 pairs of data. VF-to-CPR ratio was chosen as -20 dB, -15 dB, -10 dB, -5 dB, 0 dB, 5 dB and 10 dB. Here -20 dB corresponds to the highest level of CPR-artifacts.</p> <p>Results</p> <p>For non-optimized <it>coherent line removal </it>based on signals with a VF-to-CPR ratio of -20 dB, -15 dB, -10 dB, -5 dB and 0 dB, the signal-to-noise gains (SNR-gains) were 9.3 ± 2.4 dB, 9.4 ± 2.4 dB, 9.5 ± 2.5 dB, 9.3 ± 2.5 dB and 8.0 ± 2.7 (mean ± std, <it>n </it>= 168), respectively. Characteristically, an original VF-to-CPR ratio of -10 dB, corresponds to a variance ratio <it>var</it>(VF):<it>var</it>(CPR) = 1:10. An improvement by 9.5 dB results in a restored VF-to-CPR ratio of -0.5 dB, corresponding to a variance ratio <it>var</it>(VF):<it>var</it>(CPR) = 1:1.1, the variance of the CPR in the signal being reduced by a factor of 8.9.</p> <p>Discussion</p> <p>The <it>localized coherent line removal </it>algorithm uses the information of a single ECG channel. In contrast to multi-channel algorithms, no additional information such as thorax impedance, blood pressure, or pressure exerted on the sternum during CPR is required. Predictors of defibrillation success such as mean and median frequency of VF-ECGs containing CPR-artifacts are prone to being governed by the harmonics of the artifacts. Reduction of CPR-artifacts is therefore necessary for determining reliable values for estimators of defibrillation success.</p> <p>Conclusions</p> <p>The <it>localized coherent line removal </it>algorithm reduces CPR-artifacts in VF-ECG, but does not eliminate them. Our SNR-improvements are in the same range as offered by multichannel methods of Rheinberger et al., Husoy et al. and Aase et al. The latter two authors dealt with different ventricular rhythms (VF and VT), whereas here we dealt with VF, only. Additional developments are necessary before the algorithm can be tested in real CPR situations.</p

Feedback systems for the quality of chest compressions during cardiopulmonary resuscitation

167 p.Se define la parada cardiorrespiratoria como la detención súbita de la actividad mecánica del corazón, confirmada por la ausencia de signos de circulación. En caso de parada cardiorrespiratoria, dos actuaciones son clave para la supervivencia del paciente: la reanimación cardiopulmonar (RCP) precoz, y la desfibrilación precoz. La RCP consiste en proporcionar compresiones torácicas y ventilaciones al paciente para mantener un mínimo flujo de sangre oxigenada a los órganos vitales. La calidad de las compresiones está relacionada con la supervivencia del paciente. Por esta razón las guías de resucitación recomiendan el uso de sistemas de feedback que monitorizan la calidad de la RCP en tiempo real. Estos dispositivos se sitúan generalmente entre el pecho del paciente y las manos del rescatador, y guían al rescatador para ayudarle a alcanzar la profundidad y frecuencia de compresión objetivo. Esta tesis explora nuevas alternativas para monitorizar la calidad de las compresiones durante la RCP. Se han seguido dos estrategias: usar la señal de impedancia transtorácica (ITT), que es adquirida por los desfibriladores actuales a través de los parches de desfibrilación, y usar la aceleración del pecho, que podría ser registrada usando un dispositivo adicional

Ventricular fibrillation detection in ventricular fibrillation signals corrupted by cardiopulmonary resuscitation artifact

This study is focused on the removal of artifacts due to Cardio Pulmonary Resuscitation (CPR) on Ventricular Fibrillation ECG signals. The aim is to allow a reliable analysis of the cardiac rhythm by an AED or the defibrillation success analysis during CPR episodes. The research is based on a human model for the CPR artifact and the VF ECG signals. The test signals were generated adding the CPR artifact (noise) to the VF (signal), with a known Signal-to-Noise Ratio (SNR). The results of the adaptive Kalman filtering have been obtained according to three different levels: SNR improvement; Sensitivity improvement in the AED algorithm for the detection of shockable rhythm; and Variations of the significant frequencies, compared to the values obtained with the original VF signals. In all cases, remarkable results have been achieved regarding to the efficiency in the artifact removal. 1

Applications of the Transthoracic Impedance Signal during Resuscitation

Defibrillators acquire both the ECG and the transthoracic impedance (TI) signal through defibrillation pads. TI represents the resistance of the thorax to current flow, and is measured by defibrillators to check that defibrillation pads are correctly attached to the chest of the patient. Additionally, some defibrillators use the TI measurement to adjust the energy of the defibrillation pulse. Changes in tissue composition due to redistribution and movement of fluids induce fluctuations in the TI. Blood flow during the cardiac cycle generates small fluctuations synchronized to each heartbeat. Respiration (or assisted ventilation) also causes changes in the TI. Additionally, during cardiopulmonary resuscitation (CPR), chest compressions cause a disturbance in the electrode-skin interface, inducing artifacts in the TI signal. These fluctuations may provide useful information regarding CPR quality, length of pauses in chest compressions (no flow time), presence of circulation, etc. This chapter explores the new applications of the transthoracic impedance signal acquired through defibrillation pads during resuscitative attempts