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

Monitoring Chest Compression Rate in Automated External Defibrillators Using the Autocorrelation of the Transthoracic Impedance

Aim High-quality chest compressions is challenging for bystanders and first responders to out-of-hospital cardiac arrest (OHCA). Long compression pauses and compression rates higher than recommended are common and detrimental to survival. Our aim was to design a simple and low computational cost algorithm for feedback on compression rate using the transthoracic impedance (TI) acquired by automated external defibrillators (AEDs). Methods ECG and TI signals from AED recordings of 242 OHCA patients treated by basic life support (BLS) ambulances were retrospectively analyzed. Beginning and end of chest compression series and each individual compression were annotated. The algorithm computed a biased estimate of the autocorrelation of the TI signal in consecutive non-overlapping 2-s analysis windows to detect the presence of chest compressions and estimate compression rate. Results A total of 237 episodes were included in the study, with a median (IQR) duration of 10 (6-16) min. The algorithm performed with a global sensitivity in the detection of chest compressions of 98.7%, positive predictive value of 98.7%, specificity of 97.1%, and negative predictive value of 97.1% (validation subset including 207 episodes). The unsigned error in the estimation of compression rate was 1.7 (1.3-2.9) compressions per minute. Conclusion Our algorithm is accurate and robust for real-time guidance on chest compression rate using AEDs. The algorithm is simple and easy to implement with minimal software modifications. Deployment of AEDs with this capability could potentially contribute to enhancing the quality of chest compressions in the first minutes from collapse.The Basque Government provided support in the form of a grant for research groups (IT1087-16) for authors Sofia Ruiz de Gauna, Jesus Maria Ruiz, and Jose Julio Gutierrez. The Spanish Ministry of Economy, Industry and Competitiveness provided support in the form of a grant for research projects (RTI2018-094396-BI00) for authors Sofia Ruiz de Gauna, Jesus Maria Ruiz, and Jose Julio Gutierrez; and in the form of the program Torres Quevedo (PTQ-16-08201) for author Digna Maria Gonzalez-Otero. The University of the Basque Country (UPV/EHU) provided support in the form of a grant for collaboration between research groups and companies (US18/30) for authors Sofia Ruiz de Gauna, Jesus Maria Ruiz, and Jose Julio Gutierrez. Bexen Cardio, a Spanish medical device manufacturer, provided support in the form of a salary for author Digna Mara Gonzalez-Otero. None of the above funding organizations had any additional role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific role of each author is articulated in the Author Contributions section. Authors Daniel Alonso, Carlos Corcuera, and Juan Francisco Urtusagasti received no funding for this work

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

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

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