Feedback on the Rate and Depth of Chest Compressions during Cardiopulmonary Resuscitation Using Only Accelerometers

Background Quality of cardiopulmonary resuscitation (CPR) is key to increase survival from cardiac arrest. Providing chest compressions with adequate rate and depth is difficult even for well-trained rescuers. The use of real-time feedback devices is intended to contribute to enhance chest compression quality. These devices are typically based on the double integration of the acceleration to obtain the chest displacement during compressions. The integration process is inherently unstable and leads to important errors unless boundary conditions are applied for each compression cycle. Commercial solutions use additional reference signals to establish these conditions, requiring additional sensors. Our aim was to study the accuracy of three methods based solely on the acceleration signal to provide feedback on the compression rate and depth. Materials and Methods We simulated a CPR scenario with several volunteers grouped in couples providing chest compressions on a resuscitation manikin. Different target rates (80, 100, 120, and 140 compressions per minute) and a target depth of at least 50 mm were indicated. The manikin was equipped with a displacement sensor. The accelerometer was placed between the rescuer's hands and the manikin's chest. We designed three alternatives to direct integration based on different principles (linear filtering, analysis of velocity, and spectral analysis of acceleration). We evaluated their accuracy by comparing the estimated depth and rate with the values obtained from the reference displacement sensor. Results The median (IQR) percent error was 5.9% (2.8-10.3), 6.3% (2.9-11.3), and 2.5% (1.2-4.4) for depth and 1.7% (0.0-2.3), 0.0% (0.0-2.0), and 0.9% (0.4-1.6) for rate, respectively. Depth accuracy depended on the target rate (p < 0.001) and on the rescuer couple (p < 0.001) within each method. Conclusions Accurate feedback on chest compression depth and rate during CPR is possible using exclusively the chest acceleration signal. The algorithm based on spectral analysis showed the best performance. Despite these encouraging results, further research should be conducted to asses the performance of these algorithms with clinical data.This work was supported by Ministerio de Economia y Competitividad: TEC2012-31144 (http://www.mineco.gob.es, SRDG JR DMGO) and Basque Government (Gobierno Vasco): BFI-2011-166 (https://www.euskadi.eus, DMGO). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

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

Audiovisual Feedback Devices for Chest Compression Quality during CPR

During cardiopulmonary resuscitation (CPR), chest compression quality is the key for patient survival. However, several studies have shown that both professionals and laypeople often apply CPR at improper rates and depths. The use of real-time feedback devices increases adherence to CPR quality guidelines. This chapter explores new alternatives to provide feedback on the quality of chest compressions during CPR. First, we describe and evaluate three methods to compute chest compression depth and rate using exclusively the chest acceleration. To evaluate the accuracy of the methods, we used episodes of simulated cardiac arrest acquired in a manikin model. One of the methods, based on the spectral analysis of the acceleration, was particularly accurate in a wide range of conditions. Then, we assessed the feasibility of using the transthoracic impedance (TI) signal acquired through defibrillation pads to provide feedback on chest compression depth and rate. For that purpose, we retrospectively analyzed three databases of out-of-hospital cardiac arrest episodes. When a wide variety of patients and rescuers were included, TI could not be used to reliably estimate the compression depth. However, compression rate could be accurately estimated. Development of simpler methods to provide feedback on CPR quality could contribute to the widespread of these devices

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

Waveform Capnography for Monitoring Ventilation during Cardiopulmonary Resuscitation: The Problem of Chest Compression Artifact

Sudden cardiac arrest (SCA) is the sudden cessation of the heart’s effective pumping function, confirmed by the absence of pulse and breathing. Without appropriate treatment, it leads to sudden cardiac death, considered responsible for half of the global cardiac disease deaths. Cardiopulmonary resuscitation (CPR) is a key intervention during SCA. Current resuscitation guidelines emphasize the use of waveform capnography during CPR in order to enhance CPR quality and improve patient outcomes. Capnography represents the concentration of the partial pressure of carbon dioxide (CO2) in respiratory gases and reflects ventilation and perfusion of the patient. Waveform capnography should be used for confirming the correct placement of the tracheal tube and monitoring ventilation. Other potential uses of capnography in resuscitation involve monitoring CPR quality, early identification of restoration of spontaneous circulation (ROSC), and determination of patient prognosis. An important role of waveform capnography is ventilation rate monitoring to prevent overventilation. However, some studies have reported the appearance of high-frequency oscillations synchronized with chest compressions superimposed on the capnogram. This chapter explores the incidence of chest compression artifact in out-of-hospital capnograms, assesses its negative influence in the automated detection of ventilations, and proposes several methods to enhance ventilation detection and capnography waveform

A Feasibility Study for Measuring Accurate Chest Compression Depth and Rate on Soft Surfaces Using Two Accelerometers and Spectral Analysis

Background. Cardiopulmonary resuscitation (CPR) feedback devices are being increasingly used. However, current accelerometerbased devices overestimate chest displacement when CPR is performed on soft surfaces, which may lead to insufficient compression depth. Aim. To assess the performance of a new algorithm for measuring compression depth and rate based on two accelerometers in a simulated resuscitation scenario. Materials and Methods. Compressions were provided to a manikin on two mattresses, foam and sprung, with and without a backboard. One accelerometer was placed on the chest and the second at the manikin&apos;s back. Chest displacement and mattress displacement were calculated from the spectral analysis of the corresponding acceleration every 2 seconds and subtracted to compute the actual sternal-spinal displacement. Compression rate was obtained from the chest acceleration. Results. Median unsigned error in depth was 2.1 mm (4.4%). Error was 2.4 mm in the foam and 1.7 mm in the sprung mattress ( &lt; 0.001). Error was 3.1/2.0 mm and 1.8/1.6 mm with/without backboard for foam and sprung, respectively ( &lt; 0.001). Median error in rate was 0.9 cpm (1.0%), with no significant differences between test conditions. Conclusion. The system provided accurate feedback on chest compression depth and rate on soft surfaces. Our solution compensated mattress displacement, avoiding overestimation of compression depth when CPR is performed on soft surfaces

Modeling the impact of ventilations on the capnogram in out-of-hospital cardiac arrest

Aim Current resuscitation guidelines recommend waveform capnography as an indirect indicator of perfusion during cardiopulmonary resuscitation (CPR). Chest compressions (CCs) and ventilations during CPR have opposing effects on the exhaled carbon dioxide (CO2) concentration, which need to be better characterized. The purpose of this study was to model the impact of ventilations in the exhaled CO2 measured from capnograms collected during out-of-hospital cardiac arrest (OHCA) resuscitation. Methods We retrospectively analyzed OHCA monitor-defibrillator files with concurrent capnogram, compression depth, transthoracic impedance and ECG signals. Segments with CC pauses, two or more ventilations, and with no pulse-generating rhythm were selected. Thus, only ventilations should have caused the decrease in CO2 concentration. The variation in the exhaled CO2 concentration with each ventilation was modeled with an exponential decay function using non-linear-least-squares curve fitting. Results Out of the original 1002 OHCA dataset (one per patient), 377 episodes had the required signals, and 196 segments from 96 patients met the inclusion criteria. Airway type was endotracheal tube in 64.8% of the segments, supraglottic King LT-D (TM) in 30.1%, and unknown in 5.1%. Median (IQR) decay factor of the exhaled CO2 concentration was 10.0% (7.8 - 12.9) with R-2 = 0.98(0.95 - 0.99). Differences in decay factor with airway type were not statistically significant (p = 0.17). From these results, we propose a model for estimating the contribution of CCs to the end-tidal CO2 level between consecutive ventilations and for estimating the end-tidal CO2 variation as a function of ventilation rate. Conclusion We have modeled the decrease in exhaled CO2 concentration with ventilations during chest compression pauses in CPR. This finding allowed us to hypothesize a mathematical model for explaining the effect of chest compressions on ETCO2 compensating for the influence of ventilation rate during CPR. However, further work is required to confirm the validity of this model during ongoing chest compressions.The Basque Government provided support in the form of a grant for research groups (IT1087-16) for authors Jose Julio Gutierrez, Jesus Maria Ruiz, Sofia Ruiz de Gauna, and Mikel Leturiondo; and in the form of a predoctoral grant (PRE-2017-2-0201) for author Mikel Leturiondo (https://www.euskadi.eus).The Spanish Ministry of Economy, Industry and Competitiveness provided support in the form of a grant for research projects (RTI2018-094396-B-I00) for authors Jose Julio Gutierrez, Jesus Maria Ruiz, Sofia Ruiz de Gauna, and Mikel Leturiondo; and in the form of the program Torres Quevedo (PTQ-16-08201) for author Digna Maria Gonzalez-Otero (http://www.ciencia.gob.es/).Bexen Cardio, a Spanish medical device manufacturer, provided support in the form of a salary for author Digna Mari ' a Gonza ' lez-Otero. None of the above funders 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 James Knox Russell, Carlos Corcuera, Juan Francisco Urtusagasti, and Mohamud Ramzan Daya received no funding for this work

The Role of Chest Compressions on Ventilation during Advanced Cardiopulmonary Resuscitation

There is growing interest in the quality of manual ventilation during cardiopulmonary resuscitation (CPR), but accurate assessment of ventilation parameters remains a challenge. Waveform capnography is currently the reference for monitoring ventilation rate in intubated patients, but fails to provide information on tidal volumes and inspiration–expiration timing. Moreover, the capnogram is often distorted when chest compressions (CCs) are performed during ventilation compromising its reliability during CPR. Our main purpose was to characterize manual ventilation during CPR and to assess how CCs may impact on ventilation quality. Methods: Retrospective analysis were performed of CPR recordings fromtwo databases of adult patients in cardiac arrest including capnogram, compression depth, and airway flow, pressure and volume signals. Using automated signal processing techniques followed by manual revision, individual ventilations were identified and ventilation parameters were measured. Oscillations on the capnogram plateau during CCs were characterized, and its correlation with compression depth and airway volume was assessed. Finally, we identified events of reversed airflow caused by CCs and their effect on volume and capnogram waveform. Results: Ventilation rates were higher than the recommended 10 breaths/min in 66.7% of the cases. Variability in ventilation rates correlated with the variability in tidal volumes and other ventilatory parameters. Oscillations caused by CCs on capnograms were of high amplitude (median above 74%) and were associated with low pseudo-volumes (median 26 mL). Correlation between the amplitude of those oscillations with either the CCs depth or the generated passive volumes was low, with correlation coefficients of −0.24 and 0.40, respectively. During inspiration and expiration, reversed airflow events caused opposed movement of gases in 80% of ventilations. Conclusions: Our study confirmed lack of adherence between measured ventilation rates and the guideline recommendations, and a substantial dispersion in manual ventilation parameters during CPR. Oscillations on the capnogram plateau caused by CCs did not correlate with compression depth or associated small tidal volumes. CCs caused reversed flow during inspiration, expiration and in the interval between ventilations, sufficient to generate volume changes and causing oscillations on capnogram. Further research is warranted to assess the impact of these findings on ventilation quality during CPR.This research was funded by the grant PID2021-126021OB-I00 by MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe, and by the grant IT1590-22 by the Basque Government. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

A Feasibility Study for Measuring Accurate Chest Compression Depth and Rate on Soft Surfaces Using Two Accelerometers and Spectral Analysis

Background. Cardiopulmonary resuscitation (CPR) feedback devices are being increasingly used. However, current accelerometer-based devices overestimate chest displacement when CPR is performed on soft surfaces, which may lead to insufficient compression depth. Aim. To assess the performance of a new algorithm for measuring compression depth and rate based on two accelerometers in a simulated resuscitation scenario. Materials and Methods. Compressions were provided to a manikin on two mattresses, foam and sprung, with and without a backboard. One accelerometer was placed on the chest and the second at the manikin’s back. Chest displacement and mattress displacement were calculated from the spectral analysis of the corresponding acceleration every 2 seconds and subtracted to compute the actual sternal-spinal displacement. Compression rate was obtained from the chest acceleration. Results. Median unsigned error in depth was 2.1 mm (4.4%). Error was 2.4 mm in the foam and 1.7 mm in the sprung mattress (p<0.001). Error was 3.1/2.0 mm and 1.8/1.6 mm with/without backboard for foam and sprung, respectively (p<0.001). Median error in rate was 0.9 cpm (1.0%), with no significant differences between test conditions. Conclusion. The system provided accurate feedback on chest compression depth and rate on soft surfaces. Our solution compensated mattress displacement, avoiding overestimation of compression depth when CPR is performed on soft surfaces