Sofía Ruiz de Gauna

A high-temporal resolution algorithm to discriminate shockable from nonshockable rhythms in adults and children

AIM To design the core algorithm of a high-temporal resolution rhythm analysis algorithm for automated external defibrillators (AEDs) valid for adults and children. Records from adult and paediatric patients were used all together to optimize and test the performance of the algorithm. METHODS A total of 574 shockable and 1126 nonshockable records from 1379 adult patients, and 57 shockable and 503 nonshockable records from 377 children aged between 1 and 8 years were used. The records were split into two groups for development and testing. The core algorithm analyses ECG segments of 3.2s duration and classifies the segments as nonshockable or likely shockable combining a time, slope and frequency domain analysis to detect normally conducted QRS complexes. RESULTS The algorithm correctly identified 98% of nonshockable segments, 97.5% in adults and 98.4% in children, and identified 99.5% of shockable segments as likely shockable, 100% in adults and 96% in children. When likely shockable segments were further analysed in terms of regularity, spectral content and heart rate to form a complete rhythm analysis algorithm the overall specificity increased to 99.6% and the sensitivity was 99.1%. CONCLUSION Paediatric and adult rhythms can be accurately diagnosed using 3.2s ECG segments. A single algorithm safe for children and adults can simplify AED use, and its high temporal resolution shortens pre-shock pauses which may contribute to improve resuscitation outcome.

Rhythm Analysis during Cardiopulmonary Resuscitation: Past, Present, and Future

Survival from out-of-hospital cardiac arrest depends largely on two factors: early cardiopulmonary resuscitation (CPR) and early defibrillation. CPR must be interrupted for a reliable automated rhythm analysis because chest compressions induce artifacts in the ECG. Unfortunately, interrupting CPR adversely affects survival. In the last twenty years, research has been focused on designing methods for analysis of ECG during chest compressions. Most approaches are based either on adaptive filters to remove the CPR artifact or on robust algorithms which directly diagnose the corrupted ECG. In general, all the methods report low specificity values when tested on short ECG segments, but how to evaluate the real impact on CPR delivery of continuous rhythm analysis during CPR is still unknown. Recently, researchers have proposed a new methodology to measure this impact. Moreover, new strategies for fast rhythm analysis during ventilation pauses or high-specificity algorithms have been reported. Our objective is to present a thorough review of the field as the starting point for these late developments and to underline the open questions and future lines of research to be explored in the following years.

Cardiopulmonary resuscitation artefact suppression using a Kalman filter and the frequency of chest compressions as the reference signal

AIM To develop a new method to suppress the artefact generated by chest compressions during cardiopulmonary resuscitation (CPR) using only the frequency of the compressions as additional information. MATERIALS AND METHODS The CPR artefact suppression method was developed and tested using a database of 381 ECG records (89 shockable and 292 non-shockable) from 299 patients. All records were extracted from real out-of-hospital cardiac arrest episodes. The suppression method consists of a Kalman filter that uses the frequency of the measured compressions to estimate the artefact and to remove it from the ECG. The performance of the filter was evaluated by comparing the sensitivity and specificity of an automated external defibrillator before and after the artefact suppression. RESULTS For the test database, the sensitivity improved from 57.8% (95% confidence interval, 43.3-71.0%) to 93.3% (81.5-98.4%) and the specificity decreased from 92.5% (87.0-95.9%) to 89.1% (83.0-93.3%). CONCLUSION For a similar sensitivity, we obtained better specificity than that reported for other methods, although still short of the values recommended by the American Heart Association. The results suggest that the CPR artefact can be accurately modelled using only the frequency of the compressions. This information could be easily acquired through the defibrillators CPR help pads, with minimal hardware modifications.

Reliability and accuracy of the thoracic impedance signal for measuring cardiopulmonary resuscitation quality metrics

AIM To determine the accuracy and reliability of the thoracic impedance (TI) signal to assess cardiopulmonary resuscitation (CPR) quality metrics. METHODS A dataset of 63 out-of-hospital cardiac arrest episodes containing the compression depth (CD), capnography and TI signals was used. We developed a chest compression (CC) and ventilation detector based on the TI signal. TI shows fluctuations due to CCs and ventilations. A decision algorithm classified the local maxima as CCs or ventilations. Seven CPR quality metrics were computed: mean CC-rate, fraction of minutes with inadequate CC-rate, chest compression fraction, mean ventilation rate, fraction of minutes with hyperventilation, instantaneous CC-rate and instantaneous ventilation rate. The CD and capnography signals were accepted as the gold standard for CC and ventilation detection respectively. The accuracy of the detector was evaluated in terms of sensitivity and positive predictive value (PPV). Distributions for each metric computed from the TI and from the gold standard were calculated and tested for normality using one sample Kolmogorov-Smirnov test. For normal and not normal distributions, two sample t-test and Mann-Whitney U test respectively were applied to test for equal means and medians respectively. Bland-Altman plots were represented for each metric to analyze the level of agreement between values obtained from the TI and gold standard. RESULTS The CC/ventilation detector had a median sensitivity/PPV of 97.2%/97.7% for CCs and 92.2%/81.0% for ventilations respectively. Distributions for all the metrics showed equal means or medians, and agreements >95% between metrics and gold standard was achieved for most of the episodes in the test set, except for the instantaneous ventilation rate. CONCLUSION With our data, the TI can be reliably used to measure all the CPR quality metrics proposed in this study, except for the instantaneous ventilation rate.

Can thoracic impedance monitor the depth of chest compressions during out-of-hospital cardiopulmonary resuscitation?

AIM To analyze the relationship between the depth of the chest compressions and the fluctuation caused in the thoracic impedance (TI) signal in out-of-hospital cardiac arrest (OHCA). The ultimate goal was to evaluate whether it is possible to identify compressions with inadequate depth using information of the TI waveform. METHODS 60 OHCA episodes were extracted, one per patient, containing both compression depth (CD) and TI signals. Every 5s the mean value of the maxima of the CD, Dmax, and three features characterizing the fluctuations caused by the compressions in the TI waveform (peak-to-peak amplitude, area and curve length) were computed. The linear relationship between Dmax and the TI features was tested using Pearson correlation coefficient (r) and univariate linear regression for the whole population, for each patient independently, and for series of compressions provided by a single rescuer. The power of the three TI features to classify each 5s-epoch as shallow/non-shallow was evaluated in terms of area under the curve, sensitivity and specificity. RESULTS The r was 0.34, 0.36 and 0.37 for peak-to-peak amplitude, area and curve length respectively when the whole population was analyzed. Within patients the median r was 0.40, 0.43 and 0.47, respectively. The analysis of the series of compressions yielded a median r of 0.81 between Dmax and the peak-to-peak amplitude, but it decreased to 0.47 when all the series were considered jointly. The classifier based on the TI features showed 90.0%/37.1% and 86.2%/43.5% sensitivity/specificity values, and an area under the curve of 0.75 and 0.71 for the training and test set respectively. CONCLUSION Low linearity between CD and TI was noted in OHCA episodes involving multiple rescuers. Our findings suggest that TI is unreliable as a predictor of Dmax and inaccurate in detecting shallow compressions.

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.

A New Method for Feedback on the Quality of Chest Compressions during Cardiopulmonary Resuscitation

Quality of cardiopulmonary resuscitation (CPR) improves through the use of CPR feedback devices. Most feedback devices integrate the acceleration twice to estimate compression depth. However, they use additional sensors or processing techniques to compensate for large displacement drifts caused by integration. This study introduces an accelerometer-based method that avoids integration by using spectral techniques on short duration acceleration intervals. We used a manikin placed on a hard surface, a sternal triaxial accelerometer, and a photoelectric distance sensor (gold standard). Twenty volunteers provided 60 s of continuous compressions to test various rates (80–140 min−1), depths (3–5 cm), and accelerometer misalignment conditions. A total of 320 records with 35312 compressions were analysed. The global root-mean-square errors in rate and depth were below 1.5 min−1 and 2 mm for analysis intervals between 2 and 5 s. For 3 s analysis intervals the 95% levels of agreement between the method and the gold standard were within −1.64–1.67 min−1 and −1.69–1.72 mm, respectively. Accurate feedback on chest compression rate and depth is feasible applying spectral techniques to the acceleration. The method avoids additional techniques to compensate for the integration displacement drift, improving accuracy, and simplifying current accelerometer-based devices.

Influence of chest compression artefact on capnogram-based ventilation detection during out-of-hospital cardiopulmonary resuscitation

BACKGROUND Capnography has been proposed as a method for monitoring the ventilation rate during cardiopulmonary resuscitation (CPR). A high incidence (above 70%) of capnograms distorted by chest compression induced oscillations has been previously reported in out-of-hospital (OOH) CPR. The aim of the study was to better characterize the chest compression artefact and to evaluate its influence on the performance of a capnogram-based ventilation detector during OOH CPR. METHODS Data from the MRx monitor-defibrillator were extracted from OOH cardiac arrest episodes. For each episode, presence of chest compression artefact was annotated in the capnogram. Concurrent compression depth and transthoracic impedance signals were used to identify chest compressions and to annotate ventilations, respectively. We designed a capnogram-based ventilation detection algorithm and tested its performance with clean and distorted episodes. RESULTS Data were collected from 232 episodes comprising 52 654 ventilations, with a mean (±SD) of 227 (±118) per episode. Overall, 42% of the capnograms were distorted. Presence of chest compression artefact degraded algorithm performance in terms of ventilation detection, estimation of ventilation rate, and the ability to detect hyperventilation. CONCLUSION Capnogram-based ventilation detection during CPR using our algorithm was compromised by the presence of chest compression artefact. In particular, artefact spanning from the plateau to the baseline strongly degraded ventilation detection, and caused a high number of false hyperventilation alarms. Further research is needed to reduce the impact of chest compression artefact on capnographic ventilation monitoring.

Direct evaluation of the effect of filtering the chest compression artifacts on the uninterrupted cardiopulmonary resuscitation time

OBJECTIVES Filtering the cardiopulmonary resuscitation (CPR) artifact has been a major approach to minimizing interruptions to CPR for rhythm analysis. However, the effects of these filters on interruptions to CPR have not been evaluated. This study presents the first methodology for directly quantifying the effects of filtering on the uninterrupted CPR time. METHODS A total of 241 shockable and 634 nonshockable out-of-hospital cardiac arrest records (median duration, 150 seconds) from 248 patients were analyzed. Filtering and rhythm analysis were commenced after 1 minute of CPR, and the end point for CPR was established at the time of the first shock diagnosis. Kaplan-Meier curves were used to compute the probability of interrupting CPR as a function of time. The probabilities of delivering 2 minutes of uninterrupted CPR for the shockable and nonshockable rhythms were compared with the 2-minute cycles of uninterrupted CPR recommended by the guidelines. RESULTS For the nonshockable rhythms, the probabilities of delivering at least 2 and 3 minutes of uninterrupted CPR were 58% (95% confidence interval, 54%-62%) and 48% (44%-52%), respectively. These are the probabilities of reducing and substantially reducing the frequency of CPR interruptions for rhythm analysis. For the shockable rhythms, the probability of avoiding unnecessary CPR prolongation beyond 2 minutes was 100% (99%-100%). CONCLUSIONS Filtering reduces the frequency of CPR interruptions for rhythm analysis in less than 60% of nonshockable rhythms. New strategies to increase the probability of prolonging CPR for nonshockable rhythms should be defined and evaluated using the methodology proposed in this study.

Chest compression rate feedback based on transthoracic impedance

BACKGROUND Quality of cardiopulmonary resuscitation (CPR) is an important determinant of survival from cardiac arrest. The use of feedback devices is encouraged by current resuscitation guidelines as it helps rescuers to improve quality of CPR performance. AIM To determine the feasibility of a generic algorithm for feedback related to chest compression (CC) rate using the transthoracic impedance (TTI) signal recorded through the defibrillation pads. METHODS We analysed 180 episodes collected equally from three different emergency services, each one using a unique defibrillator model. The new algorithm computed the CC-rate every 2s by analysing the TTI signal in the frequency domain. The obtained CC-rate values were compared with the gold standard, computed using the compression force or the ECG and TTI signals when the force was not recorded. The accuracy of the CC-rate, the proportion of alarms of inadequate CC-rate, chest compression fraction (CCF) and the mean CC-rate per episode were calculated. RESULTS Intervals with CCs were detected with a mean sensitivity and a mean positive predictive value per episode of 96.3% and 97.0%, respectively. Estimated CC-rate had an error below 10% in 95.8% of the time. Mean percentage of accurate alarms per episode was 98.2%. No statistical differences were found between the gold standard and the estimated values for any of the computed metrics. CONCLUSION We developed an accurate algorithm to calculate and provide feedback on CC-rate using the TTI signal. This could be integrated into automated external defibrillators and help improve the quality of CPR in basic-life-support settings.