An accurate method for real-time chest compression detection from the impedance signal

Extracting physiologic and clinical data from defibrillators for research purposes to improve treatment for patients in cardiac arrest

Background

A defibrillator should be connected to all patients receiving cardiopulmonary resuscitation (CPR) to allow early defibrillation. The defibrillator will collect signal data such as the electrocardiogram (ECG), thoracic impedance and end-tidal CO2, which allows for research on how patients demonstrate different responses to CPR. The aim of this review is to give an overview of methodological challenges and opportunities in using defibrillator data for research.

Methods

The successful collection of defibrillator files has several challenges. There is no scientific standard on how to store such data, which have resulted in several proprietary industrial solutions. The data needs to be exported to a software environment where signal filtering and classifications of ECG rhythms can be performed. This may be automated using different algorithms and artificial intelligence (AI). The patient can be classified being in ventricular fibrillation or -tachycardia, asystole, pulseless electrical activity or having obtained return of spontaneous circulation. How this dynamic response is time-dependent and related to covariates can be handled in several ways. These include Aalen's linear model, Weibull regression and joint models.

Conclusions

The vast amount of signal data from defibrillator represents promising opportunities for the use of AI and statistical analysis to assess patient response to CPR. This may provide an epidemiologic basis to improve resuscitation guidelines and give more individualized care. We suggest that an international working party is initiated to facilitate a discussion on how open formats for defibrillator data can be accomplished, that obligates industrial partners to further develop their current technological solutions.

A method for continuous rhythm classification and early detection of ventricular fibrillation during CPR

AIM

We developed a method which continuously classifies the ECG rhythm during CPR in order to guide clinical care.

METHODS

We conducted a retrospective study of 432 patients treated following out-of-hospital cardiac arrest. Continuous ECG sequences from two-minute CPR cycles were extracted from defibrillator recordings and further divided into five-second clips. We developed an algorithm using wavelet analysis, hidden semi-Markov modeling, and random forest classification. The algorithm classifies individual clips as asystole, organized rhythm, ventricular fibrillation, or Inconclusive, while integrating information from previous clips. Classifications were compared to manual annotations to estimate accuracy in an independent validation dataset. Continuous sequences were classified as shockable, non-shockable, or Inconclusive; classifications were used to compute shock sensitivity and specificity.

RESULTS

Of 432 patient-cases, 290 were used for development and 142 for validation. In the 12,294 validation ECG clips during CPR, accuracies were 0.88 (95% CI 0.85-0.91) for asystole, 0.98 (95% CI 0.98-0.99) for organized rhythm, and 0.97 (95% CI 0.96-0.97) for ventricular fibrillation, with 43% classified as Inconclusive. Of 457 continuous sequences, shock sensitivity was 0.90 (95% CI 0.86, 0.93), shock specificity was 0.98 (95% CI 0.93, 0.99), and 7% were Inconclusive. Median delay to ventricular fibrillation recognition was 10 (IQR 5, 32) seconds.

CONCLUSION

An automated algorithm continuously classified the primary resuscitation rhythms-asystole, organized rhythms, and ventricular fibrillation-with 88-98% accuracy, enabling accurate shock advisory guidance during most two-minute CPR cycles. Additional investigation is required to understand how algorithm implementation could affect rescuer actions and clinical outcomes.

Machine learning and feature engineering for predicting pulse presence during chest compressions

Current resuscitation protocols require pausing chest compressions during cardiopulmonary resuscitation (CPR) to check for a pulse. However, pausing CPR when a patient is pulseless can worsen patient outcomes. Our objective was to design and evaluate an ECG-based algorithm that predicts pulse presence with or without CPR. We evaluated 383 patients being treated for out-of-hospital cardiac arrest with real-time ECG, impedance and audio recordings. Paired ECG segments having an organized rhythm immediately preceding a pulse check (during CPR) and during the pulse check (without CPR) were extracted. Patients were randomly divided into 60% training and 40% test groups. From training data, we developed an algorithm to predict the clinical pulse presence based on the wavelet transform of the bandpass-filtered ECG. Principal component analysis was used to reduce dimensionality, and we then trained a linear discriminant model using three principal component modes as input features. Overall, 38% (351/912) of checks had a spontaneous pulse. AUCs for predicting pulse presence with and without CPR on test data were 0.84 (95% CI (0.80, 0.88)) and 0.89 (95% CI (0.86, 0.92)), respectively. This ECG-based algorithm demonstrates potential to improve resuscitation by predicting the presence of a spontaneous pulse without pausing CPR with moderate accuracy.

Methodology and framework for the analysis of cardiopulmonary resuscitation quality in large and heterogeneous cardiac arrest datasets

BACKGROUND

Out-of-hospital cardiac arrest (OHCA) data debriefing and clinical research often require the retrospective analysis of large datasets containing defibrillator files from different vendors and clinical annotations by the emergency medical services.

AIM

To introduce and evaluate a methodology to automatically extract cardiopulmonary resuscitation (CPR) quality data in a uniform and systematic way from OHCA datasets from multiple heterogeneous sources.

METHODS

A dataset of 2236 OHCA cases from multiple defibrillator models and manufacturers was analyzed. Chest compressions were automatically identified using the thoracic impedance and compression depth signals. Device event time-stamps and clinical annotations were used to set the start and end of the analysis interval, and to identify periods with spontaneous circulation. A manual audit of the automatic annotations was conducted and used as gold standard. Chest compression fraction (CCF), rate (CCR) and interruption ratio were computed as CPR quality variables. The unsigned error between the automated procedure and the gold standard was calculated.

RESULTS

Full-episode median errors below 2% in CCF, 1 min^-1 in CCR, and 1.5% in interruption ratio, were measured for all signals and devices. The proportion of cases with large errors (>10% in CCF and interruption ratio, and >10 min^-1 in CCR) was below 10%. Errors were lower for shorter sub-intervals of interest, like the airway insertion interval.

CONCLUSIONS

An automated methodology was validated to accurately compute CPR metrics in large and heterogeneous OHCA datasets. Automated processing of defibrillator files and the associated clinical annotations enables the aggregation and analysis of CPR data from multiple sources.

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.

Study on the Improvement of Electrical Facility System of Automated External Defibrillators by Real-Time Measurement of Thoracic Impedance

Sudden Cardiac Arrest (SCA) is a serious emergency disease that has increased steadily every year. To this end, an Automated External Defibrillator (AED) is placed in a public place so that even non-professional medical personnel can respond to SCA. However, the thoracic impedance of patients changes due to CardioPulmonary Resuscitation (CPR) and artificial respiration during first aid treatment. In addition, changes in chest statues due to gender, age, and accidents cause changes in thoracic impedance in real time. The change in thoracic impedance caused by this has a negative effect on the intended electrical energy of the automatic heart shocker to the emergency patient. To prove this, we divided it into adult and pediatric modes and experimented with the energy error of the AED according to the same impedance change. When the first peak current was up to 56.4 (A) and at least 8.4 (A) in the adult mode, the first peak current was up to 32.2 (A) and at least 4.8 (A), respectively, when the impedance changed, the error of the current figure occurred. In this paper, the inverse relationship between thoracic impedance and electric shock energy according to the state of the cardiac arrest patient is demonstrated through the results of the experiment, and the need for an electric facility system that can revise for changes in thoracic impedance of the cardiac arrest patient by reflecting them on electric shock energy in real time is presented.

A Method to Detect Presence of Chest Compressions During Resuscitation Using Transthoracic Impedance

OBJECTIVE

Interruptions in chest compressions during treatment of out-of-hospital cardiac arrest are associated with lower likelihood of successful resuscitation. Real-time automated detection of chest compressions may improve CPR administration during resuscitation, and could facilitate application of next-generation ECG algorithms that employ different parameters depending on compression state. In contrast to accelerometer sensors, transthoracic impedance (TTI) is commonly acquired by defibrillators. We sought to develop and evaluate the performance of a TTI-based algorithm to automatically detect chest compressions.

METHODS

Five-second TTI segments were collected from patients with out-of-hospital cardiac arrest treated by one of four defibrillator models. Segments with and without chest compressions were collected prior to each of the first four defibrillation shocks (when available) from each case. Patients were divided randomly into 40% training and 60% validation groups. From the training segments, we identified spectral and time-domain features of the TTI associated with compressions. We used logistic regression to predict compression state from these features. Performance was measured by sensitivity and specificity in the validation set. The relationship between performance and TTI segment length was also evaluated.

RESULTS

The algorithm was trained using 1859 segments from 460 training patients. Validation sensitivity and specificity were >98% using 2727 segments from 691 validation patients. Validation performance was significantly reduced using segments shorter than 3.2 s.

CONCLUSIONS

A novel method can reliably detect the presence of chest compressions using TTI. These results suggest potential to provide real-time feedback in order to improve CPR performance or facilitate next-generation ECG rhythm algorithms during resuscitation.

Transthoracic Impedance Measured with Defibrillator Pads-New Interpretations of Signal Change Induced by Ventilations

Compressions during the insufflation phase of ventilations may cause severe pulmonary injury during cardiopulmonary resuscitation (CPR). Transthoracic impedance (TTI) could be used to evaluate how chest compressions are aligned with ventilations if the insufflation phase could be identified in the TTI waveform without chest compression artifacts. Therefore, the aim of this study was to determine whether and how the insufflation phase could be precisely identified during TTI. We synchronously measured TTI and airway pressure (Paw) in 21 consenting anaesthetised patients, TTI through the defibrillator pads and Paw by connecting the monitor-defibrillator’s pressure-line to the endotracheal tube filter. Volume control mode with seventeen different settings were used (5–10 ventilations/setting): Six volumes (150–800 mL) with 12 min⁻¹ frequency, four frequencies (10, 12, 22 and 30 min⁻¹) with 400 mL volume, and seven inspiratory times (0.5–3.5 s) with 400 mL/10 min⁻¹ volume/frequency. Median time differences (quartile range) between timing of expiration onset in the Paw-line (Paw_EO) and the TTI peak and TTI maximum downslope were measured. TTI peak and Paw_EO time difference was 579 (432–723) ms for 12 min⁻¹, independent of volume, with a negative relation to frequency, and it increased linearly with inspiratory time (slope 0.47, R2 = 0.72). Paw_EO and TTI maximum downslope time difference was between −69 and 84 ms for any ventilation setting (time aligned). It was independent (R2 < 0.01) of volume, frequency and inspiratory time, with global median values of −47 (−153–65) ms, −40 (−168–68) ms and 20 (−93–128) ms, for varying volume, frequency and inspiratory time, respectively. The TTI peak is not aligned with the start of exhalation, but the TTI maximum downslope is. This knowledge could help with identifying the ideal ventilation pattern during CPR.

Short ECG segments predict defibrillation outcome using quantitative waveform measures

AIM

Quantitative waveform measures of the ventricular fibrillation (VF) electrocardiogram (ECG) predict defibrillation outcome. Calculation requires an ECG epoch without chest compression artifact. However, pauses in CPR can adversely affect survival. Thus the potential use of waveform measures is limited by the need to pause CPR. We sought to characterize the relationship between the length of the CPR-free epoch and the ability to predict outcome.

METHODS

We conducted a retrospective investigation using the CPR-free ECG prior to first shock among out-of-hospital VF cardiac arrest patients in a large metropolitan region (n=442). Amplitude Spectrum Area (AMSA) and Median Slope (MS) were calculated using ECG epochs ranging from 5s to 0.2s. The relative ability of the measures to predict return of organized rhythm (ROR) and neurologically-intact survival was evaluated at different epoch lengths by calculating the area under the receiver operating characteristic curve (AUC) using the 5-s epoch as the referent group.

RESULTS

Compared to the 5-s epoch, AMSA performance declined significantly only after reducing epoch length to 0.2s for ROR (AUC 0.77-0.74, p=0.03) and with epochs of ≤0.6s for neurologically-intact survival (AUC 0.72-0.70, p=0.04). MS performance declined significantly with epochs of ≤0.8s for ROR (AUC 0.78-0.77, p=0.04) and with epochs ≤1.6s for neurologically-intact survival (AUC 0.72-0.71, p=0.04).

CONCLUSION

Waveform measures predict defibrillation outcome using very brief ECG epochs, a quality that may enable their use in current resuscitation algorithms designed to limit CPR interruption.