Unai Irusta

A Least Mean-Square Filter for the Estimation of the Cardiopulmonary Resuscitation Artifact Based on the Frequency of the Compressions

Cardiopulmonary resuscitation (CPR) artifacts caused by chest compressions and ventilations interfere with the rhythm diagnosis of automated external defibrillators (AED). CPR must be interrupted for a reliable diagnosis. However, pauses in chest compressions compromise the defibrillation success rate and reduce perfusion of vital organs. The removal of the CPR artifacts would enable compressions to continue during AED rhythm analysis, thereby increasing the likelihood of resuscitation success. We have estimated the CPR artifact using only the frequency of the compressions as additional information to model it. Our model of the artifact is adaptively estimated using a least mean-square (LMS) filter. It was tested on 89 shockable and 292 nonshockable ECG samples from real out-of-hospital sudden cardiac arrest episodes. We evaluated the results using the shock advice algorithm of a commercial AED. The sensitivity and specificity were above 95% and 85%, respectively, for a wide range of working conditions of the LMS filter. Our results show that the CPR artifact can be accurately modeled using only the frequency of the compressions. These can be easily registered after small changes in the hardware of the CPR compression pads.

Suppression of the cardiopulmonary resuscitation artefacts using the instantaneous chest compression rate extracted from the thoracic impedance

AIM To demonstrate that the instantaneous chest compression rate can be accurately estimated from the transthoracic impedance (TTI), and that this estimated rate can be used in a method to suppress cardiopulmonary resuscitation (CPR) artefacts. METHODS A database of 372 records, 87 shockable and 285 non-shockable, from out-of-hospital cardiac arrest episodes, corrupted by CPR artefacts, was analysed. Each record contained the ECG and TTI obtained from the defibrillation pads and the compression depth (CD) obtained from a sternal CPR pad. The chest compression rates estimated using TTI and CD were compared. The CPR artefacts were then filtered using the instantaneous chest compression rates estimated from the TTI or CD signals. The filtering results were assessed in terms of the sensitivity and specificity of the shock advice algorithm of a commercial automated external defibrillator. RESULTS The correlation between the mean chest compression rates estimated using TTI or CD was r=0.98 (95% confidence interval, 0.97-0.98). The sensitivity and specificity after filtering using CD were 95.4% (88.4-98.6%) and 87.0% (82.6-90.5%), respectively. The sensitivity and specificity after filtering using TTI were 95.4% (88.4-98.6%) and 86.3% (81.8-89.9%), respectively. CONCLUSIONS The instantaneous chest compression rate can be accurately estimated from TTI. The sensitivity and specificity after filtering are similar to those obtained using the CD signal. Our CPR suppression method based exclusively on signals acquired through the defibrillation pads is as accurate as methods based on signals obtained from CPR feedback devices.

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.

Automatic detection of chest compressions for the assessment of CPR-quality parameters

AIM Accurate chest compression detection is key to evaluate cardiopulmonary resuscitation (CPR) quality. Two automatic compression detectors were developed, for the compression depth (CD), and for the thoracic impedance (TI). The objective was to evaluate their accuracy for compression detection and for CPR quality assessment. METHODS Compressions were manually annotated using the force and ECG in 38 out-of-hospital resuscitation episodes, comprising 869 min and 67,402 compressions. Compressions were detected using a negative peak detector for the CD. For the TI, an adaptive peak detector based on the amplitude and duration of TI fluctuations was used. Chest compression rate (CC-rate) and chest compression fraction (CCF) were calculated for the episodes and for every minute within each episode. CC-rate for rescuer feedback was calculated every 8 consecutive compressions. RESULTS The sensitivity and positive predictive value were 98.4% and 99.8% using CD, and 94.2% and 97.4% using TI. The mean CCF and CC-rate obtained from both detectors showed no significant differences with those obtained from the annotations (P>0.6). The Bland-Altman analysis showed acceptable 95% limits of agreement between the annotations and the detectors for the per-minute CCF, per-minute CC-rate, and CC-rate for feedback. For the detector based on TI, only 3.7% of CC-rate feedbacks had an error larger than 5%. CONCLUSION Automatic compression detectors based on the CD and TI signals are very accurate. In most cases, episode review could safely rely on these detectors without resorting to manual review. Automatic feedback on rate can be accurately done using the impedance channel.

ECG spectral and morphological parameters reviewed and updated to detect adult and paediatric life-threatening arrhythmia

Since the International Liaison Committee on Resuscitation approved the use of automated external defibrillators (AEDs) in children, efforts have been made to adapt AED algorithms designed for adult patients to detect paediatric ventricular arrhythmias accurately. In this study, we assess the performance of two spectral (A(2) and VFleak) and two morphological parameters (TCI and CM) for the detection of lethal ventricular arrhythmias using an American Heart Association (AHA) compliant database that includes adult and paediatric arrhythmias. Our objective was to evaluate how those parameters can be optimally adjusted to discriminate shockable from nonshockable rhythms in adult and paediatric patients. A total of 1473 records were analysed: 751 from 387 paediatric patients (<or=16 years of age) and 722 records from 381 adult patients. The spectral parameters showed no significant differences (p > 0.01) between the adult and paediatric patients for the shockable records; the differences for nonshockable records however were significant. Still, these parameters maintained the discrimination power when paediatric rhythms were included. A single threshold could be adjusted to obtain sensitivities and specificities above the AHA goals for the complete database. The sensitivities for ventricular fibrillation (VF) and ventricular tachycardia (VT) were 91.1% and 96.6% for VFleak, and 90.3% and 99.3% for A(2). The specificities for normal sinus rhythm (NSR) and other nonshockable rhythms were 99.5% and 96.3% for VFleak, and 99.0% and 97.7% for A(2). On the other hand, the morphological parameters showed significant differences between the adult and paediatric patients, particularly for the nonshockable records, because of the faster heart rates of the paediatric rhythms. Their performance clearly degraded with paediatric rhythms. Using a single threshold, the sensitivities and specificities were below the AHA goals, particularly VT sensitivity (60.4% for TCI and 65.8% for CM) and the specificity for other nonshockable rhythms (51.7% for TCI and 34.5% for CM). The specificities, particularly for the adult case, improve when the thresholds are independently adjusted for each adult and paediatric database.

A simple effective filtering method for removing CPR caused artefacts from surface ECG signals

Cardiopulmonary Resuscitation (CPR) introduces strong interference on the surface ECG, corrupting any diagnosis of the cardiac rhythm. In this work a filtering method is proposed to remove the CPR artefact from Ventricular Fibrillation (VF) signals. It is a notch filter adapted to the fundamental frequency of the CPR artefact. It is designed using the mean time interval between compressions characteristic of each CPR artefact. The method has been tested with a set of 17 artefact signals and 191 VF signals, recorded in real out-of-hospital assistances. For the different corruption levels tested (i.e. SNR of -10, -6, -3, 0, 3 and 3 dB), the mean SNR values restored are: -3.16, 0.39, 2.77, 4.79 and 6.38 dB. The positive effect of the filtering on the rhythm classification algorithm of an AED has been evaluated. The sensitivities values after filtering are: 52.26%, 79.3%, 90.48%, 94.91% and 96.64% respectively

A Reliable Method for Rhythm Analysis during Cardiopulmonary Resuscitation

Interruptions in cardiopulmonary resuscitation (CPR) compromise defibrillation success. However, CPR must be interrupted to analyze the rhythm because although current methods for rhythm analysis during CPR have high sensitivity for shockable rhythms, the specificity for nonshockable rhythms is still too low. This paper introduces a new approach to rhythm analysis during CPR that combines two strategies: a state-of-the-art CPR artifact suppression filter and a shock advice algorithm (SAA) designed to optimally classify the filtered signal. Emphasis is on designing an algorithm with high specificity. The SAA includes a detector for low electrical activity rhythms to increase the specificity, and a shock/no-shock decision algorithm based on a support vector machine classifier using slope and frequency features. For this study, 1185 shockable and 6482 nonshockable 9-s segments corrupted by CPR artifacts were obtained from 247 patients suffering out-of-hospital cardiac arrest. The segments were split into a training and a test set. For the test set, the sensitivity and specificity for rhythm analysis during CPR were 91.0% and 96.6%, respectively. This new approach shows an important increase in specificity without compromising the sensitivity when compared to previous studies.

Feasibility of automated rhythm assessment in chest compression pauses during cardiopulmonary resuscitation

AIM To demonstrate the feasibility of doing a reliable rhythm analysis in the chest compression pauses (e.g. pauses for two ventilations) during cardiopulmonary resuscitation (CPR). METHODS We extracted 110 shockable and 466 nonshockable segments from 235 out-of-hospital cardiac arrest episodes. Pauses in chest compressions were already annotated in the episodes. We classified pauses as ventilation or non-ventilation pause using the transthoracic impedance. A high-temporal resolution shock advice algorithm (SAA) that gives a shock/no-shock decision in 3s was launched once for every pause longer than 3s. The sensitivity and specificity of the SAA for the analyses during the pauses were computed. RESULTS We identified 4476 pauses, 3263 were ventilation pauses and 2183 had two ventilations. The median of the mean duration per segment of all pauses and of pauses with two ventilations were 6.1s (4.9-7.5s) and 5.1s (4.2-6.4s), respectively. A total of 91.8% of the pauses and 95.3% of the pauses with two ventilations were long enough to launch the SAA. The overall sensitivity and specificity were 95.8% (90% low one-sided CI, 94.3%) and 96.8% (CI, 96.2%), respectively. There were no significant differences between the sensitivities (P=0.84) and the specificities (P=0.18) for the ventilation and the non-ventilation pauses. CONCLUSION Chest compression pauses are frequent and of sufficient duration to launch a high-temporal resolution SAA. During these pauses rhythm analysis was reliable. Pre-shock pauses could be minimised by analysing the rhythm during ventilation pauses when CPR is delivered at 30:2 compression:ventilation ratio.