Current automated external defibrillators mandate interruptions of chest compression to avoid the effect of artifacts produced by CPR for reliable rhythm analyses. But even seconds of interruption of chest compression during CPR adversely affects the rate of restoration of spontaneous circulation and survival. Numerous digital signal processing techniques have been developed to remove the artifacts or interpret the corrupted ECG with promising result, but the performance is still inadequate, especially for nonshockable rhythms. In the present study, we suppressed the CPR artifacts with an enhanced adaptive filtering method. The performance of the method was evaluated by comparing the sensitivity and specificity for shockable rhythm detection before and after filtering the CPR corrupted ECG signals. The dataset comprised 283 segments of shockable and 280 segments of nonshockable ECG signals during CPR recorded from 22 adult pigs that experienced prolonged cardiac arrest. For the unfiltered signals, the sensitivity and specificity were 99.3% and 46.8%, respectively. After filtering, a sensitivity of 93.3% and a specificity of 96.0% were achieved. This animal trial demonstrated that the enhanced adaptive filtering method could significantly improve the detection of nonshockable rhythms without compromising the ability to detect a shockable rhythm during uninterrupted CPR.
Early defibrillation is critical for the survival of patient who suffered from cardiac arrest [
If accurate cardiac rhythm analysis can be performed during CPR, these interruptions will be minimized or totally avoided. During the last decade, numerous digital signal processing techniques have been developed to remove the artifacts or interpret CC corrupted ECG during CPR. Sensitivity and specificity are the proportion of correctly identified shockable and nonshockable rhythms, respectively, and are used to evaluate the performance of artifact suppression method. Algorithms removing artifacts using only the ECG signal, including independent component analysis (ICA) [
In the present study, the effects of CC on signal-to-noise ratio (SNR) at different types of underlying rhythms (ventricular fibrillation (VF), pulseless electrical activity (PEA), and asystole (ASY)) were firstly analyzed in an adult porcine model of prolonged cardiac arrest and CPR. An enhanced adaptive filtering method was then developed to suppress the CPR artifact and evaluated by comparing the sensitivity and specificity for shockable rhythm detection before and after filtering.
The experimental data were collected from 22 male adult pigs that experienced prolonged cardiac arrest and CPR. The porcine model has been well established to simulate real out-of-hospital scenarios due to the fact that heart size, blood pressure, and heart rate are similar to those in humans [
The ECG, acceleration, and transthoracic impedance (TTI) waveform were continuously measured and recorded through a data acquisition system supported by Windaq hardware/software (Dataq Instruments Inc., Akron, OH, USA) at a sample rate of 300 Hz. During CC, the acceleration and TTI signals also served as feedback to control the compression rate and depth. The ECG was measured from the output of a commercial defibrillator (M-Series, Zoll medical corporation, Chelmsford, MA, USA) with the use of a hard gel type of adult defibrillation/pacing pads (stat-padz, Zoll Medical Corporation, Chelmsford, MA, USA) that were applied with an anterior to lateral placement. TTI waveform was recorded through a user designed circuit which was parallelly connected with the defibrillator using a sinusoid-wave excitation current of 2 mA and 30 kHz across the defibrillation pads. The acceleration signal was recorded from an accelerometer-based handheld CPR device (CPR-D-padz, Zoll Medical Corporation, Chelmsford, MA, USA) that was placed on the surface of the animal’s chest just above the heart and underneath the rescuer’s hands during CC.
Data were analyzed offline through user designed software using Matlab (The MathWorks, Inc., Natick, MA, USA). ECG, together with acceleration and TTI signals during CPR, was extracted and annotated from the digitalized experimental records. The CD was calculated from the double integration of acceleration signal. Each segment consisted of 4-second corrupted signal and 3-second artifact-free signal, either during ventilation or during rhythm analysis. These segments were then annotated as VF, PEA, or ASY by an experienced emergency medical doctor. As shown in Figure
Segments of ECG and reference signals during cardiopulmonary resuscitation (CPR). (a) Ventricular fibrillation with and without chest compression (CC). (b) Pulseless electrical activity (PEA) without and with CC. (c) Asystole (ASY) with and without CC. TTI: transthoracic impedance.
To investigate the effects of CC on SNRo (before filtering) at different types of underlying rhythms (VF, PEA, and ASY) and performance of the proposed filtering method, we estimate the SNRo and the SNRf (after filtering) of the CPR corrupted ECG based on the contiguous artifact-free signal [
Examples of signal selection for SNR estimation. The CPR corrupted signal was selected either from the latest 3 seconds of chest compression (CC) (a) or 1 second after the beginning of CC (b).
The estimation is based on the hypothesis that time-limited VF and ASY can be considered quasi-stationary signal. On the other hand, since the energy of a normal sinus rhythm depends on the number of QRS complexes appearing within a segment, we therefore exclude the segments that have unequal numbers of QRS complex within the selected artifact-free and corrupted ECG signals when the underlying rhythm is annotated as PEA.
To suppress the CC related artifacts (CC-artifact), an enhanced adaptive filtering method is developed by estimating the proportion of artifact within the CPR corrupted ECG signal. The flowchart of the proposed method is shown in Figure
Flowchart of the enhanced adaptive filtering method.
The corrupted ECG and reference (TTI) signals are firstly preprocessed by a 4th order Butterworth band-pass filter (0.2–45 Hz) to remove offset and high frequency noise. The power spectral density (PSD) of reference and preprocessed ECG signals are then calculated through dividing the square of the amplitude of fast Fourier transform (FFT) by the length of data points. The frequency of CC
The proportion of the artifact power is then compared with a predefined threshold. If the proportion
In this enhanced adaptive filtering method, normalized least mean squares (NLMS) is used to adjust the coefficient matrix of adaptive filter, and the step size is dynamically adjusted by the estimated artifact proportion
After filtering, the proportion of artifact
In order to compare the performance with the traditional fixed coefficient high-pass filter [
To evaluate the performance of the proposed method, the sensitivity and specificity for detecting a shockable rhythm before and after filtering are compared with an established rhythm classification algorithm named phase space reconstruction (RSR) [
The average peak-to-peak amplitude of the filtered signal
A 3-second rectangular window is used to perform PSR, and the value of
The distributions of SNRo of the CPR corrupted ECG signal did not pass the Kolmogorov-Smirnov normality test and were presented as medians (25/75 percentile). The Wilcoxon rank sum test was used for median values comparison. The relationship between SNRo and CD was tested with Pearson correlation coefficients.
The performance of the filtering method was expressed as sensitivity and specificity. Sensitivity and specificity of ECG signals before and after filtering were compared with the classification results of artifact-free ECG signals using Chi-square test. A
The average duration of CPR was
A total of 107 segments of PEA were used for SNR estimation because the numbers of QRS complex within the selected artifact-free and corrupted ECG signals were equal. Table
Estimated signal-to-noise ratio (SNR) for pulseless electrical activity (PEA), ventricular fibrillation (VF), and asystole (ASY) before and after filtering.
Unfiltered | Adaptive filter | High-pass filter | |
---|---|---|---|
Medians (dB) (25/75 percentiles) | |||
VF | −9.3 (−14.9/−3.6)△△ | 0.2 (−5.1/4.5)** | 0.1 (−4.2/0.9)** |
PEA | −6.2 (−9.0/−1.12)△△ | 0.1 (−3.6/3.4)** | −2.0 (−7.4/−0.6)** |
ASY | −21.2 (−24.2/−18.5)△△ | −12.7 (−15.0/−4.4)** | −7.1 (−10.7/−6.3)** |
Range (dB) (min./max.) | |||
VF | −26.1/9.6 | −18.2/20.0 | −19.7/20.4 |
PEA | −16.0/9.9 | −7.6/19.9 | −14.0/14.7 |
ASY | −31.6/−10.0 | −20.6/2.4 | −18.4/1.7 |
The linear regression result between SNRo and CD is shown in Figure
Linear regression results between SNRo and CD for the full database and different types of underlying rhythms (ventricular fibrillation, VF; pulseless electric activity, PEA; asystole, ASY).
Table
Sensitivity and specificity for the artifact-free ECG and CC corrupted signals before and after filtering.
Rhythm | Number | Artifact-free | Unfiltered | Adaptive | High-pass | |
---|---|---|---|---|---|---|
Shockable (sensitivity) | VF | 283 | 99.0% | 99.3% | 93.3%** | 93.0%** |
|
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Nonshockable (specificity) | All | 280 | 98.2%** | 46.8% | 96.0%**## | 80.4%** |
PEA | 208 | 98.6%** | 53.9% | 97.6%**## | 86.3%** | |
ASY | 72 | 97.2%** | 26.4% | 91.7%**## | 63.9%** |
The present study confirmed that the SNRo of CPR corrupted ECG was negatively correlated with CD in a porcine model of prolonged cardiac arrest and CPR. Based on this observation, we developed an enhanced adaptive filtering method to suppress the CC-artifact by estimating the proportion of artifact within the corrupted ECG signal. The experimental results demonstrated that the enhanced adaptive filtering method could effectively reduce the residual component of artifact and improve the SNR of the ECG signal as well as the outcome of specificity.
The CC-artifact was predominant from the electrode-skin interface and generated by the contraction of thoracic muscles with direct impact of the compressions on chest wall [
Compared with the result that was reported by de Gauna et al. [
Based on the findings that SNRo was negatively correlated with CD, we developed an enhanced adaptive filtering method to suppress the CPR artifact by estimating the proportion of artifact with the use of TTI as reference. Compared with the corrupted signal, both traditional fixed coefficient high-pass filter and proposed method could greatly improve the SNR and specificity. But compared with a specificity of 80.4% for high-pass filter, a remarkable improvement was achieved for the proposed method with a value of 96.0%.
The following modification in removing the CPR related artifact might contribute to the improved performance of the proposed method. Firstly, a parameter was introduced to estimate the proportion of artifact from PSD of ECG signal with the use of compression frequency as reference. The proportion of artifact was correlated with the power of artifact and therefore the SNR level. Secondly, the step size of commonly used LMS adaptive filter was dynamically adjusted by referenced TTI signal and the estimated proportion of artifact. This modification provided greater stability and convergence speed compared with traditional LMS based adaptive method which was used by Irusta et al. [
Besides the enhanced adaptive filter, the algorithm used for rhythm classification also contributed to the improved specificity. The parameters were optimized according to clean ECG signals recorded from the animals when SPR was used [
Although the SNR and specificity were greatly improved after filtering, the sensitivity decreased from 99.3% to 93.3%. It is because the enhanced filtering method also suppressed the component of underlying ECG signals while removing the CPR related artifact. As a result, amplitude of fine VF might be reduced to a level that is below the criteria for classification. When the nonshockable rhythms were investigated separately, the specificity for detecting ASY was relatively lower compared with that of PEA and still below the 95% limit recommended by AHA task force on AEDs [
There are limitations that need to be acknowledged and addressed regarding the present study. Firstly, although the SNRo of CPR corrupted ECG was demonstrated to be negatively correlated with CD for the full database, this correlation was only observed in VF when different ECG rhythms were investigated individually. Additionally, the anatomy structure of human chest was different with that of the animals. Therefore the relationship between artifact level and CD in human beings at different underlying rhythms is still needed to be investigated. Secondly, only TTI signal was used as reference in this study; the effects of different reference signals on the performance of the proposed method have not been investigated. Thirdly, although a great improvement in specificity was achieved in this experimental trial, characteristics of ECG waveform, together with the CPR related artifact, may differ from the data that are recorded from patients who experienced out-of-hospital cardiac arrest and CPR. Performance of the proposed method therefore needs further clinical validating studies. Finally, even though the specificity for detecting a nonshockable rhythm was greatly improved and above the 95% limit recommended by the AHA task force on AEDs [
This experimental animal trial demonstrated that the SNRo of ECG signal corrupted by CPR artifact was negatively correlated with CD and the enhanced adaptive filtering method could significantly improve the detection of nonshockable rhythms without compromising the ability to detect a shockable rhythm during uninterrupted CPR.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This study was supported in part by the National Nature Science Foundation of China (NSFC81271656), a foundation from the General Logistics Department of PLA (CWS12J094), and a Foundation for the Author of National Excellent Doctoral Dissertation of China (FANEDD 201060).