Reduction of CPR artifacts in the ventricular fibrillation ECG by coherent line removal
 Anton Amann^{1}Email author,
 Andreas Klotz^{2},
 Thomas Niederklapfer^{1},
 Alexander Kupferthaler^{1},
 Tobias Werther^{2},
 Marcus Granegger^{2},
 Wolfgang Lederer^{1},
 Michael Baubin^{1} and
 Werner Lingnau^{1}Email author
https://doi.org/10.1186/1475925X92
© Amann et al; licensee BioMed Central Ltd. 2010
Received: 24 July 2009
Accepted: 6 January 2010
Published: 6 January 2010
Abstract
Background
Interruption of cardiopulmonary resuscitation (CPR) impairs the perfusion of the fibrillating heart, worsening the chance for successful defibrillation. Therefore ECGanalysis during ongoing chest compression could provide a considerable progress in comparison with standard analysis techniques working only during "handsoff" intervals.
Methods
For the reduction of CPRrelated artifacts in ventricular fibrillation ECG we use a localized version of the coherent line removal algorithm developed by Sintes and Schutz. This method can be used for removal of periodic signals with sufficiently coupled harmonics, and can be adapted to specific situations by optimal choice of its parameters (e.g., the number of harmonics considered for analysis and reconstruction). Our testing was done with 14 different human ventricular fibrillation (VF) ECGs, whose fibrillation band lies in a frequency range of [1 Hz, 5 Hz]. The VFECGs were mixed with 12 different ECGCPRartifacts recorded in an animal experiment during asystole. The length of each of the ECGdata was chosen to be 20 sec, and testing was done for all 168 = 14 × 12 pairs of data. VFtoCPR ratio was chosen as 20 dB, 15 dB, 10 dB, 5 dB, 0 dB, 5 dB and 10 dB. Here 20 dB corresponds to the highest level of CPRartifacts.
Results
For nonoptimized coherent line removal based on signals with a VFtoCPR ratio of 20 dB, 15 dB, 10 dB, 5 dB and 0 dB, the signaltonoise gains (SNRgains) were 9.3 ± 2.4 dB, 9.4 ± 2.4 dB, 9.5 ± 2.5 dB, 9.3 ± 2.5 dB and 8.0 ± 2.7 (mean ± std, n = 168), respectively. Characteristically, an original VFtoCPR ratio of 10 dB, corresponds to a variance ratio var(VF):var(CPR) = 1:10. An improvement by 9.5 dB results in a restored VFtoCPR ratio of 0.5 dB, corresponding to a variance ratio var(VF):var(CPR) = 1:1.1, the variance of the CPR in the signal being reduced by a factor of 8.9.
Discussion
The localized coherent line removal algorithm uses the information of a single ECG channel. In contrast to multichannel algorithms, no additional information such as thorax impedance, blood pressure, or pressure exerted on the sternum during CPR is required. Predictors of defibrillation success such as mean and median frequency of VFECGs containing CPRartifacts are prone to being governed by the harmonics of the artifacts. Reduction of CPRartifacts is therefore necessary for determining reliable values for estimators of defibrillation success.
Conclusions
The localized coherent line removal algorithm reduces CPRartifacts in VFECG, but does not eliminate them. Our SNRimprovements are in the same range as offered by multichannel methods of Rheinberger et al., Husoy et al. and Aase et al. The latter two authors dealt with different ventricular rhythms (VF and VT), whereas here we dealt with VF, only. Additional developments are necessary before the algorithm can be tested in real CPR situations.
Background
Frequent interruptions of chest compressions (CC) as part of cardiopulmonary resuscitation (CPR) during ventricular fibrillation (VF) and pulseless ventricular tachycardia (VT) impair myocardial perfusion and worsen the chance for successful defibrillation with stable return of spontaneous circulation [1, 2]. Eilevstjonn et al. reported that "noflow times" (NFT) comprise about 50% of time during resuscitation [3], and gave suggestions on how to reduce NFT. On the other hand, analysis of the ECG for fibrillation detection necessarily requires interruption of CC, at least with the ECGanalysis algorithms currently implemented in defibrillators available on the market.
Therefore ECGanalysis during ongoing chest compression can provide a considerable progress in comparison with standard analysis techniques working only during "handsoff" intervals [4]. These intervals have recently been found to be unexpectedly long and harmful [1, 5, 6]. In addition to avoiding "handsoff" times, new analysis techniques could become the prerequisite for prediction of defibrillation success probability [7–18]. These analysis techniques would allow to avoid unpromising and therefore ultimately damaging defibrillator shocks.
Aase, Husoy, Eilevstjonn et al. [19–22] have developed adaptive filtering approaches to realtime separation of VF/VT and CPR for a multichannelcontext. Berger et al. suggested an adaptive noise cancellation technique [23] and recently Kalman filtering techniques were used [4, 24]. We note also the contributions by Aramendi et al. [25] and Irusta et al. [26]. In the present work, we concentrate again on timefrequency methods [27, 28] and on the situation where only one ECG channel is available, without any additional information concerning blood pressure or concerning the pressure on the sternum applied during resuscitation. This is particularly adapted to the current use of automated external defibrillators (AEDs), where  so far  no such additional information is available.
In this work we present a method based on a timefrequency analysis. The method makes use of a windowed Fourier transform that captures characteristic features of VF signals and CPR artifacts. A commonly used criterion to assess an algorithm is the improvement of the signaltonoise ratio (SNR). The SNR is expressed as the variance of the "proper" VFECG without CPRartifacts (signal) divided by the variance of the CPRartifacts (noise) in the ECG. Coherent line removal can be used for reduction of CPRrelated artifacts in VF signals, because the fibrillation ECG does not contain a line spectrum (at some particular frequency) which would unintentionally be removed by the algorithm, but many different frequencies which are continuously distributed in a "fibrillation (frequency) band".
Mere improvement of signaltonoise ratio is not a guarantee for a better estimate of the "proper" VFECG. It is of additional importance, that typical ECGbased parameters like the median frequency (of the ECG) show similar results in estimate_{VF}(t) as compared to ECG_{VF}(t). Furthermore, it is important that an artifactfree ECGsignal (containing VF only) shows approximately the same median frequency before and after application of the CPRfiltering algorithm. Median frequency is considered to be an interesting parameter for prediction of defibrillation success [8, 10, 29], even though not unequivocally [30, 31].
In the present work, we illustrate by examples how the proposed method affects the median frequency and present numerical results for the SNRimprovement.
Methods
(A) Data
We used one exemplary dataset from a VFexperiment in a pig model for illustration of the effect of our CPRreduction algorithm on the power spectrum. Data in this animal experiment were recorded with 12 bit and 1000 Hz sampling frequency. For the present purpose, we used a downsampled version of the ECG recorded with 200 Hz sampling frequency.
The actual testing of our CPRreduction algorithm was done with 14 different human ventricular fibrillation (VF) ECGs, which were mutually mixed with 12 different ECGCPRartifacts recorded in an animal experiment during asystole with an applied CPRfrequency between 80/min and 120/min. The length of each of the ECGdata was chosen to be 20 sec, and testing was done for all 168 = 14 × 12 pairs of data.
The 14 different human ECGs have been collected using a Welch Allyn PIC 50 defibrillator, recorded with 12 bit and 375 Hz sampling frequency during real outofhospital CPR situations. For the present purpose we chose human ECGs with frequencies lying (roughly) in the range [1 Hz, 5 Hz].
The pig experiments were conducted according to Utsteinstyle guidelines [32] and approved by the Federal Austrian Animal Experiment Committee. The recording of the human data was approved by the local Ethics Committee of Innsbruck Medical University.
(B) Data processing and quality assessment for CPRreduction algorithms
Data were processed using MATLAB (The Mathworks, Natick (MA), version R2007b). For computation of mean frequency, median frequency and dominant frequency the upper cutoff frequency was 30 Hz. The lower cutoff frequency was 4.33 Hz (for ECGdata including CPRartifacts) and 2.2 Hz (for ECGdata purged from CPRartifacts).
For spectrograms shown in Figures the frequency range has been restricted to [0 Hz, 15 Hz] to improve visibility (the spectrogram above 15 Hz does not contain much interesting information). The "typical frequencies" corresponding to ventricular fibrillation ECG are called the "fibrillation band". This "fibrillation band" changes in the course of time. For human VFECGs, the fibrillation band typically is within the frequency window [1 Hz, 6 Hz].
where var(ECG_{VF}) is the variance of the proper VFECG and var(ECG_{CPR}) is the variance of the CPRrelated artifacts in the ECG. The acronym SNR refers to "signaltonoise ratio", where the signal is ECG_{VF} and the noise is ECG_{CPR}. Usually it is expressed in decibel units (dB), i.e., as 10 times the logarithm (with basis 10) of this SNR. For very strong artifacts, the signaltonoise ratio is around 20 dB, whereas 0 dB corresponds to rather small CPRrelated artifacts.
In particular, we use the SNRgain, i.e., the difference (rSNR  SNR).
(C) Coherent line removal
Coherent line removal has a few parameters which can be adapted to the signal in question:

the time window,

the number har of harmonics (default = 4),

the frequency width delta for line removal (default = 4),

and the number of harmonics NumHar considered for reconstruction (default = 10),
We choose a time window of 2048 data points, corresponding to 10.24 sec (at a sampling frequency of 200 Hz), and determined the optimal choice of har, delta and NumHar in a grid search, varying har and NumHar from 2 to 10, and delta from 2 to 5 (or took default values). The "grid search" simply computes the variance of the error var(ECG_{CPR}  estimate_{CPR}) and looks for the smallest error (among all different sets of parameters) for our 168 = 14 × 12 datasets. Minimization of var(ECG_{CPR}  estimate_{CPR}) is equivalent to maximum SNR improvement.
Results
In Fig 4, the median VFECG frequency is shown for the original ECG as compared with the median frequency of the VFECG purged from CPRrelated artifacts by coherent line removal. Before start of CPR, these two median frequencies coincide almost perfectly which indicates that coherent line removal does not distort VFECG when the latter does not contain CPRrelated artifacts. After start of CPR, the two median frequencies are rather different and visual inspection shows that the median frequency of the original VFECG (containing CPRrelated artifacts) is governed by the harmonics of the CPRartifacts. In the present example, the second harmonic at 5.5 Hz is particularly dominating. The median frequency of the VFECG purged from CPRartifacts by coherent line removal, is not governed by the harmonics of CPRrelated artifacts, but by the "fibrillation band" which reflects the ventricular fibrillation of the heart of the animal investigated.
SNRgain by coherent line removal
VRtoCPR ratio [dB]  snrgain (optimized parameters) n = 168  snrgain (fixed parameters) n = 168 

20  10.3 ± 2.0  9.3 ± 2.4 
15  10.5 ± 2.2  9.4 ± 2.4 
10  10.5 ± 2.6  9.5 ± 2.5 
5  10.2 ± 2.8  9.3 ± 2.5 
0  8.9 ± 2.9  8.0 ± 2.7 
5  6.3 ± 3.7  4.9 ± 3.7 
10  1.7 ± 4.0  1.0 ± 4.0 
In this particular example, the human ECG mixed with CPRECG had a "fibrillation band" in the frequency range [1 Hz, 5 Hz], which is expected to be difficult to separate from CPRrelated artifacts with ~1.8 Hz and harmonics at ~3.7 Hz, ~5.5 Hz etc.
Table 1 refers to 14 different human outofhospital VFECG datasets, mutually mixed with 12 different CPRECGs, but now using different choices of VFtoCPR ratios of 20 dB, 15 dB, 10 dB, 5 dB, 0 dB, 5 dB, and 10 dB. The optimal SNRgains (for optimized parameters of the coherent line removal algorithm) were 10.3 ± 2.0 dB, 10.5 ± 2.2 dB, 10.5 ± 2.6 dB, 10.2 ± 2.8 dB, 8.9 ± 2.9 dB, 6.3 ± 3.7 dB, and 1.7 ± 4.0 dB (mean ± std, n = 168) in the example used. Optimization by a grid search was done with respect to the parameters which can be chosen for implementation of the coherent line removal algorithm (such as the number of harmonics used for the estimation of the CPRrelated artifacts in the ECG). If the algorithm is not optimized, but used with the fixed default values of the parameters, the SNRgains are 9.3 ± 2.4 dB, 9.4 ± 2.4 dB, 9.5 ± 2.5 dB, 9.3 ± 2.5 dB, 8.0 ± 2.7 dB, 4.9 ± 3.7 dB, and 1.0 ± 4.0 dB.
Discussion
An important point in the development of CPRartifact reduction algorithms is the use of human VFECG data with typical frequencies lying in the same range as the CPRartifacts themselves. This is not the case in animal experimental data: there often the frequencies of ventricular fibrillation are much higher than the frequencies of CPRartifacts, simplifying the separation of VFECG and CPRartifacts. Here, we deliberately took human outofhospital VFECG data with a frequency range of about [1 Hz, 5 Hz], i.e., much lower than in animal data. This frequency range matches well with the frequency range of cardiopulmonary resuscitation of [1.3 Hz, 2 Hz] (= [80/min, 120/min]) and its harmonics. These are compression frequencies near those given in the present resuscitation guidelines (100/min). The animal VFECG as used in Figs 1, 2, 3 and 4 served merely the purpose to clearly illustrate the strategy of the coherent line removal method.
The VFtoCPR ratio (expressed in dB) is negative for very strong CPRrelated artifacts and positive, if the CPRrelated artifacts are very weak. In case of typical VFtoCPR ratios which range between 15 dB to 5 dB, the coherent line removal algorithm achieves an SNRimprovement of ~9.5.
A typical example would be an original VFtoCPR ratio of 15 dB, corresponding to a variance ratio var(VF):var(CPR) = 1:31.6. An improvement by 9.5 dB would result in a restored VFtoCPR ratio of 5.5 dB, corresponding to a variance ratio var(VF):var(CPR) = 1:3.5, the variance of the CPR in the signal being reduced by a factor of 8.9.
Another typical example would be an original VFtoCPR ratio of 10 dB, corresponding to a variance ratio var(VF):var(CPR) = 1:10. An improvement by 9.5 dB would result in a restored VFtoCPR ratio of 0.5 dB, corresponding to a variance ratio var(VF):var(CPR) = 1:1.1, the variance of the CPR in the signal being reduced by a factor of 8.9.
Many studies on CPR artifacts removal used SNR improvement to measure the quality of CPR artifact suppression [19, 21, 24, 35–39]. All these studies were based on the additive data model, i.e., adding a pure artifact signal to the artifactfree ECG, that we adopted. The SNR improvement that we achieved on our testing data is close to that obtained in studies that were based on a twochannel setting, i.e., including additional information such as blood pressure [24, 39] and/or compression depth [19, 21]. For instance, at the SNR level of 10 dB, we obtained the average SNR improvement of 10.5 dB which is smaller than 12.4 dB and 11.84 dB obtained in refs [19] and [39], respectively, but superior to 10.2 dB obtained in [24]. None of the previous studies has analyzed the SNR improvement for SNR level smaller than 10 dB, i.e., for very large artifacts. It is interesting that in our study the SNR improvement for the SNR levels 15 dB and 20 dB did not increase further and stayed in the same range as the SNR improvement obtained for 5 dB and 10 dB, see Table 1. In contrast, SNR improvement decreases with increasing SNR levels that exceed 5 dB as it was also reported in the previous studies stated above.
Here we used a time window of about 10 sec for application of the coherent line removal algorithm (i.e., typically 2 timewindows for a dataset of 20 sec length), which is the reason that single strong CPRartifacts (with a duration of ~0.6 sec, as shown in Fig 4 in the time window [21 sec, 22 sec]) were not eliminated.
During performance of CPR, automatic analysis of the ECG is difficult, because the predominant part of the signal consists of CPRartifacts. This is illustrated in Fig 2 by computing the parameters mean frequency, the median frequency and the dominant frequency in the frequency window [4.33 Hz, 30 Hz] [8, 10, 29, 30]. Since this frequency window excludes the CPRartifacts at ~1.8 Hz and its first harmonic ~3.7 Hz, one might be tempted to consider these parameters (such as the median frequency) to be a good indicator of the "fibrillation band" [7, 8, 11, 16, 17, 40–45]. This is not the case. As illustrated in Fig 2, only before start of CPR the mean frequency (blue), median frequency (magenta) and dominant frequency (black) in Fig 2 follow very well the "fibrillation band". After start of CPR, these parameters are much more governed by the CPRartifacts than by the "fibrillation band" itself. The dominant frequency, in particular, coincides with the second harmonic of CPRartifacts at ~5.5 Hz during the time period [~310 sec, 1410 sec]. During this time period the "fibrillation band" changes dramatically. Nevertheless the dominant frequency is entirely independent of these changes in the "fibrillation band". For the mean frequency and the median frequency, the situation is not so pronounced, but still very unsatisfactory. The median frequency, for example, coincides with the second harmonic of CPRartifacts during the time period [~908 sec, ~1410 sec]. We therefore conclude that parameters such as mean, median and dominant frequency are blurred by CPRartifacts and are therefore not particularly adapted to serve as quantitative indicators for the location of the "fibrillation band".
Fig 3 shows the same pig experiment as Fig 2 again, but now the ECG has been purged from CPRartifacts by local coherent line removal. Again the mean frequency (blue), the median frequency (magenta) and the dominant frequency (black) are shown. The dominant frequency still does not seem to be helpful. The mean and median frequency, on the other hand, now follow closely the "fibrillation band" and can therefore be considered as parameters describing the location of the fibrillation band for the time period [~80 sec, ~1100 sec]. Later than 1100 sec the "fibrillation band" in this pig experiment does not seem to exist any more due to asystole of the heart of the animal. The lower panel of Fig 3 shows that the proportion of the power in the frequency band [0.33 Hz, 2 Hz] as compared to the frequency band [0.33 Hz, 30 Hz] is much lower after filtering of CPRartifacts than without it. For a large time period [~80 sec, ~1000 sec] this proportion is only about 10%  20% (instead of 80%  90%).
Fig 4 shows a comparison (again for the same pig experiment) of the median frequencies before and after CPRfiltering. Observe, in particular, that during the time period [~80 sec, ~310 sec], during which CPR is not performed, there is almost a perfect match between the median frequencies computed with/without CPRfiltering. This is to say that the filtering algorithm used (namely localized coherent line removal) does not remove anything from the "fibrillation band". During the time period [~80 sec ~1400 sec] where CPR is performed, the median frequency after CPRfiltering (magenta line in Fig 4) follows much better the "fibrillation band" than the median frequency before CPRfiltering (black line in Fig 4).
Optimization of the parameters of the coherent line removal algorithm do not give much better results than just using default values (see Table 1). We consider this to be an important result, showing that optimization of the coherent line removal algorithm is not important. Also the default values like har and NumHar could be changed as long as they are not obviously too small. All values for har and NumHar larger than our default values would give results rather equal in quality.
When dealing with CPR artefacts of real outofhospital cardiac arrests a wide variety of compressions rates are found. Coherent line removal as presented here depends on constant compression rate within the respective chosen timewindow.
It is common standard that, when introducing a new method, simple data models (such as additive simulations of CPR artefacts) are used for preliminary testing. Methods need to perform adequately on such simplified data before extending testing on a heterogenous dataset. Most of the authors hitherto have used additive models of VF. Also, to the best of our knowledge, the SNR is the basic criterion for classifying the quality of a CPRremoval algorithm. In our recently published paper [46], we investigated the efficiency of various twochannel methods for CPRartefact removal in nonshockable rhythms: for such ECGs, twochannel methods could not reduce CPR artefacts without affecting the rhythm analysis for shock recommendation. For the coherent line removal algorithm the same is true, i.e., the efficiency and quality of CPRartefact removal for nonshockable rhythms is not satisfactory. Possibly a better understanding of the spectral distribution of rhythms other than VF is needed for future adaption of the coherent line algorithm.
From a computational point of view, the coherent line removal algorithm is very fast. It could probably be implemented on a Coldfire processor containing a floating point unit. Such Coldfire processors are commercially available, but only when ordering large amounts (> 10000 units). The coherent line removal algorithm is, in particular, much faster than the Kalman filter algorithm presented in ref [24].
Conclusions
The localized coherent line removal algorithm reduces CPRartifacts in VFECG, but does not eliminate them. It uses the ECGchannel only, without any additional information (like blood pressure). Our SNRimprovements are in the same range as offered by multichannel methods of Rheinberger et al. [24], Husoy et al. [20] and Aase et al. [21]. In refs. [20, 21] the authors dealt with different rhythms (VF and VT) whereas we dealt with VF, exclusively. Additional developments are necessary before the algorithm can be tested in real CPR situations.
Appendix: Short description of the coherent line removal algorithm
with slowly varying amplitude r(t) and frequency f _{0}(t), and with . The number M of considered harmonics is one of the parameters of the coherent line removal algorithm.
In the present application, the coherent part y(t) of the ECGsignal is identified with its CPRartifacts, corresponding to the function estimate_{CPR}(t) in the Methods Section. The frequency f _{0}(t) is the frequency of resuscitation (e.g., 110/min = 1.83 Hz).
We use a localized version of the approach from Ref [33], using timewindows of 10.24 sec length. Localization is necessary, because CPRrelated artifacts change in amplitude and frequency, and may disappear at all when no resuscitation is performed. Localization means that the coefficients α _{ k }depend on the specific timewindow, e.g., [0 sec, 10.24 sec], [10.24 sec, 20.48 sec], [20.48 sec, 30.72 sec] etc. If no resuscitation artifacts are present in a timewindow, then we have α _{ k }= 0 for all harmonics k = 1, 2, ..., M.
From the values of Γ_{ k }one may compute the values for the α _{ k }, namely α _{ k }= (α _{1}Γ_{ k })^{ k }.
which allows to estimate the coherent part of the original signal.
Declarations
Acknowledgements
We thank our colleagues and the Red Cross staff at Innsbruck Emergency Medical Service for the recording of preclinical ECGdata. This work was supported by the Austrian Science Fund (FWF) under grant L288, and by the Oesterreichische Nationalbank (OeNB) under grant Jubilaeumsfondsprojekt No 8665.
Authors’ Affiliations
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