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Reducing CPR artefacts in ventricular fibrillation in vitro
Resuscitation 48 (2001) 279– 291 www.elsevier.com/locate/resuscitation
Reducing CPR artefacts in ventricular fibrillation in vitro Audun Langhelle a,b,*, Trygve Eftestøl c, Helge Myklebust d, Morten Eriksen a, Bjørn Terje Holten c, Petter Andreas Steen e a
Institute for Experimental Medical Research, Ulle6al Uni6ersity Hospital, N-0407 Oslo, Norway b Norwegian Air Ambulance, N-1441 Drøbak, Norway c Sta6anger Uni6ersity College, Department of Electrical and Computer Engineering, N-4091 Sta6anger, Norway d Laerdal Medical AS, N-4002 Sta6anger, Norway e Di6ision of Surgery, Ulle6al Uni6ersity Hospital, N-0407 Oslo, Norway Received 19 May 2000; received in revised form 14 July 2000; accepted 15 July 2000
Abstract CPR creates artefacts on the ECG, and a pause in CPR is therefore mandatory during rhythm analysis. This hands-off interval is harmful to the already marginally circulated tissues during CPR, and if the artefacts could be removed by filtering, the rhythm could be analyzed during ongoing CPR. Fixed coefficient filters used in animals cannot solve this problem in humans, due to overlapping frequency spectra for artefacts and VF signals. In the present study, we established a method for mixing CPR-artefacts (noise) from a pig with human VF (signal) at various signal-to-noise ratios (SNR) from − 10 dB to +10 dB. We then developed a new methodology for removing CPR artefacts by applying a digital adaptive filter, and compared the results with this filter to that of a fixed coefficient filter. The results with the adaptive filter clearly outperformed the fixed coefficient filter for all SNR levels. At an original SNR of 0 dB, the restored SNRs were 9.0 90.7 dB versus 0.9 90.7 dB respectively (PB 0.0001). © 2001 Elsevier Science Ireland Ltd. All rights reserved. Keywords: Artefacts; Adaptive Filtering; Cardiopulmonary Resuscitation; Electrocardiography; Ventricular Fibrillation
Resumo As manobras de reanimac¸a˜o provocam interfereˆncias no ECG obrigando a` sua interrupc¸a˜o para ana`lise do ritmo. Esta paragem e´ prejudicial para a perfusa˜o dos tecidos, ja´ de si marginal durante a reanimac¸a˜o, pelo que se as interfereˆncia pudessem ser eliminadas, por filtragem, o ritmo poderia ser analisado sem interromper a reanimac¸a˜o. Os filtros de coenficiente fixo utilizados nos animais na˜o resolvem este problema nos homens devido a` sobreposic¸a˜o dos espectros de frequeˆncia entre interfereˆncia e sinais de Fibrilhac¸a˜o Ventricular (FV). Neste estudo fizemos a mistura das interfereˆncias da reanimac¸a˜o (ruı`do) obtidas do porco com o sinal de FV obtida no homem (sinal), com diferentes relac¸o˜es sinal/ruı`do: de–10 dB a+10 dB. Depois desenvolvemos um novo me´todo para remover as interfereˆncias da reanimac¸a˜o aplicando um filtro adaptativo digital e compara´mos os resultados deste filtro com os do filtro de coeficiente fixo. Os resultados deste novo filtro foram claramente melhores do que os do coeficiente fixo para todos os nı`veis de relac¸a˜o sinal/ruı`do. Para um sinal/ruı`do original de 0 dB obtivemos com o filtro adaptativo digital uma relac¸a˜o sinal/ruı`do de 9.0 9 0.7 dB versus 0.9 9 0.7 para o filtro de coenficiente fixo (PB 0.0001). © 2001 Elsevier Science Ireland Ltd. Todos os direitos reservados. Pala6ras cha6e: Artefactos; Filtrac˛a˜o; Reanimaça˜o cardiorespirato´ria; Electrocardiografia; Fibrilhaçaˆo Ventricular
1. Introduction * Corresponding author. Tel.: +47-23-016819; Fax: + 47-23016799. E-mail address: [email protected] (A. Langhelle).
Early defibrillation provides the single best option for restoration of spontaneous circulation (ROSC) in patients with ventricular fibrillation
0300-9572/01/$ - see front matter © 2001 Elsevier Science Ireland Ltd. All rights reserved. PII: S 0 3 0 0 - 9 5 7 2 ( 0 0 ) 0 0 2 5 9 - 8
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(VF) [1]. In the current operation of automated external defibrillators, chest compressions have to be discontinued during automated rhythm analysis before the possible delivery of an electric countershock. The lack of cerebral and myocardial perfusion during this period is harmful to the tissues, and the rate of ROSC is reduced with increased duration of this no-flow period in rats [2]. As concluded by Sato et al. [2] automated defibrillators are therefore likely to be maximally effective if they are programmed to secure minimal ‘hands-off’ delay before delivery of the electric countershock. If the artefacts caused by CPR could be eliminated from the ECG, CPR could continue during the rhythm analysis period, and thereby increase the likelihood of success. Furthermore, as the ECG frequency spectra during VF change with the myocardial condition [3,4], the removal or reduction of CPR artefacts would make it possible to assess the CPR effects on the myocardium as indicated by VF changes. To evaluate whether the CPR artefacts could be removed, we first needed to collect information about the nature of the artefacts and then design special filter solutions. We wanted to challenge the filters with artefact signals of different relative strength compared to the VF signals, and be able to evaluate the filtering effects by comparing the filtered VF signal to the identical VF signal without CPR artefacts. To achieve this we could not start with clinical ECGs of VF with CPR artefacts, but first needed to: 1. Collect data necessary to simulate CPR artefacts in human VF. Artefacts from animal ECG during CPR (noise) were mixed with human VF (signal) at various signal-to-noise ratios (SNR). 2. Develop a new methodology for removing these artefacts, assuming that our simulation of CPR artefacts in human VF was valid. 3. Evaluate the potential of such a filtering methodology for restoring the original VF signal from the noisy signal with simulated artefacts. The performance could be measured by the restored SNR and compared to a similar evaluation of a filter with fixed coefficients, which has been used for CPR artefact removal in earlier works [5 – 7].
2. Materials and methods
2.1. Collecting data 2.1.1. Animal preparation The study was approved by the Norwegian Council for Animal Research. Nine healthy pigs (Norwegian landrace, Sus scropha domestica) of either sex (17 –22 kg, aged 6–8 weeks) were fasted overnight with free access to water. The pigs were anaesthetised with ketamine 30 mg kg − 1 and atropine 1 mg i.m. A catheter was placed in an ear vein for infusion of 30 ml kg − 1 h − 1 Ringer acetate throughout the preparation period. The pigs were placed supine with the chest in a U-shaped trough and the limbs secured to prevent lateral displacement of the chest during CPR. A specially constructed pig mask was used for initiation of inhalation anaesthesia with desflurane (Suprane®, Pharmacia and Upjohn, end-tidal 15%), before a tracheotomy was performed. Anaesthesia was maintained with Suprane end-tidal 10% measured in a sidestream by a Datex Capnomac Ultima (Helsinki, Finland). This is the reported MAC level for pig [8], and was individually adjusted if needed. Ventilation was performed with a Siemens Servo Respirator 900 B, with FiO2 of 0.5, a frequency of 16 min − 1 and an initial tidal volume of 15 ml kg − 1 adjusted to maintain endtidal CO2 (ETCO2) at 5.090.5 kPa as measured by the gas monitor. The urine was drained continuously through a cystostoma, and the rectal temperature was kept at 37.5°C90.5°C, using a heating pad. The ECG signals and the artefacts were recorded both from defibrillation pads (Katecho LT3300, USA) attached to the thorax and ECG monitoring electrodes (Medicotest Blue Sensor R-00-S, Denmark) attached firmly to the limbs. Before application, each site was shaved and slightly sanded to obtain optimal electrode contact. The negative defibrillator electrode was positioned midaxillary immediately above the right front leg. The positive electrode was positioned midaxillary just cranial to the left rib arch. The left rear leg monitoring electrode was positive, the right front leg negative, and the right rear leg was connected to ground. All electrodes were secured using fabric tape.
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2.1.2. Machinery The chest was compressed with a modified mechanical chest compression device (Thumper® 1005, Michigan Instruments) controlled by a laptop computer (Compaq Armada 7770DMT) via a data acquisition card (PCMCIA DAQ-Card AI-16XE-50, National Instrument) and software package (LabView 5.0). The compression pad of the piston was placed on the midsternum. The compression-relaxation duty cycle was held constant at 50/50 and the compression depth at 45 mm. Via a specially designed non-commercial link box (Laerdal Medical AS, Stavanger, Norway) the computer also sampled and stored the raw data: The ECG signals from the defibrillator pads and the monitoring electrodes, the signals from the compression force transducer and the compression displacement transducer, and for the last three animals in the study, the thoracic impedance. The specifications are given in Table 1. 2.1.3. Experimental protocol After preparation, baseline ECG measurements were obtained and thereafter continuously recorded throughout the study. Mechanical chest compressions were then initiated for 30 s for the final adjustment of the thumper and for priming of the chest wall compliance. During sinus rhythm, 45 mm standard mechanical chest compressions were commenced at one of three compression rates, 60, 90 or 120 min − 1, picked at random from a list. The randomisation list was Table 1 Specification of the ECG acquisition system
Lead Input impedance (diff) Input bias path Gain Dynamic range input Bandwidth (-3dB) Sampling rate Resolution Linearity
ECG Defib. channel
ECG Monitoring channel
Lead II, no GND 0.536 MV
Lead II w/GND
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written so that the different compression rates were evenly spread between being performed as the first, second or third method. Manual bag valve ventilation with 100% O2 was performed 15 times per minute without interrupting the compression-relaxation cycle. Expired CO2 was continuously measured, and the person ventilating the pig was blinded for the results. After 2 min CPR was stopped for 30 s, enabling ECG recordings with and without CPR. Chest compressions and ventilations were thereafter restarted at one of the other compression rates. After 2 min CPR was again discontinued for 30 s, and then resumed at the last compression rate. The i.v. infusion, the heating, the inhalation anaesthesia and ventilation were thereafter discontinued. Ventricular fibrillation (VF) was induced by a trans-thoracic current (90 V AC) for 3 s and confirmed by characteristic ECG changes. After 3 min of VF, CPR was restarted with the same compression rates and measurements as during sinus rhythm. After completion of all three compression rates during VF, asystole was induced by a bolus of 20 mmol monokaliumphosphate, and the same study protocol as during sinus rhythm and VF was repeated. All CPR was thereafter stopped, and the pigs died. Each pig was autopsied to check for damage to the internal organs.
2.1.4. Animal data After collection, the animal ECG data were down-sampled to 100 Hz sample rate, and stored in one file for each channel and animal. Signals from all the transducers and the impedance system were sampled at 25 Hz and collected in one file for each animal after interpolation to 100 Hz sample rate.
10 GV
From front end 82 961 mV
From GND electrode
0.035–250 Hz 2000 Hz 1.86 mV 93°, 0.15–25 Hz
0.035–240 Hz 2000 Hz 1.52 mV 93°, 0.15–25 Hz
100 950 mV
2.1.5. Human data Twenty-five ECG episodes each of 15 s duration sampled at 100 Hz, 8 bit resolution of ventricular fibrillation without CPR artefacts, were collected from a proprietary ECG rhythm library (Laerdal Medical AS, Stavanger, Norway) annotated by cardiologists. These signals were originally recorded with various models of Heartstart® (Laerdal Medical AS) during out-ofhospital cardiac arrest.
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2.1.6. Frequency spectra of human and pig VF 6s CPR artefacts Frequency spectra were computed from ten 15 s ECG recordings of CPR artefacts (90 compressions min − 1) randomly extracted from our animal experimental data. Likewise, spectra were computed from ten animal VF records and from ten human VF from the rhythm library described above (2.1.5). All the VF spectra were computed from periods when no CPR was performed, thus without artefacts. 2.2. Simulating artefacts in human ECG 2.2.1. Suggested artefact component model We propose an artefact component model where the artefacts are generated from four separate sources: G1, G2, G3 and G4. G1 are signal components originating from the heart due to mechanical stimulation of the heart itself. G2 are signal components generated by mechanical stimulation of thoracic muscles. G3 are signal components originating from the electrodes and caused by electrode tapping or dragging. G4 are signal components caused by static electricity and the following charge equalising currents between the ECG amplifier and the patient. Chest compressions and ventilation may cause each of the generators to contribute, but with different magnitudes. Some artefact reduction can be achieved by measurement methodology, and some by filtering methodology. Applying a third ground electrode, which will carry most of the charge equalising currents, will reduce the effect of G4. Further reduction is possible by balancing the total impedance in each of the measurement leads, primarily by initial preparation of each electrode site. The magnitude of G3 primarily depends on the magnitude of tapping or dragging during CPR, the electrode active area and the materials that forms the electrode and electrolyte and thus the electrode potentials [9]. In our model, we expect that the defibrillator channel will present artefacts composed from all model components, whereas the monitoring channel present artefacts primarily composed of G1 and G2.
2.2.2. Adding animal CPR artefacts to human VF (VFh) To simulate CPR artefacts in human ECG, we added animal artefacts at different signal-to-noise ratios (SNR). In search for a filtering methodology, we decided to use the artefacts in the material that were the most challenging to filter. These occurred during asystole in one animal and included disturbances from both compressions and ventilations. In the whole material artefacts in asystole had more amplitude and frequency components over a broader band than during VF. We also decided to use the signals from the monitoring channel, as they should be dominated by the G1 and G2 components with less components from G3 (electrode drag or tap) and G4 (static electricity). We hypothesise that the G1 and G2 components are correlated to the information from the thumper displacement signal and the thorax impedance signal. Artefacts were added to each of the 25 human ECGs to produce five mixes with the original SNR set to −10, −5, 0, 5 and 10 dB. These signal to noise ratios were selected in order to simulate a wide range of noise conditions. In the development of the methods for artefact reduction we experimented to find good parameter settings for the filters. To limit the scope of this work, we only used one artefact record read from the monitor electrodes with well-defined artefacts caused by both ventilation and compressions at a rate of 90 min − 1. 2.3. Artefact reduction Preliminary experiments with different filter options demonstrated a potential in applying digital adaptive filtering [10]. In contrast to filtering with constant filter coefficients, this method allows for filtering even when the frequency spectra for the artefact and the desired signal overlap. The adaptive filtering technique also provides an opportunity to use the information in the thumper control and impedance measurement signals, thus applying information about G1 and G2 in the artefact model. (For details on the mathematics of artefact addition and reduction see appendix).
2.4. Statistical analysis Data are presented as mean9S.D. For the comparison of the restored SNR levels for the two
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filter types we used Wilcoxon rank-sum test. Statistical significance was considered to be at the PB0.05 level.
3. Results
3.1. Animal data, obser6ations The CPR artefacts appeared different during SR, VF and asystole (Fig. 1). During SR the only observed changes in the ECG with CPR were RR-interval modifications in the QRS-complexes during chest compressions, and some signal excursions possibly due to G3 and G4. During VF and asystole, the magnitude of the artefact increased as the spontaneous electrical activity dropped. Furthermore, we observed that there tended to be differences in artefact magnitudes and shapes between the monitoring channel and the defibrillator channel. This is illustrated in Fig. 2 showing the simultaneous recording via monitoring (a) and defibrillation (b) leads from one of the animals. CPR was performed only during the first 7.5 s
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where there are distinct artefacts present in the defibrillation channel that do not appear in the monitoring channel. The constructed spectrograms are time-frequency transformations of the ECG (Fig. 3). In the two parts of Fig. 3, the spectrograms represent the period after onset of VF until right before asystole is induced in two different animals. In Fig. 3a, the frequency components corresponding to the harmonics of the CPR artefacts given at the three rates, 60, 90 and 120 min − 1, appear as distinct horizontal red lines in the 0–5 Hz frequency band. The frequency components corresponding to VF appear as a time-varying composition of frequency components in the frequency area above 5 Hz. In the pauses between the first two compression periods the direct effect of CPR disappears as indicated by the yellow to green colouring without distinct bands. In some animals spontaneous periodic activity appeared during VF and continued after the end of CPR. This is illustrated in Fig. 3a, where there is evidence of low frequency activity in the 0–5 Hz areas after the last compression period. In Fig. 3b
Fig. 1. Different degree of disturbance in (a) sinus rhythm (SR), (b) ventricular fibrillation (VF) and (c) asystole (A). The first and last three-second periods are with and without ongoing CPR respectively.
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Fig. 2. Example of difference in artefacts presented by (a) monitoring channel and (b) defibrillation pad channel. CPR was ongoing only during the first 7.5 s where there are artefact components in the defibrillation pad channel which are not present in the monitoring channel. This suggests that these artefact signals do not originate from the heart or thoracic muscles (G1 and G2 of the artefact model), but from the defibrillation pads by electrode tapping or dragging (G3) or static electricity followed by charge equilibration (G4).
such spontaneous activity is evident from the onset of VF. The compression periods are less evident than in the corresponding parts of Fig. 3a. The frequency components of the artefacts become less distinct when spontaneous activity is present.
3.2. A6eraged frequency spectra of human VF and pig VF 6ersus CPR artefacts (Fig. 5) The spectral components of the CPR artefacts during asystole were in the frequency area below 5 Hz (Fig. 4a), of animal VF in the area above 5 Hz (Fig. 4b), and of the human VF in the area from 0 to 10 Hz.
in Fig. 5. These were the two channels we later attempted to reconstruct after filtering. In the first attempt we selected thorax impedance (6i (n)) as reference. Results after filtering are shown in Fig. 6. In the next attempt displacement (6d (n)) was the reference (Fig. 7). In the third attempt a mix of thorax impedance and displacement (6d + i (n)) was used (Fig. 8). The best performance was achieved using the latter, clearly demonstrated when comparing Figs. 5 and 8. The reconstructed channels corresponding to the fixed coefficient filter clearly show that this kind of filter fails when challenged with human ECG (Fig. 9).
3.3. Considering 6arious filter types
3.4. Comparing adapti6e filters to fixed coefficient filters
Human VF was mixed with asystole with CPR artefacts from pig no. 7 at a compression rate of 90 min − 1. The original signals in the mix are shown
When the 25 human ECGs, which each mixed to five different original SNR levels, were attempted restored by filtering, the adaptive mixed reference
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Fig. 3. Time frequency representations (spectrograms showing the scaled logarithm of the magnitudes of the time dependent Fourier transform) of the VF period in two animals. The colours identify the magnitudes as follows: low (dark blue), below middle (light blue), above middle (yellow), high (red). The frequency components of CPR artefacts (and spontaneous activity) and VF are identified by high magnitudes (red) in the frequency areas 0 – 5 Hz and 5 – 15 Hz respectively.
filter outperformed the high-pass fixed coefficient filter for all SNR levels (Table 2).
4. Discussion Separating CPR artefacts from the myocardial ECG signal during VF in man is more difficult than the corresponding problem in pigs where it has been successfully achieved by applying digital filters with fixed coefficients [6]. These filters work by suppressing fixed frequencies, and their success thus require that the major frequency components of the CPR artefacts and the signal reflecting the cardiac rhythm are separate. This is the case in pigs with the major artefact components below 5 Hz and the VF band in the area above 5 Hz as
found in the present study (Figs. 3 and 4) and previously reported by Noc et al. [5] and Strohmenger et al. [6]. It is not the case in humans where the frequencies of VF are lower with much more overlap with the artefacts as seen in the present study (Fig. 4) and reported by Strohmenger et al. [7]. Therefore, other techniques have to be applied. To explore the potential of other techniques we developed a model to simulate different CPR artefacts during human VF by adding artefacts from animals to original human ECGs in VF. The intention was to develop a new principle and evaluate it according to signal reconstruction and not by effects on rhythm or VF analysis. This was the reason for using this combination of signals instead of using only human data during CPR.
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Fig. 4. Averaged spectral estimates for (a) CPR-artefact in asystole, (b) Ventricular fibrillation (VF) in animals and (c) VF in humans. The estimates were computed as the mean of the individual estimates for each of ten 15-s ECG records, each normalised to unity variance. The area under the spectrum curve represents the total power of the signal.
Fig. 5. The original signals representing (a) animal artefact and (b) human ventricular fibrillation to be attempted reconstructed after filtering. (a) and (b) illustrates the ideal filter output.
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Fig. 6. Filter solution with impedance as artefact reference. (a) Reconstructed CPR artefact. (b) Reconstructed ventricular fibrillation (VF).
Fig. 7. Filter solution with displacement as artefact reference. (a) Reconstructed CPR artefact. (b) Reconstructed ventricular fibrillation (VF).
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Fig. 8. Adaptive filtered estimated channels with reference mix. (a) Reconstructed CPR artefact. (b) Reconstructed ventricular fibrillation (VF).
Fig. 9. Fast FIR filter with fixed coefficients selected frequency 4.875 Hz removing three lower harmonics of CPR artefacts. (a) Reconstructed CPR artefact. (b) Reconstructed ventricular fibrillation (VF).
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Table 2 Filter performancea Original SNR (dB)
−10.0 −5.0 0.0 5.0 10.0
Restored SNR Fixed coefficient filter
Adaptive filter
P
−0.290.5 0.690.7 0.990.7 1.090.8 1.09 0.8
2.49 0.5 6.29 0.7 9.09 0.7 10.69 0.8 11.39 0.8
B0.0001 B0.0001 B0.0001 B0.0001 B0.0001
a
Twenty-five human VF segments were all mixed with animal CPR artefact in asystole at 90 compressions min−1 via monitoring electrodes to the given original signal-to-noise ratio (SNR) levels. The adaptive filter uses the mixed references of impedance and position while the fixed coefficient filter removes the three lower harmonics of the CPR artefact. The results are mean9 S.D. Test for difference in population by Wilcoxon rank-sum test to compare performance of adaptive filter solution to that of fixed coefficient filter solution.
During clinical, manual CPR we have no control of the artefact production with exact registration of impedance, timing of compressions and ventilation etc. The present was a modelling experiment that also enabled a comparison of the signals after the attempted removal of the artefacts with the original VF signals before the artefacts were added. In this study we have established a method for mixing artefacts from animals (noise) with human VF (signal) at various signal-to-noise ratios. We also managed to develop a new methodology for removing these artefacts by applying a digital adaptive filter where the reference inputs reflect CPR activity. The degree of success depends on the reference signals being used. Our choice was to use both the compression depth signal and the thoracic impedance signal, which together represent signals that are correlated to the artefacts seen during CPR. The filter performed best when the two reference signals were mixed together. A third reference signal representing the charge equalising current determining G4 might improve the performance. A further improvement of the digital adaptive filter design would make it handle a number of independent references separately. The feasibility of this principle was then assessed in a model where human VF was mixed with animal artefacts. The results illustrate that this filtering principle has a potential and outperformed the fixed coefficient high-pass filter under the given conditions. The key issue is that adaptive digital filters can be used on artefacts that overlap the desired signal in frequency.
When CPR modified certain VF characteristics, like amplitude and median frequency, this has been considered the effect of the treatment and not artefacts [3,4,13]. Our artefact model suggests four independent sources of artefact components, but further analysis should be carried out to verify the model and to possibly expand it. Further investigations are also required to evaluate how well VF classifiers used in defibrillators perform during CPR, with and without filtering. To conclude on the feasibility of the principle, human ECGs with corresponding reference signals acquired during CPR are needed. Finally, we will emphasise that the solution in reducing CPR artefacts should always be to first minimise the original artefacts by using electrodes with the lowest electrode potential, proper electrode contact, proper placement, avoid tapping the electrodes during CPR and preferably also by using a third ground electrode. Acknowledgements We would like to thank Michigan Instruments for their contribution with instrumentation for this study. Appendix A We denote the human ECG by xh (n), and the animal artefact to be added by a(n). These two signals are added together x(n) according to x(n) =xh (n)+a(n).
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The original SNR is defined as SNRorg = 10·log10
s2xh , s2a
where s2xh is the variance of the human ECG and s2a is the variance of the added artefact. The artefacts were initially normalised to unit variance and then added to the human VF to a specific SNRorg according to x(n)= xh (n) + C·an (n), where C= s2xh/10SNRorg /10 and an (n) is the normalised artefact. Our main objective was to restore xh (n), the human VF part of the mix, as well as possible by designing a digital filter, h(n) of length N, so that the restored signal, xˆh (n), could be obtained through the filtering procedure N−1
xˆh (n) = % h(n −k)·x(k), k=0
where k is the counting index for the filter coefficients. The adaptive filter method updates h(n) at every sample instant by providing an iterative gradient search approximation for the optimal filter coefficients given by the solution of the Wiener –Hopf equations [11]. The filter illustrated in Fig. 10 seeks to remove the part of x(n) correlated to the reference signal, 6(n). One might consider the removed part, aˆ (n), as an estimate of the artefact. The reference signal, 6(n), is either the signal from the displacement transducer (6d (n)) or the signal from
the impedance system (6i (n)) or a combination of the two (6d + i (n)). We used a Conjugate Gradient (CG) algorithm [12] Adaptive Finite Impulse Response (FIR) filter [11], which in preliminary experiments [10] proved to perform best compared to other gradient search techniques with respect to convergence time and error adaptation. In these experiments, a step size of 0.005, forgetting factor of 0.99999 and a filter length of 9 were found to be reasonable filter parameters. As comparative reference we applied a fixed coefficient high pass filter with cut off frequency, fc, set according to fc =fhi + ( fhi +1−fhi /2), fhi denoting the ith harmonic of an artefact signal. We wanted to remove the three lower harmonics of artefacts corresponding to 90 compressions min − 1, this resulting in fc =5.25 Hz. This cut off frequency corresponds to what Strohmenger et al. found to be necessary to reduce interference from CPR artefacts on VF analysis in man [7]. This filter was designed using the window method for construction of linear phase FIR filters with 30 coefficients [11]. We did some observational trials at filtering one given artefact mix with all these filter types: (a) Adaptive filter with 6d as reference, (b) adaptive filter with 6i as reference, (c) adaptive filter with 6d + i as reference and (d) the high-pass filter with fixed coefficients. In these trials we wanted to examine the behaviour of both the restored signals, xˆh (n) and aˆ (n). The filter quality was measured through SNR of the restored signal. The error signal e(n) is defined as the difference of the original noise-free human ECG to the restored signal e(n)=xh (n)−xˆh (n).
Fig. 10. Adaptive filter. The adaptive filter, h(n), is updated at every sample instant to remove part of the mixed signal, x(n) =xh (n) +a(n), correlated to the reference signal, 6(n). The removed signal, aˆ (n), represents the estimated artefact signal, while the remaining part, xˆh (n), represents the estimate of the human VF.
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We wanted this error to be as small as possible or equivalently obtain a large SNR for the restored signal as given by SNRres = 10·log10
s2xh . s2e
The adaptive filter using the reference signal 6d + i(n) was evaluated with respect to SNRres to be compared to the performance of the high-pass fixed coefficient filter for the signals mixed as described previously original SNR levels −10, − 5, 0, 5 and 10 dB.
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[5] Noc M, Weil MH, Tang W, et al. Electrocardiographic prediction of the success of cardiac resuscitation. Crit Care Med 1999;27:708– 14. [6] Strohmenger HU, Lindner KH, Keller A, Lindner IM, Pfenninger EG. Spectral analysis of ventricular fibrillation and closed-chest cardiopulmonary resuscitation. Resuscitation 1996;33:155– 61. [7] Strohmenger HU, Lindner KH, Brown CG. Analysis of the ventricular fibrillation ECG signal amplitude and frequency parameters as predictors of countershock success in humans. Chest 1997;111:584– 9. [8] Yasuda N, Targ AG, Eger EI, Johnson BH, Weiskopf RB. Pharmacokinetics of desflurane, sevoflurane, isoflurane, and halothane in pigs. Anesth Analg 1990;71:340– 8. [9] Geddes LA, Baker LE. Principles of Applied Biomedical Instrumentation. 3rd ed. New York: John Wiley & Sons, 1989. P. 315– 73. [10] Holten BT. Filtering artefacts in biomedical signals (Thesis in Norwegian). Stavanger, Norway: Stavanger University College, 1999. [11] Proakis JG, Sanders AB. Digital Signal Processing: Principles, Algorithms and Applications. 2nd ed. New York: Macmillan Publishing company, 1992. [12] Golub GH, Van Loan CF. Matrix Computations. 2nd ed. Baltimore: John Hopkins University Press; 1989. [13] Eftestøl T, Sunde K, Aase SO, Husøy JH, Steen PA. Predicting outcome of defibrillation by spectral characterization and non-parametric classification of ventricular fibrillation in out-of-hospital cardiac arrested patients. [In Press] Circulation, 2000.
Reducing CPR artefacts in ventricular fibrillation in vitro Audun Langhelle a,b,*, Trygve Eftestøl c, Helge Myklebust d, Morten Eriksen a, Bjørn Terje Holten c, Petter Andreas Steen e a
Institute for Experimental Medical Research, Ulle6al Uni6ersity Hospital, N-0407 Oslo, Norway b Norwegian Air Ambulance, N-1441 Drøbak, Norway c Sta6anger Uni6ersity College, Department of Electrical and Computer Engineering, N-4091 Sta6anger, Norway d Laerdal Medical AS, N-4002 Sta6anger, Norway e Di6ision of Surgery, Ulle6al Uni6ersity Hospital, N-0407 Oslo, Norway Received 19 May 2000; received in revised form 14 July 2000; accepted 15 July 2000
Abstract CPR creates artefacts on the ECG, and a pause in CPR is therefore mandatory during rhythm analysis. This hands-off interval is harmful to the already marginally circulated tissues during CPR, and if the artefacts could be removed by filtering, the rhythm could be analyzed during ongoing CPR. Fixed coefficient filters used in animals cannot solve this problem in humans, due to overlapping frequency spectra for artefacts and VF signals. In the present study, we established a method for mixing CPR-artefacts (noise) from a pig with human VF (signal) at various signal-to-noise ratios (SNR) from − 10 dB to +10 dB. We then developed a new methodology for removing CPR artefacts by applying a digital adaptive filter, and compared the results with this filter to that of a fixed coefficient filter. The results with the adaptive filter clearly outperformed the fixed coefficient filter for all SNR levels. At an original SNR of 0 dB, the restored SNRs were 9.0 90.7 dB versus 0.9 90.7 dB respectively (PB 0.0001). © 2001 Elsevier Science Ireland Ltd. All rights reserved. Keywords: Artefacts; Adaptive Filtering; Cardiopulmonary Resuscitation; Electrocardiography; Ventricular Fibrillation
Resumo As manobras de reanimac¸a˜o provocam interfereˆncias no ECG obrigando a` sua interrupc¸a˜o para ana`lise do ritmo. Esta paragem e´ prejudicial para a perfusa˜o dos tecidos, ja´ de si marginal durante a reanimac¸a˜o, pelo que se as interfereˆncia pudessem ser eliminadas, por filtragem, o ritmo poderia ser analisado sem interromper a reanimac¸a˜o. Os filtros de coenficiente fixo utilizados nos animais na˜o resolvem este problema nos homens devido a` sobreposic¸a˜o dos espectros de frequeˆncia entre interfereˆncia e sinais de Fibrilhac¸a˜o Ventricular (FV). Neste estudo fizemos a mistura das interfereˆncias da reanimac¸a˜o (ruı`do) obtidas do porco com o sinal de FV obtida no homem (sinal), com diferentes relac¸o˜es sinal/ruı`do: de–10 dB a+10 dB. Depois desenvolvemos um novo me´todo para remover as interfereˆncias da reanimac¸a˜o aplicando um filtro adaptativo digital e compara´mos os resultados deste filtro com os do filtro de coeficiente fixo. Os resultados deste novo filtro foram claramente melhores do que os do coeficiente fixo para todos os nı`veis de relac¸a˜o sinal/ruı`do. Para um sinal/ruı`do original de 0 dB obtivemos com o filtro adaptativo digital uma relac¸a˜o sinal/ruı`do de 9.0 9 0.7 dB versus 0.9 9 0.7 para o filtro de coenficiente fixo (PB 0.0001). © 2001 Elsevier Science Ireland Ltd. Todos os direitos reservados. Pala6ras cha6e: Artefactos; Filtrac˛a˜o; Reanimaça˜o cardiorespirato´ria; Electrocardiografia; Fibrilhaçaˆo Ventricular
1. Introduction * Corresponding author. Tel.: +47-23-016819; Fax: + 47-23016799. E-mail address: [email protected] (A. Langhelle).
Early defibrillation provides the single best option for restoration of spontaneous circulation (ROSC) in patients with ventricular fibrillation
0300-9572/01/$ - see front matter © 2001 Elsevier Science Ireland Ltd. All rights reserved. PII: S 0 3 0 0 - 9 5 7 2 ( 0 0 ) 0 0 2 5 9 - 8
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(VF) [1]. In the current operation of automated external defibrillators, chest compressions have to be discontinued during automated rhythm analysis before the possible delivery of an electric countershock. The lack of cerebral and myocardial perfusion during this period is harmful to the tissues, and the rate of ROSC is reduced with increased duration of this no-flow period in rats [2]. As concluded by Sato et al. [2] automated defibrillators are therefore likely to be maximally effective if they are programmed to secure minimal ‘hands-off’ delay before delivery of the electric countershock. If the artefacts caused by CPR could be eliminated from the ECG, CPR could continue during the rhythm analysis period, and thereby increase the likelihood of success. Furthermore, as the ECG frequency spectra during VF change with the myocardial condition [3,4], the removal or reduction of CPR artefacts would make it possible to assess the CPR effects on the myocardium as indicated by VF changes. To evaluate whether the CPR artefacts could be removed, we first needed to collect information about the nature of the artefacts and then design special filter solutions. We wanted to challenge the filters with artefact signals of different relative strength compared to the VF signals, and be able to evaluate the filtering effects by comparing the filtered VF signal to the identical VF signal without CPR artefacts. To achieve this we could not start with clinical ECGs of VF with CPR artefacts, but first needed to: 1. Collect data necessary to simulate CPR artefacts in human VF. Artefacts from animal ECG during CPR (noise) were mixed with human VF (signal) at various signal-to-noise ratios (SNR). 2. Develop a new methodology for removing these artefacts, assuming that our simulation of CPR artefacts in human VF was valid. 3. Evaluate the potential of such a filtering methodology for restoring the original VF signal from the noisy signal with simulated artefacts. The performance could be measured by the restored SNR and compared to a similar evaluation of a filter with fixed coefficients, which has been used for CPR artefact removal in earlier works [5 – 7].
2. Materials and methods
2.1. Collecting data 2.1.1. Animal preparation The study was approved by the Norwegian Council for Animal Research. Nine healthy pigs (Norwegian landrace, Sus scropha domestica) of either sex (17 –22 kg, aged 6–8 weeks) were fasted overnight with free access to water. The pigs were anaesthetised with ketamine 30 mg kg − 1 and atropine 1 mg i.m. A catheter was placed in an ear vein for infusion of 30 ml kg − 1 h − 1 Ringer acetate throughout the preparation period. The pigs were placed supine with the chest in a U-shaped trough and the limbs secured to prevent lateral displacement of the chest during CPR. A specially constructed pig mask was used for initiation of inhalation anaesthesia with desflurane (Suprane®, Pharmacia and Upjohn, end-tidal 15%), before a tracheotomy was performed. Anaesthesia was maintained with Suprane end-tidal 10% measured in a sidestream by a Datex Capnomac Ultima (Helsinki, Finland). This is the reported MAC level for pig [8], and was individually adjusted if needed. Ventilation was performed with a Siemens Servo Respirator 900 B, with FiO2 of 0.5, a frequency of 16 min − 1 and an initial tidal volume of 15 ml kg − 1 adjusted to maintain endtidal CO2 (ETCO2) at 5.090.5 kPa as measured by the gas monitor. The urine was drained continuously through a cystostoma, and the rectal temperature was kept at 37.5°C90.5°C, using a heating pad. The ECG signals and the artefacts were recorded both from defibrillation pads (Katecho LT3300, USA) attached to the thorax and ECG monitoring electrodes (Medicotest Blue Sensor R-00-S, Denmark) attached firmly to the limbs. Before application, each site was shaved and slightly sanded to obtain optimal electrode contact. The negative defibrillator electrode was positioned midaxillary immediately above the right front leg. The positive electrode was positioned midaxillary just cranial to the left rib arch. The left rear leg monitoring electrode was positive, the right front leg negative, and the right rear leg was connected to ground. All electrodes were secured using fabric tape.
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2.1.2. Machinery The chest was compressed with a modified mechanical chest compression device (Thumper® 1005, Michigan Instruments) controlled by a laptop computer (Compaq Armada 7770DMT) via a data acquisition card (PCMCIA DAQ-Card AI-16XE-50, National Instrument) and software package (LabView 5.0). The compression pad of the piston was placed on the midsternum. The compression-relaxation duty cycle was held constant at 50/50 and the compression depth at 45 mm. Via a specially designed non-commercial link box (Laerdal Medical AS, Stavanger, Norway) the computer also sampled and stored the raw data: The ECG signals from the defibrillator pads and the monitoring electrodes, the signals from the compression force transducer and the compression displacement transducer, and for the last three animals in the study, the thoracic impedance. The specifications are given in Table 1. 2.1.3. Experimental protocol After preparation, baseline ECG measurements were obtained and thereafter continuously recorded throughout the study. Mechanical chest compressions were then initiated for 30 s for the final adjustment of the thumper and for priming of the chest wall compliance. During sinus rhythm, 45 mm standard mechanical chest compressions were commenced at one of three compression rates, 60, 90 or 120 min − 1, picked at random from a list. The randomisation list was Table 1 Specification of the ECG acquisition system
Lead Input impedance (diff) Input bias path Gain Dynamic range input Bandwidth (-3dB) Sampling rate Resolution Linearity
ECG Defib. channel
ECG Monitoring channel
Lead II, no GND 0.536 MV
Lead II w/GND
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written so that the different compression rates were evenly spread between being performed as the first, second or third method. Manual bag valve ventilation with 100% O2 was performed 15 times per minute without interrupting the compression-relaxation cycle. Expired CO2 was continuously measured, and the person ventilating the pig was blinded for the results. After 2 min CPR was stopped for 30 s, enabling ECG recordings with and without CPR. Chest compressions and ventilations were thereafter restarted at one of the other compression rates. After 2 min CPR was again discontinued for 30 s, and then resumed at the last compression rate. The i.v. infusion, the heating, the inhalation anaesthesia and ventilation were thereafter discontinued. Ventricular fibrillation (VF) was induced by a trans-thoracic current (90 V AC) for 3 s and confirmed by characteristic ECG changes. After 3 min of VF, CPR was restarted with the same compression rates and measurements as during sinus rhythm. After completion of all three compression rates during VF, asystole was induced by a bolus of 20 mmol monokaliumphosphate, and the same study protocol as during sinus rhythm and VF was repeated. All CPR was thereafter stopped, and the pigs died. Each pig was autopsied to check for damage to the internal organs.
2.1.4. Animal data After collection, the animal ECG data were down-sampled to 100 Hz sample rate, and stored in one file for each channel and animal. Signals from all the transducers and the impedance system were sampled at 25 Hz and collected in one file for each animal after interpolation to 100 Hz sample rate.
10 GV
From front end 82 961 mV
From GND electrode
0.035–250 Hz 2000 Hz 1.86 mV 93°, 0.15–25 Hz
0.035–240 Hz 2000 Hz 1.52 mV 93°, 0.15–25 Hz
100 950 mV
2.1.5. Human data Twenty-five ECG episodes each of 15 s duration sampled at 100 Hz, 8 bit resolution of ventricular fibrillation without CPR artefacts, were collected from a proprietary ECG rhythm library (Laerdal Medical AS, Stavanger, Norway) annotated by cardiologists. These signals were originally recorded with various models of Heartstart® (Laerdal Medical AS) during out-ofhospital cardiac arrest.
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2.1.6. Frequency spectra of human and pig VF 6s CPR artefacts Frequency spectra were computed from ten 15 s ECG recordings of CPR artefacts (90 compressions min − 1) randomly extracted from our animal experimental data. Likewise, spectra were computed from ten animal VF records and from ten human VF from the rhythm library described above (2.1.5). All the VF spectra were computed from periods when no CPR was performed, thus without artefacts. 2.2. Simulating artefacts in human ECG 2.2.1. Suggested artefact component model We propose an artefact component model where the artefacts are generated from four separate sources: G1, G2, G3 and G4. G1 are signal components originating from the heart due to mechanical stimulation of the heart itself. G2 are signal components generated by mechanical stimulation of thoracic muscles. G3 are signal components originating from the electrodes and caused by electrode tapping or dragging. G4 are signal components caused by static electricity and the following charge equalising currents between the ECG amplifier and the patient. Chest compressions and ventilation may cause each of the generators to contribute, but with different magnitudes. Some artefact reduction can be achieved by measurement methodology, and some by filtering methodology. Applying a third ground electrode, which will carry most of the charge equalising currents, will reduce the effect of G4. Further reduction is possible by balancing the total impedance in each of the measurement leads, primarily by initial preparation of each electrode site. The magnitude of G3 primarily depends on the magnitude of tapping or dragging during CPR, the electrode active area and the materials that forms the electrode and electrolyte and thus the electrode potentials [9]. In our model, we expect that the defibrillator channel will present artefacts composed from all model components, whereas the monitoring channel present artefacts primarily composed of G1 and G2.
2.2.2. Adding animal CPR artefacts to human VF (VFh) To simulate CPR artefacts in human ECG, we added animal artefacts at different signal-to-noise ratios (SNR). In search for a filtering methodology, we decided to use the artefacts in the material that were the most challenging to filter. These occurred during asystole in one animal and included disturbances from both compressions and ventilations. In the whole material artefacts in asystole had more amplitude and frequency components over a broader band than during VF. We also decided to use the signals from the monitoring channel, as they should be dominated by the G1 and G2 components with less components from G3 (electrode drag or tap) and G4 (static electricity). We hypothesise that the G1 and G2 components are correlated to the information from the thumper displacement signal and the thorax impedance signal. Artefacts were added to each of the 25 human ECGs to produce five mixes with the original SNR set to −10, −5, 0, 5 and 10 dB. These signal to noise ratios were selected in order to simulate a wide range of noise conditions. In the development of the methods for artefact reduction we experimented to find good parameter settings for the filters. To limit the scope of this work, we only used one artefact record read from the monitor electrodes with well-defined artefacts caused by both ventilation and compressions at a rate of 90 min − 1. 2.3. Artefact reduction Preliminary experiments with different filter options demonstrated a potential in applying digital adaptive filtering [10]. In contrast to filtering with constant filter coefficients, this method allows for filtering even when the frequency spectra for the artefact and the desired signal overlap. The adaptive filtering technique also provides an opportunity to use the information in the thumper control and impedance measurement signals, thus applying information about G1 and G2 in the artefact model. (For details on the mathematics of artefact addition and reduction see appendix).
2.4. Statistical analysis Data are presented as mean9S.D. For the comparison of the restored SNR levels for the two
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filter types we used Wilcoxon rank-sum test. Statistical significance was considered to be at the PB0.05 level.
3. Results
3.1. Animal data, obser6ations The CPR artefacts appeared different during SR, VF and asystole (Fig. 1). During SR the only observed changes in the ECG with CPR were RR-interval modifications in the QRS-complexes during chest compressions, and some signal excursions possibly due to G3 and G4. During VF and asystole, the magnitude of the artefact increased as the spontaneous electrical activity dropped. Furthermore, we observed that there tended to be differences in artefact magnitudes and shapes between the monitoring channel and the defibrillator channel. This is illustrated in Fig. 2 showing the simultaneous recording via monitoring (a) and defibrillation (b) leads from one of the animals. CPR was performed only during the first 7.5 s
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where there are distinct artefacts present in the defibrillation channel that do not appear in the monitoring channel. The constructed spectrograms are time-frequency transformations of the ECG (Fig. 3). In the two parts of Fig. 3, the spectrograms represent the period after onset of VF until right before asystole is induced in two different animals. In Fig. 3a, the frequency components corresponding to the harmonics of the CPR artefacts given at the three rates, 60, 90 and 120 min − 1, appear as distinct horizontal red lines in the 0–5 Hz frequency band. The frequency components corresponding to VF appear as a time-varying composition of frequency components in the frequency area above 5 Hz. In the pauses between the first two compression periods the direct effect of CPR disappears as indicated by the yellow to green colouring without distinct bands. In some animals spontaneous periodic activity appeared during VF and continued after the end of CPR. This is illustrated in Fig. 3a, where there is evidence of low frequency activity in the 0–5 Hz areas after the last compression period. In Fig. 3b
Fig. 1. Different degree of disturbance in (a) sinus rhythm (SR), (b) ventricular fibrillation (VF) and (c) asystole (A). The first and last three-second periods are with and without ongoing CPR respectively.
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Fig. 2. Example of difference in artefacts presented by (a) monitoring channel and (b) defibrillation pad channel. CPR was ongoing only during the first 7.5 s where there are artefact components in the defibrillation pad channel which are not present in the monitoring channel. This suggests that these artefact signals do not originate from the heart or thoracic muscles (G1 and G2 of the artefact model), but from the defibrillation pads by electrode tapping or dragging (G3) or static electricity followed by charge equilibration (G4).
such spontaneous activity is evident from the onset of VF. The compression periods are less evident than in the corresponding parts of Fig. 3a. The frequency components of the artefacts become less distinct when spontaneous activity is present.
3.2. A6eraged frequency spectra of human VF and pig VF 6ersus CPR artefacts (Fig. 5) The spectral components of the CPR artefacts during asystole were in the frequency area below 5 Hz (Fig. 4a), of animal VF in the area above 5 Hz (Fig. 4b), and of the human VF in the area from 0 to 10 Hz.
in Fig. 5. These were the two channels we later attempted to reconstruct after filtering. In the first attempt we selected thorax impedance (6i (n)) as reference. Results after filtering are shown in Fig. 6. In the next attempt displacement (6d (n)) was the reference (Fig. 7). In the third attempt a mix of thorax impedance and displacement (6d + i (n)) was used (Fig. 8). The best performance was achieved using the latter, clearly demonstrated when comparing Figs. 5 and 8. The reconstructed channels corresponding to the fixed coefficient filter clearly show that this kind of filter fails when challenged with human ECG (Fig. 9).
3.3. Considering 6arious filter types
3.4. Comparing adapti6e filters to fixed coefficient filters
Human VF was mixed with asystole with CPR artefacts from pig no. 7 at a compression rate of 90 min − 1. The original signals in the mix are shown
When the 25 human ECGs, which each mixed to five different original SNR levels, were attempted restored by filtering, the adaptive mixed reference
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Fig. 3. Time frequency representations (spectrograms showing the scaled logarithm of the magnitudes of the time dependent Fourier transform) of the VF period in two animals. The colours identify the magnitudes as follows: low (dark blue), below middle (light blue), above middle (yellow), high (red). The frequency components of CPR artefacts (and spontaneous activity) and VF are identified by high magnitudes (red) in the frequency areas 0 – 5 Hz and 5 – 15 Hz respectively.
filter outperformed the high-pass fixed coefficient filter for all SNR levels (Table 2).
4. Discussion Separating CPR artefacts from the myocardial ECG signal during VF in man is more difficult than the corresponding problem in pigs where it has been successfully achieved by applying digital filters with fixed coefficients [6]. These filters work by suppressing fixed frequencies, and their success thus require that the major frequency components of the CPR artefacts and the signal reflecting the cardiac rhythm are separate. This is the case in pigs with the major artefact components below 5 Hz and the VF band in the area above 5 Hz as
found in the present study (Figs. 3 and 4) and previously reported by Noc et al. [5] and Strohmenger et al. [6]. It is not the case in humans where the frequencies of VF are lower with much more overlap with the artefacts as seen in the present study (Fig. 4) and reported by Strohmenger et al. [7]. Therefore, other techniques have to be applied. To explore the potential of other techniques we developed a model to simulate different CPR artefacts during human VF by adding artefacts from animals to original human ECGs in VF. The intention was to develop a new principle and evaluate it according to signal reconstruction and not by effects on rhythm or VF analysis. This was the reason for using this combination of signals instead of using only human data during CPR.
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Fig. 4. Averaged spectral estimates for (a) CPR-artefact in asystole, (b) Ventricular fibrillation (VF) in animals and (c) VF in humans. The estimates were computed as the mean of the individual estimates for each of ten 15-s ECG records, each normalised to unity variance. The area under the spectrum curve represents the total power of the signal.
Fig. 5. The original signals representing (a) animal artefact and (b) human ventricular fibrillation to be attempted reconstructed after filtering. (a) and (b) illustrates the ideal filter output.
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Fig. 6. Filter solution with impedance as artefact reference. (a) Reconstructed CPR artefact. (b) Reconstructed ventricular fibrillation (VF).
Fig. 7. Filter solution with displacement as artefact reference. (a) Reconstructed CPR artefact. (b) Reconstructed ventricular fibrillation (VF).
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Fig. 8. Adaptive filtered estimated channels with reference mix. (a) Reconstructed CPR artefact. (b) Reconstructed ventricular fibrillation (VF).
Fig. 9. Fast FIR filter with fixed coefficients selected frequency 4.875 Hz removing three lower harmonics of CPR artefacts. (a) Reconstructed CPR artefact. (b) Reconstructed ventricular fibrillation (VF).
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Table 2 Filter performancea Original SNR (dB)
−10.0 −5.0 0.0 5.0 10.0
Restored SNR Fixed coefficient filter
Adaptive filter
P
−0.290.5 0.690.7 0.990.7 1.090.8 1.09 0.8
2.49 0.5 6.29 0.7 9.09 0.7 10.69 0.8 11.39 0.8
B0.0001 B0.0001 B0.0001 B0.0001 B0.0001
a
Twenty-five human VF segments were all mixed with animal CPR artefact in asystole at 90 compressions min−1 via monitoring electrodes to the given original signal-to-noise ratio (SNR) levels. The adaptive filter uses the mixed references of impedance and position while the fixed coefficient filter removes the three lower harmonics of the CPR artefact. The results are mean9 S.D. Test for difference in population by Wilcoxon rank-sum test to compare performance of adaptive filter solution to that of fixed coefficient filter solution.
During clinical, manual CPR we have no control of the artefact production with exact registration of impedance, timing of compressions and ventilation etc. The present was a modelling experiment that also enabled a comparison of the signals after the attempted removal of the artefacts with the original VF signals before the artefacts were added. In this study we have established a method for mixing artefacts from animals (noise) with human VF (signal) at various signal-to-noise ratios. We also managed to develop a new methodology for removing these artefacts by applying a digital adaptive filter where the reference inputs reflect CPR activity. The degree of success depends on the reference signals being used. Our choice was to use both the compression depth signal and the thoracic impedance signal, which together represent signals that are correlated to the artefacts seen during CPR. The filter performed best when the two reference signals were mixed together. A third reference signal representing the charge equalising current determining G4 might improve the performance. A further improvement of the digital adaptive filter design would make it handle a number of independent references separately. The feasibility of this principle was then assessed in a model where human VF was mixed with animal artefacts. The results illustrate that this filtering principle has a potential and outperformed the fixed coefficient high-pass filter under the given conditions. The key issue is that adaptive digital filters can be used on artefacts that overlap the desired signal in frequency.
When CPR modified certain VF characteristics, like amplitude and median frequency, this has been considered the effect of the treatment and not artefacts [3,4,13]. Our artefact model suggests four independent sources of artefact components, but further analysis should be carried out to verify the model and to possibly expand it. Further investigations are also required to evaluate how well VF classifiers used in defibrillators perform during CPR, with and without filtering. To conclude on the feasibility of the principle, human ECGs with corresponding reference signals acquired during CPR are needed. Finally, we will emphasise that the solution in reducing CPR artefacts should always be to first minimise the original artefacts by using electrodes with the lowest electrode potential, proper electrode contact, proper placement, avoid tapping the electrodes during CPR and preferably also by using a third ground electrode. Acknowledgements We would like to thank Michigan Instruments for their contribution with instrumentation for this study. Appendix A We denote the human ECG by xh (n), and the animal artefact to be added by a(n). These two signals are added together x(n) according to x(n) =xh (n)+a(n).
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The original SNR is defined as SNRorg = 10·log10
s2xh , s2a
where s2xh is the variance of the human ECG and s2a is the variance of the added artefact. The artefacts were initially normalised to unit variance and then added to the human VF to a specific SNRorg according to x(n)= xh (n) + C·an (n), where C= s2xh/10SNRorg /10 and an (n) is the normalised artefact. Our main objective was to restore xh (n), the human VF part of the mix, as well as possible by designing a digital filter, h(n) of length N, so that the restored signal, xˆh (n), could be obtained through the filtering procedure N−1
xˆh (n) = % h(n −k)·x(k), k=0
where k is the counting index for the filter coefficients. The adaptive filter method updates h(n) at every sample instant by providing an iterative gradient search approximation for the optimal filter coefficients given by the solution of the Wiener –Hopf equations [11]. The filter illustrated in Fig. 10 seeks to remove the part of x(n) correlated to the reference signal, 6(n). One might consider the removed part, aˆ (n), as an estimate of the artefact. The reference signal, 6(n), is either the signal from the displacement transducer (6d (n)) or the signal from
the impedance system (6i (n)) or a combination of the two (6d + i (n)). We used a Conjugate Gradient (CG) algorithm [12] Adaptive Finite Impulse Response (FIR) filter [11], which in preliminary experiments [10] proved to perform best compared to other gradient search techniques with respect to convergence time and error adaptation. In these experiments, a step size of 0.005, forgetting factor of 0.99999 and a filter length of 9 were found to be reasonable filter parameters. As comparative reference we applied a fixed coefficient high pass filter with cut off frequency, fc, set according to fc =fhi + ( fhi +1−fhi /2), fhi denoting the ith harmonic of an artefact signal. We wanted to remove the three lower harmonics of artefacts corresponding to 90 compressions min − 1, this resulting in fc =5.25 Hz. This cut off frequency corresponds to what Strohmenger et al. found to be necessary to reduce interference from CPR artefacts on VF analysis in man [7]. This filter was designed using the window method for construction of linear phase FIR filters with 30 coefficients [11]. We did some observational trials at filtering one given artefact mix with all these filter types: (a) Adaptive filter with 6d as reference, (b) adaptive filter with 6i as reference, (c) adaptive filter with 6d + i as reference and (d) the high-pass filter with fixed coefficients. In these trials we wanted to examine the behaviour of both the restored signals, xˆh (n) and aˆ (n). The filter quality was measured through SNR of the restored signal. The error signal e(n) is defined as the difference of the original noise-free human ECG to the restored signal e(n)=xh (n)−xˆh (n).
Fig. 10. Adaptive filter. The adaptive filter, h(n), is updated at every sample instant to remove part of the mixed signal, x(n) =xh (n) +a(n), correlated to the reference signal, 6(n). The removed signal, aˆ (n), represents the estimated artefact signal, while the remaining part, xˆh (n), represents the estimate of the human VF.
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We wanted this error to be as small as possible or equivalently obtain a large SNR for the restored signal as given by SNRres = 10·log10
s2xh . s2e
The adaptive filter using the reference signal 6d + i(n) was evaluated with respect to SNRres to be compared to the performance of the high-pass fixed coefficient filter for the signals mixed as described previously original SNR levels −10, − 5, 0, 5 and 10 dB.
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