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Thoracic-impedance changes measured via defibrillator pads can monitor signs of circulation
Resuscitation (2007) 73, 221—228
CLINICAL PAPER
Thoracic-impedance changes measured via defibrillator pads can monitor signs of circulation夽 Heidrun Losert a, Martin Risdal b, Fritz Sterz a,∗, Jon Nysæther c, Klemens K¨ ohler a, Trygve Eftestøl b, Cosima Wandaller a, Helge Myklebust c, Thomas Uray a, Sven O. Aase b, Anton N. Laggner a a
Department of Emergency Medicine, Medical University of Vienna, Austria Department of Electrical and Computer Engineering, University of Stavanger, Norway c Laerdal Medical, Stavanger, Norway b
Received 22 August 2006 ; received in revised form 26 September 2006; accepted 3 October 2006 KEYWORDS Automated external defibrillator (AED); Blood pressure; Cardiopulmonary resuscitation (CPR); External chest compression (ECC); Cardiac arrest; Return of spontaneous circulation; Transthoracic impedance
Summary Aims: To investigate the potential for finding an alternative for the ‘pulse check’ during CPR, we studied the use of thoracic impedance measured via the defibrillator pads for circulation assessment during CPR. Materials and methods: Transthoracic impedance, ECG and arterial pressures were recorded on 69 patients with a resulting data set of 434 segments. The circulatoryrelated impedance waveform was first isolated manually and features characterising its shape were suggested. Results: The features were correlated with corresponding blood pressure measurements, where a low, but significant, correlation coefficient (0.3) was found. By dividing the data set in groups of sufficient and insufficient circulation and using a neural network, we found that trends in features of the impedance waveform showed a discriminative potential for the two groups. Our classifier achieved a sensitivity of 90% for recognising insufficient circulation with a specificity of 82%. Conclusions: We have shown that the circulation-related information found in the impedance signal may be used for circulatory assessment, especially the recognition of restoration of spontaneous circulation after cardiac arrest. © 2006 Elsevier Ireland Ltd. All rights reserved.
Introduction 夽
A Spanish translated version of the summary of this article appears as Appendix in the final online version at 10.1016/j.resuscitation.2006.10.001. ∗ Corresponding author at: Universit¨ atsklinik f¨ ur Notfallmedizin, Medizinische Universit¨ at Wien, W¨ ahringerg¨ urtel 18-20/6D, 1090 Wien, Austria. Tel.: +43 1404001964; fax: +43 1404001965. E-mail address: [email protected] (F. Sterz).
A key element in cardiopulmonary resuscitation is the recognition of cardiac arrest. So far no accurate methods of diagnosing cardiac arrest are available.1,2 Checking the carotid pulse is an inaccurate method of confirming the pres-
0300-9572/$ — see front matter © 2006 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.resuscitation.2006.10.001
222 ence or absence of circulation3—8 and there is no evidence that checking for other ‘signs of circulation’ is diagnostically superior.9,10 Therefore, a simple, objective and non-invasive way of performing a pulse check is of interest and may potentially increase survival in cardiac arrest patients. Transthoracic-impedance plethysmography has been studied for decades as a non-invasive technique for estimating stroke volume and cardiac output.11—13 Impedance plethysmography was described as early as 1897 by Stewart and proposed for non-invasive measurements of cardiac output.14 It has been suggested to equip automated external defibrillators (AEDs) with the ability to measure the transthoracic impedance through defibrillator pads.15,16 The impedance measurement may provide the rescuer with information on the circulation that will help in treatment of the cardiac arrest patient by potentially reducing the time spent on pulse check.17 Therefore, studies of the impedance waveform measured from an AED during CPR are necessary to describe the complex relation between the circulation and the impedance waveform. We suggest that the circulation-related impedance waveform represented by descriptive parameters can be correlated with the blood pressure. If this does not find a relationship, we attempted to solve the problem within a pattern recognition framework of a neural network to discriminate between segments with systolic arterial pressures >80 mmHg from those with <80 mmHg.
Materials and methods Study design This was a prospective, observational case series of a convenience sample of critically ill and ventilated patients, between March 2003 and January 2005. The study procedures were approved by the committee on human experimentation. The study was carried out at an emergency department of a tertiary care university hospital with an annual census of 75,000 patients.
Participants A convenience sample of patients older than 18 years of age with atraumatic, normothermic, witnessed cardiac arrest, in a haemodynamically stable condition with controlled mechanical ventilation were studied. Patients were not included if
H. Losert et al. Table 1 Clinical relevant data of patients in haemodynamically stable controlled mechanical ventilated conditions N 32 Age (years) 50 (44, 59) Female (n, %) 9 (28) BMI 25 (23, 30) Body temperature during 36.1 (33.5, 36.8) [30] measurements (◦ C) [n] Arterial pressure monitoring site Radial artery (n, %) 30 (94) Femoral artery (n, %) 2 (6) Cardiac rhythm during measurements Sinus (n, %) 28 (88) Arterial fibrillation (n, %) 1 (3) Pacemaker (n, %) 3 (9) Resuscitation before 17 (13, 20) [6 (19)] measurements (min) [(n, %)] Admission diagnosis Cardiac arrest, cardiac 14 (44) aetiology (n, %) Cerebrovascular disease 8 (25) (n, %) Intoxication (n, %) 5 (16) Cardiogenic shock (n, %) 1 (3) Sepsis (n, %) 1 (3) Gastrointestinal bleeding 1 (3) (n, %) Patients history Mild pulmonary 1 (3) emphysema (n, %) Cardiomyopathy (n, %) 4 (13) Pulmonary artery embolism 1 (3) (n, %) Chest X-ray (n, %) 32 (100) Pneumonia (n, %) 4 (13) Pneumothorax (n, %) 1 (3) Effusion (n, %) 8 (25) Edema (n, %) 5 (16) Atelectasis (n, %) 3 (9) Data are presented as the median and interquartile range (IQR, range from the 25th to the 75th percentile).
they had known terminal conditions or pregnancy (Tables 1 and 2). Intensive care medicine was provided according to a standard protocol.18,19 Admission diagnosis and known medical history were assessed routinely and the data for cardiac arrest patients encompassed all information required for the international ‘‘Utstein’’-Style criteria.20,21
Measurement All patients were ventilated with a ServoI® Ventilator system (Version 1.2, Siemens Medical Group,
Thoracic-impedance changes measured via defibrillator pads Table 2 Clinical relevant data of patients with nontraumatic, normothermic, witnessed cardiopulmonary arrest N 37 Age (years) 60 (49, 73) Female (n, %) 11 (30) BMI 28 (25, 29) Body temperature during 35.0 (34.4, 35.0) [31] measurements (◦ C) [n] Arterial pressure monitoring site Radial artery (n, %) 22 (59) Femoral artery (n, %) 15 (41) Cardiac rhythm during measurements Ventricular fibrillation (n, %) 18 (49) Asystole (n, %) 20 (54) Pulseless electrical activity 8 (22) (n, %) CPR duration during 14 (9, 30) measurements (min) CPR duration before 24 (13, 30) measurements (min) Cardiac arrest aetiology Cardiac (n, %) Pulmonary artery embolism (n, %) Intoxication (n, %) Aortic aneurysm (n, %) Hyperkalaemia (n, %) Cardiac arrest—–intestinal bleeding (n, %) Patients history Cardiovascular (n, %) Unknown (n, %) Outcome Restoration of spontaneous circulation (n, %) Died (n, %) Good survival (n, %)
30 (81) 2 (5) 1 (3) 1 (3) 1 (3) 1 (3)
15 (41) 9 (24) 17 (46) 14 (27) 12 (32)
Data are presented as the median and interquartile range (IQR, range from the 25th to the 75th percentile).
Frankfurt, Germany) and the blood pressure was monitored via a catheter in the radial or femoral artery (Tables 1 and 2). Catheter positions were confirmed by observing waveforms, pressure measurements and blood gas analyses. Catheters were connected to transducers (Baxter Pressure Monitoring Kit, Bentley Laboratories Europe B.V., Uden, Holland) through a normal saline flush system. Continuous pressure tracings were displayed on a multichannel monitor (HPSeries600 Monitor, Product No. M-1166A, Hewlett-Packard, Palo Alto, CA, USA) and as analogue readout (HPSeries with eight-channel analog output option #J11, HewlettPackard, Palo Alto, CA, USA). The analogue output function card provides eight channels of analogue
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output for connection to the data acquisition system interfaced to a local server (VipdasTM Biosys GesmbH, Wien, Austria) for continuous recording of wave and numeric outputs. For each patient, the ECG and arterial pressures were recorded continuously. This enabled the recorded arterial pressure data to act as a synchronised reference for impedance data. An investigational monitor/defibrillator was used in the study to record thoracic impedance (Heartstart® 4000SP Laerdal Medical Cooperation, Stavanger, Norway) via commercially available selfadhesive electrode defibrillator pads (Heartstart Pads® , Philips Medical Systems, Seattle, WA, USA) in the recommended standard positions.22 The episodes were analysed offline, and segments with rhythm type of either pulseless electrical activity (PEA) or a pulse-generating rhythm where identified using the ECG recordings. If the ECG, impedance or the blood pressure trace were corrupted by noise or not present, the segment was not included in the remaining analysis. By making sure that the blood pressure was stable over the duration of the segment, we assumed that the impedance waveform resulting from each heartbeat was the same throughout the segment. Due to respiration artefacts in the impedance channel in the cardiac arrest data, we chose to use only segments with more than nine QRS complexes. This was to assure the accuracy of the technique used first isolating the circulatory-related impedance waveform to be analysed and later combining this into one representative waveform. For each segment, we now wanted to find a representation of the circulatory-related impedance waveform resulting from a heartbeat. Such descriptive parameters were the difference between the minimum and maximum value of the waveform (feature 1). This was expected to be connected with the duration of the waveform, so we let feature 2 be feature 1 divided by the duration of the segment in ms. Features 3 and 4 were the same features extracted from the derivative signal of the wave. We hypothesised that the area under the waveform changes with circulation. By setting the starting point of the wave to zero, we let feature 5 represent the area under the waveform, and let feature 6 represent the area per sample. The impedance change of the longest negative flank of the waveform was feature 7 and its duration feature 8. The impedance change per time of this flank was feature 9. The relation between the parameters describing the circulatory-related impedance waveform and the circulatory information was then studied. After thorough studies of the impedance waveform
224 of many patients, the connection between blood circulation and the impedance waveform was far more complex than can be captured by a single measurement. Therefore, in a further evaluation a pattern recognition framework was used. There we make no presumption on the connection between impedance and blood pressure-related variables, but allow the problem to be solved by a neural network. Here we used the blood pressure measurements to classify the impedance trace of an organised rhythm as either systolic arterial pressure of [Ps] > 80 mmHg or Ps ≤ 80 mmHg.23,20 To be able to compare the impedance parameters to circulation information, the variables in the blood pressure channel had to be selected. We, therefore, estimated the systolic (Ps), diastolic (Pd), peak (PP) and mean arterial pressure (MAP) over the duration of the segment.
Data analysis The extracted data for any mutual information in the impedance and blood pressure channel were now analysed in two ways.
Correlating the variables For each patient, the mean of the impedance variables and the blood pressure parameters over all segments was calculated. The sample set of the different variables were compared by computing their correlation coefficient,24 and the statistical significance of the correlation coefficient.25 The p-value indicates how likely it was that the correlation coefficient truly is 0 and that the estimation was a result of variations in the data. For values of less than 0.05, the correlation was assumed to be statistically significant.
Discriminating between patients with and without sufficient spontaneous circulation (systolic blood pressure >80 mmHg and ≤80 mmHg) A neural network was trained to classify the segments as either Ps > 80 mmHg or Ps ≤ 80 mmHg.26 The performance of a classifier was evaluated and visualised by means of receiver operating characteristic (ROC) graphs.27 The area under the ROC curve, AUC, was used as a measure of performance, which gave a general impression of the ability of the classifier to discriminate between the classes. We treated the output of the neural network as the discrimination function of observing various numbers of input features. We used a deci-
H. Losert et al. sion rule where to assign the observation to class as either Ps > 80 mmHg or Ps ≤ 80 mmHg. For a choice of decision threshold, we were able to calculate the sensitivity and specificity of our training and test set, and thereby a point on the ROC graph. Because of the sigmoid transfer function in the neural network, the output was between 0 and 1. We let decision thresholds compute points on the ROC graphs. The area under the ROC graph, AUC, was calculated for each test set. An estimate of the performance of the classifier was then calculated as the mean of the AUC over all test sets, and gave a general measure of the classifier’s performance for different variable settings. The standard deviation was also calculated to get an impression of the variation of the classifier performance. To find the best way to represent the impedance waveform, e.g. to find the best feature a greedy search was performed. First the performance in terms of AUC when using one feature was evaluated, and then the performance of all combinations of two features, where one of them was the one that gave the best performance when using only one feature. This routine was continued until all possible features had been included. We analysed the results to find the feature combination of the search procedure that gave the best discrimination power. The final result was compared to the result from using only one feature.
Results Characteristics of study subjects Due to data transfer problems between the monitor and our computer-based data analysis system 6 of the haemodynamically stable patients (n = 38) and 48 of the patients in cardiac arrest (n = 85) had to be excluded. Therefore, measurements from 32 haemodynamically stable patients and 37 cardiac arrest patients were useable. Patient characteristics, reasons for admittance, known medical history and other relevant clinical data are shown in Tables 1 and 2. The resulting data set consisted of 434 segments, 114 from 32 haemodynamically stable patients, and 320 from 37 cardiac arrest patients. Each patient is represented by a median of four segments (range 1—79), with a median duration of 15.0 s (range 5.5—40.0).
Correlating the variables In Table 3, the correlation coefficient between the impedance variables and the blood pressure vari-
Thoracic-impedance changes measured via defibrillator pads
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Table 3 The correlation coefficient between the blood pressure [systolic (Ps), diastolic (Pd), peak (PP) and mean arterial pressure (MAP)] and selected impedance waveform features (see text) Impedance variables (see text) 1 2 3 4 5 6 7 8 9 * Correlation
Blood pressure variables Ps
Pd *
0.2979 0.3299* 0.3122* 0.2998* −0.2697* −0.3351* 0.2821* −0.0237 0.2898*
PP *
0.3443 0.3326* 0.3514* 0.2961* −0.3117* −0.3537* 0.3303* −0.0353 0.3481*
MAP *
0.2380 0.3102* 0.2516* 0.2802* −0.2068 −0.2926* 0.2214* −0.0219 0.2283*
0.3431* 0.2809* 0.3448* 0.2474* −0.3162* −0.3222* 0.3336* −0.0368 0.3547*
coefficients that are statistically significant (p < 0.05).
ables are listed. We observe that many correlations are statistically significant, and that no correlations are high. Boxplots of three features with sufficient correlation coefficients with p-values are shown in Figure 1. There are trends of the impedance values being different for Ps > 80 mmHg and Ps ≤ 80 mmHg. The range of the circulation-related impedance waveform relative to its duration and the range of the derivative signal of the waveform seem to have a higher value for sufficient, than for insufficient circulation. In addition, the area per sample under that waveform, seems to have a larger negative value for sufficient, than for insufficient circulation.
Discriminating between patients with and without sufficient spontaneous circulation (Ps > 80 mmHg and ≤80 mmHg) The segments were divided in two classes according to Ps > 80 mmHg and Ps ≤ 80 mmHg; 334 of the segments were recorded as Ps > 80 mmHg and the remaining 100 as Ps ≤ 80 mmHg. The performance of the classifier for increasing number of included features is shown in Figure 2. Each point represents the mean performance of the best feature for a given number of features. As the dimensionality of the features increases, so does the discrimination ability of the classifier. The largest improvement is for going from one to three features. The benefit in spatial separation of going from one to two dimensions is easily observed. It is worth noting that several of the polynomial coefficients are chosen at an early stage of the greedy search. Other important features are related to the duration of the waveform, the derivative of the waveform and the area under the waveform. In Figure 3, the ROC graph of the classifier when using the combination of the best features is shown together with the ROC
graph of the best classifier using only one single feature. We see the improvement from increasing the feature dimensions.
Discussion Comparing variables describing the circulationrelated impedance waveform and the blood pressure measurements showed a low correlation between the two measurements. The distribution of the variables describing the circulation-related impedance waveform does however indicate that the waveform contains information that can help identify an adequate circulation. A neural networkbased classifier was able to accurately identify segments with a systolic blood pressure above 80 mmHg. The impedance signal, therefore, may have an important role in automatically identifying restoration of spontaneous circulation (ROSC) during resuscitation. By correlating features describing the thoracicimpedance waveform related to cardiac activity and arterial blood pressure, we found a significant correlation for several of the features. The correlation was however small, and it can, therefore, be concluded that these features cannot be used for estimation of blood pressure using a linear model due to different waveforms. Therefore, we used the blood pressure measurements to classify the impedance trace of an organised rhythm as either an arterial pressure of ≤80 mmHg or >80 mmHg. The assumption was that pressures >80 mmHg are an indicator of a pulse (ROSC). We found features with significantly different medians for the two subgroups. This indicates that the characteristics of the circulation-related impedance waveform contains information, that can be used to discriminate between patients with an adequate and inadequate
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H. Losert et al.
Figure 2 The results in terms of area under the curve (AUC) from increasing the numbers of features by a greedy search.
Figure 1 Boxplot of the distribution of segment feature (a) 2, (b) 3 and (c) 6 for the two classes where Ps ≤ 80 and Ps > 80 mmHg (‘‘ROSC-’’ and ‘‘no-ROSC-group’’), where N is the number of samples the boxplot is based upon. For the distribution of samples representing patients, N = 16 is where Ps < 80 mmHg and N = 53 is where Ps > 80 mmHg. For the distribution of samples representing segments, N = 100 is where Ps ≤ 80 mmHg and N = 334 is where Ps > 80 mmHg. The boxes have lines at the lower quartile, median, and upper quartile values. The notches represent a robust estimate of the uncertainty about the medians for box-to-box comparison. A no notch overlap indicates that the medians of the two groups differ at the 5% significance level. The whiskers are lines extending from each end of the boxes to show the extent of the rest of the data. The whiskers extend from the box out to the most extreme data value within 1.5 times the interquartile range. Outliers are data with values beyond the ends of the whiskers.
circulation. The waveforms are generally larger and faster changing for patients with an adequate circulation than for an inadequate circulation. The current guideline statement1,28 with regards to pulse check is: ‘‘. . . the rescuer should not rely on the pulse check to determine the need for chest compressions or use of an AED.’’ The most convincing study showing the inability of health care providers to identify the presence of a carotid pulse in adults rapidly and accurately was by Eberle et al.6 The sensitivity and specificity of manual pulse check has been reported to be 90 and 55%, respectively. Other studies used normal, healthy volunteers8,3 , young volunteers29 , or anaesthetised
adults30 or infants31 to assess the presence of a pulse. These latter studies show that many healthcare providers require at least 10 s to identify the pulse with a reasonable degree of reliability (80% sensitivity) in a normal, well-perfused person. Lay rescuers performed less well.3 More recently, Lapostolle et al.32 used a computerised manikin and confirmed that fewer than 50% of 64 prehospital healthcare providers could determine the absence of a pulse accurately within 10 or 30 s. Rapid recognition of the presence of a pulse will avoid inappropriate chest compressions, but more importantly, failing to recognise the absence of a pulse will lead to death, if chest compressions and/or defibrillation is not provided because the lay rescuer thought a pulse was present. With our classifier we achieved a sensitivity of 90% for a specificity of 82% of discriminat-
Figure 3 The ROC graph for the best single feature, and the best feature combination.
Thoracic-impedance changes measured via defibrillator pads ing between segments with systolic blood pressure above or below 80 mmHg. The most discriminating information was found by using the features that describe the shape of the waveform. These are the polynomial coefficients and the area under the waveform. The range of the derivative of the waveform shape is also important, as it describes how fast the impedance changes for a heartbeat. The change in impedance is thought to arise mainly from redistribution of blood in the thorax and change of blood velocity,11 but the exact contribution of each circulatory event to the impedance waveform is not known. Djordjevich et al.33 correlated measurements from 146 volunteers of the magnitude of the negative peak of the first time derivative of the thoracic impedance, which is comparable to feature 3 in our experiments, with blood pressure measurements. A correlation coefficient of −0.567 with systolic blood pressure was found, and it was concluded that the negative peak seems to decrease with an increase in blood pressure. Our results indicate the opposite, but our correlation is smaller and the sample number is lower. In addition, there is a difference in electrode set-up and in patients. This may partly explain the different results, and illustrates the complexity involved in using impedance for circulatory measurements. It has also been stated33 that the shape and amplitude of the impedance waveform is strongly influenced by the dynamic response of the vascular system and the shape of the impulse created by the ventricular contractions. Due to cardiovascular malfunctions in cardiac arrest patients, the impedance waveform may assume even a wider variety of amplitudes and shapes. By combining features describing the impedance waveform, we were able to extract some useful information about the circulatory state of the patient. The impedance technique incorporated into an AED could be used to monitor signs of circulation in a cardiac arrest situation. It could serve as an aid to give circulation-related feedback to the rescuer and thereby improve CPR quality.34—36 There are, however, several problems to be solved before the results are clinically useable. First, the QRS complexes have to be detected automatically. Second, the circulation-related impedance waveform has to be isolated automatically before any discriminative features can be extracted. Inaccuracy in any of these tasks will lead to errors in the features extracted and a less accurate classification. Currently, a pause in compressions is necessary to allow for a pulse check. It should be possible to apply compression artefact removal strategies used in VF analysis so that identification of a rhythm with a pulse can be done during
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compressions.17 This could allow further reduction in no flow times.
Conclusions The present study showed that the impedance measurement system sensor of a defibrillator is likely to provide adequate monitoring of the presence or absence of circulation and thus shows that the impedance signal does contain information of clinical relevance that can help identify restoration of spontaneous circulation during resuscitation.
Conflict of interest Helge Myklebust and Jon Nysæther are Laerdal Medical Employees. Klemens K¨ ohler was employed for 12 months at the Department of Emergency Medicine, Medical University Vienna with support of a grant from Laerdal Medical, Stavanger, Norway.
Acknowledgments Laerdal Medical, Stavanger, Norway provided travel grants for scientific meetings for Heidrun Losert and Klemens K¨ ohler. Heidrun Losert received a laptop from Laerdal Medical, Stavanger, Norway. The study was supported in part by a commercial sponsor (Laerdal Medical, Stavanger, Norway).
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228 8. Ochoa FJ, Ramalle-Gomara E, Carpintero JM, Garcia A, Saralegui I. Competence of health professionals to check the carotid pulse. Resuscitation 1998;37:173—5. 9. Ruppert M, Reith MW, Widmann JH, et al. Checking for breathing: evaluation of the diagnostic capability of emergency medical services personnel, physicians, medical students, and medical laypersons. Ann Emerg Med 1999;34:720—9. 10. Skogvoll E, Isern E, Sangolt GK, Gisvold SE. In-hospital cardiopulmonary resuscitation—–5 years’ incidence and survival according to the Utstein template. Act Anaesth Scand 1999;43:177—84. 11. Geddes LA, Baker LE. Response to passage of electric current through the body. J Assoc Adv Med Instrum 1971;5:13—8. 12. Kinnen E, Duff C. Cardiac output from transthoracic impedance records using discriminant analysis. J Assoc Adv Med Instrum 1970;4:73—8. 13. Ito H, Yamakoshi KI, Togawa T. Transthoracic admittance plethysmograph for measuring cardiac output. J Appl Physiol 1976;40:451—4. 14. Ashutosh K, Gilbert R, Auchincloss JH, et al. Impedance pneumograph and magnetometer methods for monitoring tidal volume. J Appl Physiol 1974;37:964—6. 15. Pellis T, Bisera J, Tang W, Weil MH. Expanding automatic external defibrillators to include automated detection of cardiac, respiratory, and cardiorespiratory arrest. Crit Care Med 2002;30:S176—8. 16. Johnston PW, Imam Z, Dempsey G, Anderson J, Adgey AA. The transthoracic impedance cardiogram is a potential haemodynamic sensor for an automated external defibrillator. Eur Heart J 1998;19:1879—88. 17. Eilevstjonn J, Kramer-Johansen J, Eftestol T, et al. Reducing no flow times during automated external defibrillation. Resuscitation 2005;67:95—101. 18. American College of Emergency Physicians. Emergency care guidelines. Ann Emerg Med 1997;29:564—71. 19. Haupt MT, Bekes CE, Brilli RJ, et al. Guidelines on critical care services and personnel: recommendations based on a system of categorization of three levels of care. Crit Care Med 2003;31:2677—83. 20. Jacobs I, Nadkarni V, Bahr J, et al. Cardiac arrest and cardiopulmonary resuscitation outcome reports: update and simplification of the Utstein templates for resuscitation registries: a statement for healthcare professionals from a task force of the International Liaison Committee on Resuscitation (American Heart Association, European Resuscitation Council, Australian Resuscitation Council, New Zealand Resuscitation Council, Heart and Stroke Foundation of Canada, InterAmerican Heart Foundation, Resuscitation Councils of Southern Africa). Circulation 2004;110:3385— 97. 21. Brennan RT, Braslow A. Skill mastery in public CPR classes. Am J Emerg Med 1998;16:653—7.
H. Losert et al. 22. European Resuscitation Council. Guidelines 2000 for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Part 6: advanced cardiovascular life support. Section 2: defibrillation. Resuscitation 2000;46:109—13. 23. Jacobs I, Nadkarni V, Bahr J, et al. Cardiac arrest and cardiopulmonary resuscitation outcome reports: update and simplification of the Utstein templates for resuscitation registries. A statement for healthcare professionals from a task force of the international liaison committee on resuscitation (American Heart Association, European Resuscitation Council, Australian Resuscitation Council, New Zealand Resuscitation Council, Heart and Stroke Foundation of Canada, InterAmerican Heart Foundation, Resuscitation Council of Southern Africa). Resuscitation 2004;63:233—49. 24. Rangayyan RM. Biomedical signal analysis. Hoboken, NJ: Wiley-Interscience; 2002. 25. Rosner B. Fundamentals of biostatistics. Boston, MA: PWSKent Publishing Company; 1990. 26. Riedmiller M, Braun H. A direct adaptive method for faster backpropagation learning: the RPROP algorithm. IEEE Int Conf Neuronal Networks 1993;1:586—91. 27. Duda RO, Hart PE, Stork DG. Pattern classification. Hoboken, NJ: Wiley-Interscience; 2000. 28. 2005 International consensus on cardiopulmonary resuscitation and emergency cardiovascular care science with treatment recommendations. Part 6: paediatric basic and advanced life support. Resuscitation 2005;67:271—91. 29. Graham CA, Lewis NF. Evaluation of a new method for the carotid pulse check in cardiopulmonary resuscitation. Resuscitation 2002;53:37—40. 30. Mather C, O’Kelly S. The palpation of pulses. Anaesthesia 1996;51:189—91. 31. Inagawa G, Morimura N, Miwa T, et al. A comparison of five techniques for detecting cardiac activity in infants. Paediatr Anaesth 2003;13:141—6. 32. Lapostolle F, Le TP, Agostinucci JM, et al. Basic cardiac life support providers checking the carotid pulse: performance, degree of conviction, and influencing factors. Acad Emerg Med 2004;11:878—80. 33. Djordjevich L, Sadove MS, Mayoral J, Ivankovich AD. Correlation between arterial blood pressure levels and (dZ/dt)min in impedance plethysmography. IEEE Trans Biomed Eng 1985;32:69—73. 34. Wik L, Kramer-Johansen J, Myklebust H, et al. Quality of cardiopulmonary resuscitation during out-of-hospital cardiac arrest. JAMA 2005;293:299—304. 35. Abella BS, Sandbo N, Vassilatos P, et al. Chest compression rates during cardiopulmonary resuscitation are suboptimal: a prospective study during in-hospital cardiac arrest. Circulation 2005;111:428—34. 36. Abella BS, Alvarado JP, Myklebust H, et al. Quality of cardiopulmonary resuscitation during in-hospital cardiac arrest. JAMA 2005;293:305—10.
CLINICAL PAPER
Thoracic-impedance changes measured via defibrillator pads can monitor signs of circulation夽 Heidrun Losert a, Martin Risdal b, Fritz Sterz a,∗, Jon Nysæther c, Klemens K¨ ohler a, Trygve Eftestøl b, Cosima Wandaller a, Helge Myklebust c, Thomas Uray a, Sven O. Aase b, Anton N. Laggner a a
Department of Emergency Medicine, Medical University of Vienna, Austria Department of Electrical and Computer Engineering, University of Stavanger, Norway c Laerdal Medical, Stavanger, Norway b
Received 22 August 2006 ; received in revised form 26 September 2006; accepted 3 October 2006 KEYWORDS Automated external defibrillator (AED); Blood pressure; Cardiopulmonary resuscitation (CPR); External chest compression (ECC); Cardiac arrest; Return of spontaneous circulation; Transthoracic impedance
Summary Aims: To investigate the potential for finding an alternative for the ‘pulse check’ during CPR, we studied the use of thoracic impedance measured via the defibrillator pads for circulation assessment during CPR. Materials and methods: Transthoracic impedance, ECG and arterial pressures were recorded on 69 patients with a resulting data set of 434 segments. The circulatoryrelated impedance waveform was first isolated manually and features characterising its shape were suggested. Results: The features were correlated with corresponding blood pressure measurements, where a low, but significant, correlation coefficient (0.3) was found. By dividing the data set in groups of sufficient and insufficient circulation and using a neural network, we found that trends in features of the impedance waveform showed a discriminative potential for the two groups. Our classifier achieved a sensitivity of 90% for recognising insufficient circulation with a specificity of 82%. Conclusions: We have shown that the circulation-related information found in the impedance signal may be used for circulatory assessment, especially the recognition of restoration of spontaneous circulation after cardiac arrest. © 2006 Elsevier Ireland Ltd. All rights reserved.
Introduction 夽
A Spanish translated version of the summary of this article appears as Appendix in the final online version at 10.1016/j.resuscitation.2006.10.001. ∗ Corresponding author at: Universit¨ atsklinik f¨ ur Notfallmedizin, Medizinische Universit¨ at Wien, W¨ ahringerg¨ urtel 18-20/6D, 1090 Wien, Austria. Tel.: +43 1404001964; fax: +43 1404001965. E-mail address: [email protected] (F. Sterz).
A key element in cardiopulmonary resuscitation is the recognition of cardiac arrest. So far no accurate methods of diagnosing cardiac arrest are available.1,2 Checking the carotid pulse is an inaccurate method of confirming the pres-
0300-9572/$ — see front matter © 2006 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.resuscitation.2006.10.001
222 ence or absence of circulation3—8 and there is no evidence that checking for other ‘signs of circulation’ is diagnostically superior.9,10 Therefore, a simple, objective and non-invasive way of performing a pulse check is of interest and may potentially increase survival in cardiac arrest patients. Transthoracic-impedance plethysmography has been studied for decades as a non-invasive technique for estimating stroke volume and cardiac output.11—13 Impedance plethysmography was described as early as 1897 by Stewart and proposed for non-invasive measurements of cardiac output.14 It has been suggested to equip automated external defibrillators (AEDs) with the ability to measure the transthoracic impedance through defibrillator pads.15,16 The impedance measurement may provide the rescuer with information on the circulation that will help in treatment of the cardiac arrest patient by potentially reducing the time spent on pulse check.17 Therefore, studies of the impedance waveform measured from an AED during CPR are necessary to describe the complex relation between the circulation and the impedance waveform. We suggest that the circulation-related impedance waveform represented by descriptive parameters can be correlated with the blood pressure. If this does not find a relationship, we attempted to solve the problem within a pattern recognition framework of a neural network to discriminate between segments with systolic arterial pressures >80 mmHg from those with <80 mmHg.
Materials and methods Study design This was a prospective, observational case series of a convenience sample of critically ill and ventilated patients, between March 2003 and January 2005. The study procedures were approved by the committee on human experimentation. The study was carried out at an emergency department of a tertiary care university hospital with an annual census of 75,000 patients.
Participants A convenience sample of patients older than 18 years of age with atraumatic, normothermic, witnessed cardiac arrest, in a haemodynamically stable condition with controlled mechanical ventilation were studied. Patients were not included if
H. Losert et al. Table 1 Clinical relevant data of patients in haemodynamically stable controlled mechanical ventilated conditions N 32 Age (years) 50 (44, 59) Female (n, %) 9 (28) BMI 25 (23, 30) Body temperature during 36.1 (33.5, 36.8) [30] measurements (◦ C) [n] Arterial pressure monitoring site Radial artery (n, %) 30 (94) Femoral artery (n, %) 2 (6) Cardiac rhythm during measurements Sinus (n, %) 28 (88) Arterial fibrillation (n, %) 1 (3) Pacemaker (n, %) 3 (9) Resuscitation before 17 (13, 20) [6 (19)] measurements (min) [(n, %)] Admission diagnosis Cardiac arrest, cardiac 14 (44) aetiology (n, %) Cerebrovascular disease 8 (25) (n, %) Intoxication (n, %) 5 (16) Cardiogenic shock (n, %) 1 (3) Sepsis (n, %) 1 (3) Gastrointestinal bleeding 1 (3) (n, %) Patients history Mild pulmonary 1 (3) emphysema (n, %) Cardiomyopathy (n, %) 4 (13) Pulmonary artery embolism 1 (3) (n, %) Chest X-ray (n, %) 32 (100) Pneumonia (n, %) 4 (13) Pneumothorax (n, %) 1 (3) Effusion (n, %) 8 (25) Edema (n, %) 5 (16) Atelectasis (n, %) 3 (9) Data are presented as the median and interquartile range (IQR, range from the 25th to the 75th percentile).
they had known terminal conditions or pregnancy (Tables 1 and 2). Intensive care medicine was provided according to a standard protocol.18,19 Admission diagnosis and known medical history were assessed routinely and the data for cardiac arrest patients encompassed all information required for the international ‘‘Utstein’’-Style criteria.20,21
Measurement All patients were ventilated with a ServoI® Ventilator system (Version 1.2, Siemens Medical Group,
Thoracic-impedance changes measured via defibrillator pads Table 2 Clinical relevant data of patients with nontraumatic, normothermic, witnessed cardiopulmonary arrest N 37 Age (years) 60 (49, 73) Female (n, %) 11 (30) BMI 28 (25, 29) Body temperature during 35.0 (34.4, 35.0) [31] measurements (◦ C) [n] Arterial pressure monitoring site Radial artery (n, %) 22 (59) Femoral artery (n, %) 15 (41) Cardiac rhythm during measurements Ventricular fibrillation (n, %) 18 (49) Asystole (n, %) 20 (54) Pulseless electrical activity 8 (22) (n, %) CPR duration during 14 (9, 30) measurements (min) CPR duration before 24 (13, 30) measurements (min) Cardiac arrest aetiology Cardiac (n, %) Pulmonary artery embolism (n, %) Intoxication (n, %) Aortic aneurysm (n, %) Hyperkalaemia (n, %) Cardiac arrest—–intestinal bleeding (n, %) Patients history Cardiovascular (n, %) Unknown (n, %) Outcome Restoration of spontaneous circulation (n, %) Died (n, %) Good survival (n, %)
30 (81) 2 (5) 1 (3) 1 (3) 1 (3) 1 (3)
15 (41) 9 (24) 17 (46) 14 (27) 12 (32)
Data are presented as the median and interquartile range (IQR, range from the 25th to the 75th percentile).
Frankfurt, Germany) and the blood pressure was monitored via a catheter in the radial or femoral artery (Tables 1 and 2). Catheter positions were confirmed by observing waveforms, pressure measurements and blood gas analyses. Catheters were connected to transducers (Baxter Pressure Monitoring Kit, Bentley Laboratories Europe B.V., Uden, Holland) through a normal saline flush system. Continuous pressure tracings were displayed on a multichannel monitor (HPSeries600 Monitor, Product No. M-1166A, Hewlett-Packard, Palo Alto, CA, USA) and as analogue readout (HPSeries with eight-channel analog output option #J11, HewlettPackard, Palo Alto, CA, USA). The analogue output function card provides eight channels of analogue
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output for connection to the data acquisition system interfaced to a local server (VipdasTM Biosys GesmbH, Wien, Austria) for continuous recording of wave and numeric outputs. For each patient, the ECG and arterial pressures were recorded continuously. This enabled the recorded arterial pressure data to act as a synchronised reference for impedance data. An investigational monitor/defibrillator was used in the study to record thoracic impedance (Heartstart® 4000SP Laerdal Medical Cooperation, Stavanger, Norway) via commercially available selfadhesive electrode defibrillator pads (Heartstart Pads® , Philips Medical Systems, Seattle, WA, USA) in the recommended standard positions.22 The episodes were analysed offline, and segments with rhythm type of either pulseless electrical activity (PEA) or a pulse-generating rhythm where identified using the ECG recordings. If the ECG, impedance or the blood pressure trace were corrupted by noise or not present, the segment was not included in the remaining analysis. By making sure that the blood pressure was stable over the duration of the segment, we assumed that the impedance waveform resulting from each heartbeat was the same throughout the segment. Due to respiration artefacts in the impedance channel in the cardiac arrest data, we chose to use only segments with more than nine QRS complexes. This was to assure the accuracy of the technique used first isolating the circulatory-related impedance waveform to be analysed and later combining this into one representative waveform. For each segment, we now wanted to find a representation of the circulatory-related impedance waveform resulting from a heartbeat. Such descriptive parameters were the difference between the minimum and maximum value of the waveform (feature 1). This was expected to be connected with the duration of the waveform, so we let feature 2 be feature 1 divided by the duration of the segment in ms. Features 3 and 4 were the same features extracted from the derivative signal of the wave. We hypothesised that the area under the waveform changes with circulation. By setting the starting point of the wave to zero, we let feature 5 represent the area under the waveform, and let feature 6 represent the area per sample. The impedance change of the longest negative flank of the waveform was feature 7 and its duration feature 8. The impedance change per time of this flank was feature 9. The relation between the parameters describing the circulatory-related impedance waveform and the circulatory information was then studied. After thorough studies of the impedance waveform
224 of many patients, the connection between blood circulation and the impedance waveform was far more complex than can be captured by a single measurement. Therefore, in a further evaluation a pattern recognition framework was used. There we make no presumption on the connection between impedance and blood pressure-related variables, but allow the problem to be solved by a neural network. Here we used the blood pressure measurements to classify the impedance trace of an organised rhythm as either systolic arterial pressure of [Ps] > 80 mmHg or Ps ≤ 80 mmHg.23,20 To be able to compare the impedance parameters to circulation information, the variables in the blood pressure channel had to be selected. We, therefore, estimated the systolic (Ps), diastolic (Pd), peak (PP) and mean arterial pressure (MAP) over the duration of the segment.
Data analysis The extracted data for any mutual information in the impedance and blood pressure channel were now analysed in two ways.
Correlating the variables For each patient, the mean of the impedance variables and the blood pressure parameters over all segments was calculated. The sample set of the different variables were compared by computing their correlation coefficient,24 and the statistical significance of the correlation coefficient.25 The p-value indicates how likely it was that the correlation coefficient truly is 0 and that the estimation was a result of variations in the data. For values of less than 0.05, the correlation was assumed to be statistically significant.
Discriminating between patients with and without sufficient spontaneous circulation (systolic blood pressure >80 mmHg and ≤80 mmHg) A neural network was trained to classify the segments as either Ps > 80 mmHg or Ps ≤ 80 mmHg.26 The performance of a classifier was evaluated and visualised by means of receiver operating characteristic (ROC) graphs.27 The area under the ROC curve, AUC, was used as a measure of performance, which gave a general impression of the ability of the classifier to discriminate between the classes. We treated the output of the neural network as the discrimination function of observing various numbers of input features. We used a deci-
H. Losert et al. sion rule where to assign the observation to class as either Ps > 80 mmHg or Ps ≤ 80 mmHg. For a choice of decision threshold, we were able to calculate the sensitivity and specificity of our training and test set, and thereby a point on the ROC graph. Because of the sigmoid transfer function in the neural network, the output was between 0 and 1. We let decision thresholds compute points on the ROC graphs. The area under the ROC graph, AUC, was calculated for each test set. An estimate of the performance of the classifier was then calculated as the mean of the AUC over all test sets, and gave a general measure of the classifier’s performance for different variable settings. The standard deviation was also calculated to get an impression of the variation of the classifier performance. To find the best way to represent the impedance waveform, e.g. to find the best feature a greedy search was performed. First the performance in terms of AUC when using one feature was evaluated, and then the performance of all combinations of two features, where one of them was the one that gave the best performance when using only one feature. This routine was continued until all possible features had been included. We analysed the results to find the feature combination of the search procedure that gave the best discrimination power. The final result was compared to the result from using only one feature.
Results Characteristics of study subjects Due to data transfer problems between the monitor and our computer-based data analysis system 6 of the haemodynamically stable patients (n = 38) and 48 of the patients in cardiac arrest (n = 85) had to be excluded. Therefore, measurements from 32 haemodynamically stable patients and 37 cardiac arrest patients were useable. Patient characteristics, reasons for admittance, known medical history and other relevant clinical data are shown in Tables 1 and 2. The resulting data set consisted of 434 segments, 114 from 32 haemodynamically stable patients, and 320 from 37 cardiac arrest patients. Each patient is represented by a median of four segments (range 1—79), with a median duration of 15.0 s (range 5.5—40.0).
Correlating the variables In Table 3, the correlation coefficient between the impedance variables and the blood pressure vari-
Thoracic-impedance changes measured via defibrillator pads
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Table 3 The correlation coefficient between the blood pressure [systolic (Ps), diastolic (Pd), peak (PP) and mean arterial pressure (MAP)] and selected impedance waveform features (see text) Impedance variables (see text) 1 2 3 4 5 6 7 8 9 * Correlation
Blood pressure variables Ps
Pd *
0.2979 0.3299* 0.3122* 0.2998* −0.2697* −0.3351* 0.2821* −0.0237 0.2898*
PP *
0.3443 0.3326* 0.3514* 0.2961* −0.3117* −0.3537* 0.3303* −0.0353 0.3481*
MAP *
0.2380 0.3102* 0.2516* 0.2802* −0.2068 −0.2926* 0.2214* −0.0219 0.2283*
0.3431* 0.2809* 0.3448* 0.2474* −0.3162* −0.3222* 0.3336* −0.0368 0.3547*
coefficients that are statistically significant (p < 0.05).
ables are listed. We observe that many correlations are statistically significant, and that no correlations are high. Boxplots of three features with sufficient correlation coefficients with p-values are shown in Figure 1. There are trends of the impedance values being different for Ps > 80 mmHg and Ps ≤ 80 mmHg. The range of the circulation-related impedance waveform relative to its duration and the range of the derivative signal of the waveform seem to have a higher value for sufficient, than for insufficient circulation. In addition, the area per sample under that waveform, seems to have a larger negative value for sufficient, than for insufficient circulation.
Discriminating between patients with and without sufficient spontaneous circulation (Ps > 80 mmHg and ≤80 mmHg) The segments were divided in two classes according to Ps > 80 mmHg and Ps ≤ 80 mmHg; 334 of the segments were recorded as Ps > 80 mmHg and the remaining 100 as Ps ≤ 80 mmHg. The performance of the classifier for increasing number of included features is shown in Figure 2. Each point represents the mean performance of the best feature for a given number of features. As the dimensionality of the features increases, so does the discrimination ability of the classifier. The largest improvement is for going from one to three features. The benefit in spatial separation of going from one to two dimensions is easily observed. It is worth noting that several of the polynomial coefficients are chosen at an early stage of the greedy search. Other important features are related to the duration of the waveform, the derivative of the waveform and the area under the waveform. In Figure 3, the ROC graph of the classifier when using the combination of the best features is shown together with the ROC
graph of the best classifier using only one single feature. We see the improvement from increasing the feature dimensions.
Discussion Comparing variables describing the circulationrelated impedance waveform and the blood pressure measurements showed a low correlation between the two measurements. The distribution of the variables describing the circulation-related impedance waveform does however indicate that the waveform contains information that can help identify an adequate circulation. A neural networkbased classifier was able to accurately identify segments with a systolic blood pressure above 80 mmHg. The impedance signal, therefore, may have an important role in automatically identifying restoration of spontaneous circulation (ROSC) during resuscitation. By correlating features describing the thoracicimpedance waveform related to cardiac activity and arterial blood pressure, we found a significant correlation for several of the features. The correlation was however small, and it can, therefore, be concluded that these features cannot be used for estimation of blood pressure using a linear model due to different waveforms. Therefore, we used the blood pressure measurements to classify the impedance trace of an organised rhythm as either an arterial pressure of ≤80 mmHg or >80 mmHg. The assumption was that pressures >80 mmHg are an indicator of a pulse (ROSC). We found features with significantly different medians for the two subgroups. This indicates that the characteristics of the circulation-related impedance waveform contains information, that can be used to discriminate between patients with an adequate and inadequate
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Figure 2 The results in terms of area under the curve (AUC) from increasing the numbers of features by a greedy search.
Figure 1 Boxplot of the distribution of segment feature (a) 2, (b) 3 and (c) 6 for the two classes where Ps ≤ 80 and Ps > 80 mmHg (‘‘ROSC-’’ and ‘‘no-ROSC-group’’), where N is the number of samples the boxplot is based upon. For the distribution of samples representing patients, N = 16 is where Ps < 80 mmHg and N = 53 is where Ps > 80 mmHg. For the distribution of samples representing segments, N = 100 is where Ps ≤ 80 mmHg and N = 334 is where Ps > 80 mmHg. The boxes have lines at the lower quartile, median, and upper quartile values. The notches represent a robust estimate of the uncertainty about the medians for box-to-box comparison. A no notch overlap indicates that the medians of the two groups differ at the 5% significance level. The whiskers are lines extending from each end of the boxes to show the extent of the rest of the data. The whiskers extend from the box out to the most extreme data value within 1.5 times the interquartile range. Outliers are data with values beyond the ends of the whiskers.
circulation. The waveforms are generally larger and faster changing for patients with an adequate circulation than for an inadequate circulation. The current guideline statement1,28 with regards to pulse check is: ‘‘. . . the rescuer should not rely on the pulse check to determine the need for chest compressions or use of an AED.’’ The most convincing study showing the inability of health care providers to identify the presence of a carotid pulse in adults rapidly and accurately was by Eberle et al.6 The sensitivity and specificity of manual pulse check has been reported to be 90 and 55%, respectively. Other studies used normal, healthy volunteers8,3 , young volunteers29 , or anaesthetised
adults30 or infants31 to assess the presence of a pulse. These latter studies show that many healthcare providers require at least 10 s to identify the pulse with a reasonable degree of reliability (80% sensitivity) in a normal, well-perfused person. Lay rescuers performed less well.3 More recently, Lapostolle et al.32 used a computerised manikin and confirmed that fewer than 50% of 64 prehospital healthcare providers could determine the absence of a pulse accurately within 10 or 30 s. Rapid recognition of the presence of a pulse will avoid inappropriate chest compressions, but more importantly, failing to recognise the absence of a pulse will lead to death, if chest compressions and/or defibrillation is not provided because the lay rescuer thought a pulse was present. With our classifier we achieved a sensitivity of 90% for a specificity of 82% of discriminat-
Figure 3 The ROC graph for the best single feature, and the best feature combination.
Thoracic-impedance changes measured via defibrillator pads ing between segments with systolic blood pressure above or below 80 mmHg. The most discriminating information was found by using the features that describe the shape of the waveform. These are the polynomial coefficients and the area under the waveform. The range of the derivative of the waveform shape is also important, as it describes how fast the impedance changes for a heartbeat. The change in impedance is thought to arise mainly from redistribution of blood in the thorax and change of blood velocity,11 but the exact contribution of each circulatory event to the impedance waveform is not known. Djordjevich et al.33 correlated measurements from 146 volunteers of the magnitude of the negative peak of the first time derivative of the thoracic impedance, which is comparable to feature 3 in our experiments, with blood pressure measurements. A correlation coefficient of −0.567 with systolic blood pressure was found, and it was concluded that the negative peak seems to decrease with an increase in blood pressure. Our results indicate the opposite, but our correlation is smaller and the sample number is lower. In addition, there is a difference in electrode set-up and in patients. This may partly explain the different results, and illustrates the complexity involved in using impedance for circulatory measurements. It has also been stated33 that the shape and amplitude of the impedance waveform is strongly influenced by the dynamic response of the vascular system and the shape of the impulse created by the ventricular contractions. Due to cardiovascular malfunctions in cardiac arrest patients, the impedance waveform may assume even a wider variety of amplitudes and shapes. By combining features describing the impedance waveform, we were able to extract some useful information about the circulatory state of the patient. The impedance technique incorporated into an AED could be used to monitor signs of circulation in a cardiac arrest situation. It could serve as an aid to give circulation-related feedback to the rescuer and thereby improve CPR quality.34—36 There are, however, several problems to be solved before the results are clinically useable. First, the QRS complexes have to be detected automatically. Second, the circulation-related impedance waveform has to be isolated automatically before any discriminative features can be extracted. Inaccuracy in any of these tasks will lead to errors in the features extracted and a less accurate classification. Currently, a pause in compressions is necessary to allow for a pulse check. It should be possible to apply compression artefact removal strategies used in VF analysis so that identification of a rhythm with a pulse can be done during
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compressions.17 This could allow further reduction in no flow times.
Conclusions The present study showed that the impedance measurement system sensor of a defibrillator is likely to provide adequate monitoring of the presence or absence of circulation and thus shows that the impedance signal does contain information of clinical relevance that can help identify restoration of spontaneous circulation during resuscitation.
Conflict of interest Helge Myklebust and Jon Nysæther are Laerdal Medical Employees. Klemens K¨ ohler was employed for 12 months at the Department of Emergency Medicine, Medical University Vienna with support of a grant from Laerdal Medical, Stavanger, Norway.
Acknowledgments Laerdal Medical, Stavanger, Norway provided travel grants for scientific meetings for Heidrun Losert and Klemens K¨ ohler. Heidrun Losert received a laptop from Laerdal Medical, Stavanger, Norway. The study was supported in part by a commercial sponsor (Laerdal Medical, Stavanger, Norway).
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