BioMed Research International

BioMed Research International / 2016 / Article
Special Issue

Shock, Cardiac Arrest, and Resuscitation

View this Special Issue

Research Article | Open Access

Volume 2016 |Article ID 4750643 |

Muammar Sadrawi, Wei-Zen Sun, Matthew Huei-Ming Ma, Chun-Yi Dai, Maysam F. Abbod, Jiann-Shing Shieh, "Cardiopulmonary Resuscitation Pattern Evaluation Based on Ensemble Empirical Mode Decomposition Filter via Nonlinear Approaches", BioMed Research International, vol. 2016, Article ID 4750643, 6 pages, 2016.

Cardiopulmonary Resuscitation Pattern Evaluation Based on Ensemble Empirical Mode Decomposition Filter via Nonlinear Approaches

Academic Editor: John M. Ryan
Received21 Dec 2015
Revised31 Mar 2016
Accepted26 Jun 2016
Published26 Jul 2016


Good quality cardiopulmonary resuscitation (CPR) is the mainstay of treatment for managing patients with out-of-hospital cardiac arrest (OHCA). Assessment of the quality of the CPR delivered is now possible through the electrocardiography (ECG) signal that can be collected by an automated external defibrillator (AED). This study evaluates a nonlinear approximation of the CPR given to the asystole patients. The raw ECG signal is filtered using ensemble empirical mode decomposition (EEMD), and the CPR-related intrinsic mode functions (IMF) are chosen to be evaluated. In addition, sample entropy (SE), complexity index (CI), and detrended fluctuation algorithm (DFA) are collated and statistical analysis is performed using ANOVA. The primary outcome measure assessed is the patient survival rate after two hours. CPR pattern of 951 asystole patients was analyzed for quality of CPR delivered. There was no significant difference observed in the CPR-related IMFs peak-to-peak interval analysis for patients who are younger or older than 60 years of age, similarly to the amplitude difference evaluation for SE and DFA. However, there is a difference noted for the CI (). The results show that patients group younger than 60 years have higher survival rate with high complexity of the CPR-IMFs amplitude differences.

1. Introduction

Cardiac disease and out-of-hospital cardiac arrest (OHCA) are the major healthcare problem internationally [1, 2]. Despite advances in medicine and cardiology, OHCA is still associated with a high mortality rate [3, 4]. One of the main causes of OHCA is severe ischemic heart disease, including the acute coronary artery occlusion [57]. According to Eisenberg et al., successful return of spontaneous circulation (ROSC) from OHCA is based on certain factors, such as the general condition of the patients, the type and vitality of the events, and the duration to bystander cardiopulmonary resuscitation (CPR) being delivered [8].

CPR is one of the fundamental links in the chain of survival in the management of the OHCA patients. When the connections between each other are well performed, the survival rate will increase significantly [9]. On the other hand, the unexpected cardiac rhythm can be escalated when one of these connections is postponed [10, 11]. An effective chest compression itself involves the application of the pressure to the sternum maintaining the flow of blood and oxygen to myocardium and brain [12]. The chest compression condition is a dominant index of the CPR accomplishment [1315]. In order to evaluate the CPR data, the noise is an essential concern. A filtering method can be performed in order to extract the correct information from the continuous signal. The use of empirical mode decomposition (EMD) filtering algorithm, proposed by Huang et al. [16, 17], has been used to filter signal problems, such as EMD-based filters which have also been used for narrow-band signals such as electrocardiography (ECG) [18] and blood pressure [19].

In advance, the filtered signal is extracted to achieve the information containing its characteristics. One of these methods, the entropy algorithm, was used in information theory [20] to address the nonlinearity problems. An entropy algorithm was also applied to the ECG signal studies [21, 22]. In a study by Costa et al., extended sample entropy was applied to evaluate the feature extraction of the ECG using multiscale entropy [23]. Another nonlinear method, detrended fluctuation analysis (DFA), was originally utilized for the DNA sequence [24].

Studies related to purifying the signal and extracting information for the cardiac arrest cases have been done for several years. A study utilizing a multichannel Wiener filter and a matching pursuit-like method is conducted to remove CPR artifact from the ECG trace [25]. Least mean-square (LSM) filtering has also been utilized to remove the CPR problem [26]. A new method combining the noise-assisted multivariable EMD (N-A MEMD) and LSM filtering was implemented by Lo et al. [27]. Furthermore, the application of the sample entropy has been utilized for shock outcome prediction [28] as well as multiscale entropy [29]. Detrended fluctuation analysis was utilized by Lin et al. for the study of ventricular fibrillation in OHCA cases [30]. The purpose of this study is to evaluate the CPR pattern by utilizing the EEMD to purify the CPR signal and the ECG data by applying the nonlinear algorithms to see the survival rate.

2. Data Acquisition and Algorithm

2.1. Data Acquisition

The dataset is retrospectively collected from the New Taipei City fire-based of emergency medical service (EMS). All the staff have been trained for the basic life support, early defibrillation, and advanced life support. All the ambulance units are equipped with a ForeRunner AED (Philips, Seattle, WA, USA). The ECG signal is logged into the AED card data, sampled for 200 Hz. The logging lead was placed on the patient chest [27].

This study has utilized data from the whole year of 2010. A total of 1207 patient ECGs are divided into two groups: trauma and non-trauma cardiac arrest. Focusing on the non-trauma patients only, the data is divided into another two groups: patients who had an AED shock and non-shock-able signal patients. In order to evaluate the pure CPR without any help of the AED, all the 1001 non-shock-able patients, which eventually becomes 951 sets after filtering for the quality of the data, are divided according to their age with the threshold of 60 years, as shown in Figure 1. The outcome of the patient is evaluated after 2 hours based on their conditions. The results are analyzed in MATLAB language (Mathwork Inc.).

2.2. Empirical Mode Decomposition-Based Filter
2.2.1. Empirical Mode Decomposition

EMD is an algorithm to decompose the specific frequency range of the data into a finite number of intrinsic mode functions (IMFs). These decomposed IMFs illustrate certain characteristics. However, for the real-world signals, the mode-mixing disturbs the regularity of the IMFs. For this reason, the ensemble empirical mode decomposition (EEMD) was proposed.

2.2.2. Ensemble Empirical Mode Decomposition

The intermittence corrupts the consistence of the IMFs. The subsequent mode function will be affected, hence the physical meaning of those IMFs that cannot be parted based on their characteristics. Wu and Huang [31] proposed EEMD using noise-assisted method to overcome this phenomenon. In EEMD, the white noise is added to the original signal to form a mixed combination of noise and signal in order to remove the intermittence and generate consistent IMFs. EEMD study was also conducted to an ECG noise filtering problem [32].

2.3. Feature Extraction Algorithms
2.3.1. Sample Entropy and Complexity Index

Entropy is known as a thermodynamics property in the evaluation of regularity. The higher the entropy means the less regular the pattern or the sequence to be recognized. For the multiscale entropy, the coarse grained time series is based on the scale factor [23]. The coarse grained time series will be evaluated by entropy algorithm. The result of the entropy corresponds to each scale which is called multiscale entropy. The complexity index (CI) is defined as measurement of the signal complexity. It is calculated by the evaluation of the area under curve of the multiscale entropy. The calculation from the recreated time series based on the coarse grained information will affect the area under the area of the curve.

2.3.2. Detrended Fluctuation Analysis

Fractal analysis is one of the most prosperous methods to get the signal features. Detrended fluctuation analysis (DFA) is a nonstationary algorithm for statistical analysis. A considerably physiology-related problem is a nonstationary time series one. This method is originally proposed by Peng et al. [24].

3. Results and Discussion

In this study, the original ECG logged from the AED machine was filtered by the EEMD algorithm, shown in Figures 24. From these figures, it can be seen that IMF 2 to IMF 4 are relatively similar to the CPR pattern having the dominant frequency as described in previous study conducted by Lo et al. [27]. Figures 5 and 6 also show the time-frequency evaluation; this shows the differences between the raw ECG and the reconstructed-CPR signal after the EEMD filter by combining the CPR-related IMFs. Figures 5(a) and 6(a) give the information about the time-frequency information. For Figure 5(a), the dominant signal occurs mostly below the CPR frequency ranges, lower than 0.5 Hz, indicated by the red area. Meanwhile, for Figure 6(a), after the EEMD filter, the dominant frequency shifts to the range of 2 Hz to 4 Hz, indicated by the red square. This filter also automatically reduces the baseline noise of the signal that can be seen by Figures 5(b) and 6(b).

All the maxima points are detected from the reconstructed IMFs that have the CPR frequency, by evaluating the changing of the slopes from positive to negative as shown in Figure 7. Furthermore, the maxima points are evaluated to obtain the maxima interval () and maxima amplitude differences (dA) from the IMF-combined CPR, shown in Figure 7. Furthermore, both signals, and dA, are estimated by utilizing SE, CI, and DFA.

Evaluation of the results of the 951 ECGs from patients of non-trauma OHCAs with a non-shock-able rhythm using a threshold of 60 years of age is shown in Table 1. A subgroup analysis is performed which begins for patients greater than 60 years of age: of this category 579 patients died and 116 patients survived. The mean SE value is and for dead and surviving patients, respectively (). CI for patients who died is and for those who survived is (). The DFA evaluation is for patients who have died and for those who have survived ().

EvaluationAgeFeatureStatusMeanStandard deviation value ()

Interval ≥60 (579,116)SEDied1.910.580.556

Interval<60 (215,41)SEDied1.860.610.575

Amplitude ≥60 (579,116)SEDied0.220.2360.825

Amplitude<60 (215,41)SEDied0.20.230.28

Note. SE means sample entropy, CI complexity index, and DFA detrended fluctuation analysis; significant different parameter.

A further subgroup analysis is performed for patients under 60 years of age. The total number of the patients for this class is less than half of the number of the patients older than 60 years. The observed SE is and , respectively, for the patients who have died compared to those who have survived (). The CI is and , respectively, for patients who have died compared to those who have survived. The DFA is and , respectively, for patients who died and survived and also not significantly different.

On assessment of the amplitude difference, for patients aged 60 or over, patients who died had a mean SE value of and for the patients who have survived, the results are (). CI for patients who have died is versus for those who have survived ().

For cases of the category of age of less than 60 years, the SE has and , respectively, of patients who have died and are alive and has no significant differences. The CI has and , respectively, for those who died and survived; this case is significantly different (). The DFA case creates and ().

In terms of the relationship of this result to the OHCA for the future applications, the focus is the automated CPR machine. According to a study by Steen et al., the automated CPR machine was very advantageous in performing the chest compression during transportation way [33]. The automated CPR also produced better pressure of end tidal carbon dioxide [34] and cortical blood flow [35] compared to the manual CPR. However, a study with 4471 patients conducted by Perkins et al., with the consistent rate and depth, shows the automated CPR is not significantly different from the manual CPR with the main outcome being the survival rate after 30 days of OHCA [36]. In another study by Smekal et al., evaluated automated and manual CPR for 75 and 73 patients, respectively, also provided no significant difference [37]. Also, Hallstrom et al. investigated a total of 554 and 517 for automated CPR and manual CPR, respectively. This study found that the automated CPR reduced the survival and made the neurological outcome worse [38]. The controversial results of the previous studies of automated and manual CPR may be due to the consistent amplitude of the automated CPR machines. By referring to our study’s results, not that consistent depth for the CPR amplitude, which can be implemented into the CPR machine for the future tests, may increase the survival rate.

4. Conclusions and Future Work

This study evaluates a total of 951 of the non-shock-able patient ECGs, using the ensemble empirical mode decomposition filtering and utilizing nonlinear approaches. The IMF-combined CPR maxima interval and the amplitude are evaluated. For most of the observations, there were no statistically significant differences observed. However, in the evaluation of CI for the maximal amplitude, a statistically significant difference was observed.

Based on the results, it can be concluded that for patients who are less than 60 years of age a higher survival rate was observed and was associated with more complexity in CPR amplitude differences. This result can have information that the automated CPR machine with the dynamic force may be a consideration.

This study has several limitations. Namely, when the noise interference occurred at the same frequency range of the CPR IMFs, they were included in the evaluation. This may somewhat affect the observations, especially for the slope evaluation. Furthermore, there were far more observations for patients who died than for those who survived.

For future study, the application of the advanced time-domain filter may be applied to purify the unfiltered noise on the frequency domain filter.

Competing Interests

The authors declare no conflict of interests.


The authors wish to thank National Taiwan University Hospital (NTUH) doctors, nurses, and other officials who have given their best help for this research. This research is financially supported by the Ministry of Science and Technology (MOST) of Taiwan (MOST103-2627-M-155-001).


  1. J. Berdowski, R. A. Berg, J. G. P. Tijssen, and R. W. Koster, “Global incidences of out-of-hospital cardiac arrest and survival rates: systematic review of 67 prospective studies,” Resuscitation, vol. 81, no. 11, pp. 1479–1487, 2010. View at: Publisher Site | Google Scholar
  2. G. Nichol, E. Thomas, C. W. Callaway et al., “Regional variation in out-of-hospital cardiac arrest incidence and outcome,” The Journal of the American Medical Association, vol. 300, no. 12, pp. 1423–1431, 2008. View at: Publisher Site | Google Scholar
  3. R. J. Myerburg, K. M. Kessler, and A. Castellanos, “Sudden cardiac death (structure, function, and time-dependent of risk),” Circulation, vol. 85, no. 1, pp. l2–I10, 1992. View at: Google Scholar
  4. D. P. Zipes and H. J. J. Wellens, “Sudden cardiac death,” Circulation, vol. 98, no. 21, pp. 2334–2351, 1998. View at: Publisher Site | Google Scholar
  5. H. V. Huikuri, A. Castellanos, and R. J. Myerburg, “Sudden death due to cardiac arrhythmias,” The New England Journal of Medicine, vol. 345, no. 20, pp. 1473–1482, 2001. View at: Publisher Site | Google Scholar
  6. C. Atwood, M. S. Eisenberg, J. Herlitz, and T. D. Rea, “Incidence of EMS-treated out-of-hospital cardiac arrest in Europe,” Resuscitation, vol. 67, no. 1, pp. 75–80, 2005. View at: Publisher Site | Google Scholar
  7. C. M. Spaulding, L. M. Joly, A. Rosenberg et al., “Immediate coronary angiography in survivors of out-of-hospital cardiac arrest,” The New England Journal of Medicine, vol. 336, no. 23, pp. 1629–1633, 1997. View at: Publisher Site | Google Scholar
  8. M. S. Eisenberg, L. Bergner, and A. Hallstrom, “Out-of-hospital cardiac arrest: improved survival with paramedic services,” The Lancet, vol. 315, no. 8172, pp. 812–815, 1980. View at: Publisher Site | Google Scholar
  9. T. D. Rea, M. Helbock, S. Perry et al., “Increasing use of cardiopulmonary resuscitation during out-of-hospital ventricular fibrillation arrest: survival implications of guideline changes,” Circulation, vol. 114, no. 25, pp. 2760–2765, 2006. View at: Publisher Site | Google Scholar
  10. T. D. Rea, M. S. Eisenberg, G. Sinibaldi, and R. D. White, “Incidence of EMS-treated out-of-hospital cardiac arrest in the United States,” Resuscitation, vol. 63, no. 1, pp. 17–24, 2004. View at: Publisher Site | Google Scholar
  11. M. S. Eisenberg, B. T. Horwood, R. O. Cummins, R. Reynolds-Haertle, and T. R. Hearne, “Cardiac arrest and resuscitation: a tale of 29 cities,” Annals of Emergency Medicine, vol. 19, no. 2, article R32, pp. 179–186, 1990. View at: Publisher Site | Google Scholar
  12. M. D. Berg, S. M. Schexnayder, L. Chameides et al., “Pediatric basic life support: 2010 American Heart Association guidelines for cardiopulmonary resuscitation and emergency cardiovascular care,” Pediatrics, vol. 126, no. 5, pp. e1345–e1360, 2010. View at: Publisher Site | Google Scholar
  13. L. Wik, J. Kramer-Johansen, H. Myklebust et al., “Quality of cardiopulmonary resuscitation during out-of-hospital cardiac arrest,” The Journal of the American Medical Association, vol. 293, no. 3, pp. 299–304, 2005. View at: Publisher Site | Google Scholar
  14. B. S. Abella, N. Sandbo, P. Vassilatos et al., “Chest compression rates during cardiopulmonary resuscitation are suboptimal: a prospective study during in-hospital cardiac arrest,” Circulation, vol. 111, no. 4, pp. 428–434, 2005. View at: Publisher Site | Google Scholar
  15. V. T. D. Valenzuela, K. B. Kern, L. L. Clark et al., “Interruptions of chest compressions during emergency medical systems resuscitation,” Circulation, vol. 112, no. 9, pp. 1259–1265, 2005. View at: Publisher Site | Google Scholar
  16. N. E. Huang, Z. Shen, S. R. Long et al., “The empirical mode decomposition and the Hubert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995, 1998. View at: Publisher Site | Google Scholar
  17. Z. Wu and N. E. Huang, “On the filtering properties of the empirical mode decomposition,” Advances in Adaptive Data Analysis, vol. 2, no. 4, pp. 397–414, 2010. View at: Publisher Site | Google Scholar
  18. R. Balocchi, D. Menicucci, E. Santarcangelo et al., “Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition,” Chaos, Solitons and Fractals, vol. 20, no. 1, pp. 171–177, 2004. View at: Publisher Site | Google Scholar
  19. W. Huang, Z. Shen, N. E. Huang, and Y. C. Fung, “Engineering analysis of biological variables: an example of blood pressure over 1 day,” Proceedings of the National Academy of Sciences of the United States of America, vol. 95, no. 9, pp. 4816–4821, 1998. View at: Publisher Site | Google Scholar
  20. R. E. Blahut, Principles and Practice of Information Theory, Addison-Wesley, New York, NY, USA, 1987.
  21. E. Farahabadi, A. Farahabadi, H. Rabbani, A. M. Dehnavi, and M. P. Mahjoob, “An entropy-based method for ischemia diagnosis using ECG signal in wavelet domain,” in Proceedings of the IEEE 10th International Conference on Signal Processing (ICSP '10), pp. 195–198, IEEE, Beijing, China, October 2010. View at: Publisher Site | Google Scholar
  22. A. Singh, J. Liu, and J. V. Guttag, “Discretization of continuous ECG based risk metrics using asymmetric and warped entropy measures,” Computing in Cardiology, vol. 37, pp. 473–476, 2010. View at: Google Scholar
  23. M. Costa, A. L. Goldberger, and C.-K. Peng, “Multiscale entropy analysis of biological signals,” Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, vol. 71, no. 2, Article ID 021906, 2005. View at: Publisher Site | Google Scholar
  24. C.-K. Peng, S. V. Buldyrev, S. Havlin, M. Simons, H. E. Stanley, and A. L. Goldberger, “Mosaic organization of DNA nucleotides,” Physical Review E, vol. 49, no. 2, pp. 1685–1689, 1994. View at: Publisher Site | Google Scholar
  25. J. H. Husøy, J. Eilevstjønn, T. Eftestøl, S. O. Aase, H. Myklebust, and P. A. Steen, “Removal of cardiopulmonary resuscitation artifacts from human ECG using an efficient matching pursuit-like algorithm,” IEEE Transactions on Biomedical Engineering, vol. 49, no. 11, pp. 1287–1298, 2002. View at: Publisher Site | Google Scholar
  26. U. Irusta, J. Ruiz, S. R. de Gauna, T. Eftestøl, and J. Kramer-Johansen, “A least mean-square filter for the estimation of the cardiopulmonary resuscitation artifact based on the frequency of the compressions,” IEEE Transactions on Biomedical Engineering, vol. 56, no. 4, pp. 1052–1062, 2009. View at: Publisher Site | Google Scholar
  27. M.-T. Lo, L.-Y. Lin, W.-H. Hsieh et al., “A new method to estimate the amplitude spectrum analysis of ventricular fibrillation during cardiopulmonary resuscitation,” Resuscitation, vol. 84, no. 11, pp. 1505–1511, 2013. View at: Publisher Site | Google Scholar
  28. B. Chicote, U. Irusta, E. Aramendi, D. Alonso, C. Jover, and C. Corcuera, “Sample entropy as a shock outcome predictor during basis life support,” in Proceedings of the Computing in Cardiology Conference (CinC '15), pp. 557–560, Nice, France, September 2015. View at: Publisher Site | Google Scholar
  29. X. Kang, X. Jia, R. G. Geocadin, N. V. Thakor, and A. Maybhate, “Multiscale entropy analysis of eeg for assessment of post-cardiac arrest neurological recovery under hypothermia in rats,” IEEE Transactions on Biomedical Engineering, vol. 56, no. 4, pp. 1023–1031, 2009. View at: Publisher Site | Google Scholar
  30. L.-Y. Lin, M.-T. Lo, P. C.-I. Ko et al., “Detrended fluctuation analysis predicts successful defibrillation for out-of-hospital ventricular fibrillation cardiac arrest,” Resuscitation, vol. 81, no. 3, pp. 297–301, 2010. View at: Publisher Site | Google Scholar
  31. Z. H. Wu and N. E. Huang, “A study of the characteristics of white noise using the empirical mode decomposition method,” Proceedings of the Royal Society of London A: Mathematical and Physical Sciences, vol. 460, no. 2046, pp. 1597–1611, 2004. View at: Publisher Site | Google Scholar
  32. K.-M. Chang, “Arrhythmia ECG noise reduction by ensemble empirical mode decomposition,” Sensors, vol. 10, no. 6, pp. 6063–6080, 2010. View at: Publisher Site | Google Scholar
  33. S. Steen, Q. Liao, L. Pierre, A. Paskevicius, and T. Sjöberg, “Evaluation of LUCAS, a new device for automatic mechanical compression and active decompression resuscitation,” Resuscitation, vol. 55, no. 3, pp. 285–299, 2002. View at: Publisher Site | Google Scholar
  34. C. Axelsson, T. Karlsson, Å. B. Axelsson, and J. Herlitz, “Mechanical active compression–decompression cardiopulmonary resuscitation (ACD-CPR) versus manual CPR according to pressure of end tidal carbon dioxide (PETCO2) during CPR in out-of-hospital cardiac arrest (OHCA),” Resuscitation, vol. 80, no. 10, pp. 1099–1103, 2009. View at: Publisher Site | Google Scholar
  35. S. Rubertsson and R. Karlsten, “Increased cortical cerebral blood flow with LUCAS; a new device for mechanical chest compressions compared to standard external compressions during experimental cardiopulmonary resuscitation,” Resuscitation, vol. 65, no. 3, pp. 357–363, 2005. View at: Publisher Site | Google Scholar
  36. G. D. Perkins, R. Lall, T. Quinn et al., “Mechanical versus manual chest compression for out-of-hospital cardiac arrest (PARAMEDIC): a pragmatic, cluster randomised controlled trial,” The Lancet, vol. 385, no. 9972, pp. 947–955, 2015. View at: Publisher Site | Google Scholar
  37. D. Smekal, J. Johansson, T. Huzevka, and S. Rubertsson, “A pilot study of mechanical chest compressions with the LUCAS device in cardiopulmonary resuscitation,” Resuscitation, vol. 82, no. 6, pp. 702–706, 2011. View at: Publisher Site | Google Scholar
  38. A. Hallstrom, T. D. Rea, M. R. Sayre et al., “Manual chest compression vs use of an automated chest compression device during resuscitation following out-of-hospital cardiac arrest: a randomized trial,” The Journal of the American Medical Association, vol. 295, no. 22, pp. 2620–2628, 2006. View at: Publisher Site | Google Scholar

Copyright © 2016 Muammar Sadrawi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Related articles

No related content is available yet for this article.
 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder

Related articles

No related content is available yet for this article.

Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.