An Adaptive Filtering based Approach for Cardiopulmonary Resuscitation Quality Assessment
Main Article Content
Abstract
Cardiac arrest is the leading cause of deaths for thousands of people every year. Immediate treatment of cardiac arrest can help reducing cardiovascular mortality rates. One of the most common approaches for treatment of cardiac arrest is cardiopulmonary resuscitation (CPR) that provides chest compressions. Quality of chest compression is considered as one of key indicators for CPR performance assessment. In this study, we present an approach for CPR quality evaluation using ECG and thoracic impedance signals via a least mean square based adaptive filter. Then, CPR quality evaluation is performed based on analyzing the spectra and frequencies of input ECG and estimated CPR signals. The proposed approach is applied for a dataset including 526 segments from patients presenting with asystole. The results are then compared with those derived from the compression depth used as reference CPR signals. Experimental results show performance of the proposed approach for assessment of CPR quality.
Keywords
Cardiopulmonary resuscitation, Spectrum analysis, Thoracic impedance, Cardiac arrest, Adaptive Filter
Article Details
References
[1]. G. Perkins, T. Olasveengen, I. Maconochie, J. Soar, J. Wyllie, R. Greif, A. Lockey, F. Semeraro, P. Van De Voorde, C. Lott, K. Monsieurs, J. Nolan, European Resuscitation Council Guidelines for Resuscitation: 2017 update, Resuscitation, vol. 123, (2018) 43-50.
[2]. S. Chen, W. Li, Z. Zhang, H. Min, H. Li, H. Wang, Zhuang Yl, Y. Chen, C. Gao, H. Peng, Evaluating the Quality of Cardiopulmonary Resuscitation in the Emergency Department by Real-Time Video Recording System, PLoS One, vol. 10, no. 10, (2015) e0139825.
[3]. B. Abella, N. Sandbo, P. Vassilatos, J. Alvarado, N. O’hearn, H. Wigder, P. Hoffman, S. K. Tynu, T. Vanden Hoek, L. Becker, Chest compression rates during cardiopulmonary resuscitation are suboptimal: a prospective study during in-hospital cardiac arrest, Circulation, vol. 111, no. 4, (2005) 428-434.
[4]. M. Lo, L. Lin, Wh. Hsieh, P. Ko, Y. Liu., C. Lin, Y. Chang., C. Wang., Y. Young., W. Chiang, J. Lin, W. Chen, M. Ma, A new method to estimate the amplitude spectrum analysis of ventricular fibrillation during cardiopulmonary resuscitation, Resuscitation, vol. 84, no. 11, (2013) 1505–1511.
[5]. U. Ayala, T. Eftestøl, E. Alonso, U. Irusta, E. Aramendi, S. Wali, J. Johansen, Automatic detection of chest compressions for the assessment of CPR quality parameters, Resuscitation, vol. 85, no. 7, (2014) 957-963.
[6]. J. Ruiz, U. Ayala, S. Ruiz De Gauna, U. Irusta, D. Otero, E. Alonso, J. Johansen, T. Eftestøl, Feasibility of automated rhythm assessment in chest compression pauses during cardiopulmonary resuscitation, Resuscitation, vol. 84, no. 9, (2013) 1223-1228.
[7]. U. Ayala, U. Irusta, J. Ruiz, S. Ruiz de Gauna . D. González-Otero, E. Alonso, J. Johansen, H. Naas, T. Eftestøl, Fully automatic rhythm analysis during chest compression pauses, Resuscitation, vol. 89, (2015) 25-30.
[8]. N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, E. H. Shih, Q. Zheng, C. C. Tung, H. H. Liu, The empirical mode decomposition method and the Hilbert spectrum for non-stationary time series analysis, Proc. R. Soc. Lond., vol. 454A, (1998) 903-995.
[9]. T.T Tran, V.T Pham, C. Lin, H. W Yang, Y.H Wang, K.K Shyu, W.Y Tseng, M.Y Su, L.Y Lin, M.T Lo, Empirical Mode Decomposition and Monogenic Signal based Approach for Quantification of Myocardial Infarction from MR Images, IEEE Journal of Biomedical and Health Informatics, (2018) DOI 10.1109/JBHI.2018.2821675.
[10]. E. Aramendi, U. Irusta, U. Ayala, H. Naas, J. Johansen, T. Eftestøl, Filtering mechanical chest compression artefacts from out-of-hospital cardiac arrest data, Resuscitation, vol. 98, (2016) 41-47.
[2]. S. Chen, W. Li, Z. Zhang, H. Min, H. Li, H. Wang, Zhuang Yl, Y. Chen, C. Gao, H. Peng, Evaluating the Quality of Cardiopulmonary Resuscitation in the Emergency Department by Real-Time Video Recording System, PLoS One, vol. 10, no. 10, (2015) e0139825.
[3]. B. Abella, N. Sandbo, P. Vassilatos, J. Alvarado, N. O’hearn, H. Wigder, P. Hoffman, S. K. Tynu, T. Vanden Hoek, L. Becker, Chest compression rates during cardiopulmonary resuscitation are suboptimal: a prospective study during in-hospital cardiac arrest, Circulation, vol. 111, no. 4, (2005) 428-434.
[4]. M. Lo, L. Lin, Wh. Hsieh, P. Ko, Y. Liu., C. Lin, Y. Chang., C. Wang., Y. Young., W. Chiang, J. Lin, W. Chen, M. Ma, A new method to estimate the amplitude spectrum analysis of ventricular fibrillation during cardiopulmonary resuscitation, Resuscitation, vol. 84, no. 11, (2013) 1505–1511.
[5]. U. Ayala, T. Eftestøl, E. Alonso, U. Irusta, E. Aramendi, S. Wali, J. Johansen, Automatic detection of chest compressions for the assessment of CPR quality parameters, Resuscitation, vol. 85, no. 7, (2014) 957-963.
[6]. J. Ruiz, U. Ayala, S. Ruiz De Gauna, U. Irusta, D. Otero, E. Alonso, J. Johansen, T. Eftestøl, Feasibility of automated rhythm assessment in chest compression pauses during cardiopulmonary resuscitation, Resuscitation, vol. 84, no. 9, (2013) 1223-1228.
[7]. U. Ayala, U. Irusta, J. Ruiz, S. Ruiz de Gauna . D. González-Otero, E. Alonso, J. Johansen, H. Naas, T. Eftestøl, Fully automatic rhythm analysis during chest compression pauses, Resuscitation, vol. 89, (2015) 25-30.
[8]. N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, E. H. Shih, Q. Zheng, C. C. Tung, H. H. Liu, The empirical mode decomposition method and the Hilbert spectrum for non-stationary time series analysis, Proc. R. Soc. Lond., vol. 454A, (1998) 903-995.
[9]. T.T Tran, V.T Pham, C. Lin, H. W Yang, Y.H Wang, K.K Shyu, W.Y Tseng, M.Y Su, L.Y Lin, M.T Lo, Empirical Mode Decomposition and Monogenic Signal based Approach for Quantification of Myocardial Infarction from MR Images, IEEE Journal of Biomedical and Health Informatics, (2018) DOI 10.1109/JBHI.2018.2821675.
[10]. E. Aramendi, U. Irusta, U. Ayala, H. Naas, J. Johansen, T. Eftestøl, Filtering mechanical chest compression artefacts from out-of-hospital cardiac arrest data, Resuscitation, vol. 98, (2016) 41-47.