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CC BY-NC-ND 4.0 · Methods Inf Med 2018; 57(S 01): e1-e9
DOI: 10.3414/ME17-01-0071
Original Articles
Schattauer GmbH

Time-frequency Features for Impedance Cardiography Signals During Anesthesia Using Different Distribution Kernels

Jesús Escrivá Muñoz
1   Biomedical Engineering Research Centre, CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Universitat Politècnica de Catalunya, Barcelona, Spain
2   Quantium Medical, Mataró (Barcelona), Spain
,
Pedro Gambús
3   Systems Pharmacology Effect Control & Modeling (SPEC-M) Research Group, Anesthesiology Department, Hospital CLINIC de Barcelona, Barcelona, Spain
4   Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA, USA
,
Erik W. Jensen
2   Quantium Medical, Mataró (Barcelona), Spain
,
Montserrat Vallverdú
1   Biomedical Engineering Research Centre, CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Universitat Politècnica de Catalunya, Barcelona, Spain
› Author AffiliationsThis work was supported by the Industrial PhD Program DI-2014/013 (Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement, Generalitat de Catalunya, Spain) and the European Social Fund of the European Union. CIBER of Bioengineering, Biomaterials and Nanomedicine is an initiative of ISCIII.
Further Information

Publication History

received: 17 July 2017

accepted: 24 October 2017

Publication Date:
23 February 2018 (online)

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Summary

Objective: This works investigates the time-frequency content of impedance cardiography signals during a propofol-remifentanil anesthesia.

Materials and Methods: In the last years, impedance cardiography (ICG) is a technique which has gained much attention. However, ICG signals need further investigation. Time-Frequency Distributions (TFDs) with 5 different kernels are used in order to analyze impedance cardiography signals (ICG) before the start of the anesthesia and after the loss of consciousness. In total, ICG signals from one hundred and thirty-one consecutive patients undergoing major surgery under general anesthesia were analyzed. Several features were extracted from the calculated TFDs in order to characterize the time-frequency content of the ICG signals. Differences between those features before and after the loss of consciousness were studied.

Results: The Extended Modified Beta Distribution (EMBD) was the kernel for which most features shows statistically significant changes between before and after the loss of consciousness. Among all analyzed features, those based on entropy showed a sensibility, specificity and area under the curve of the receiver operating characteristic above 60%.

Conclusion: The anesthetic state of the patient is reflected on linear and non-linear features extracted from the TFDs of the ICG signals. Especially, the EMBD is a suitable kernel for the analysis of ICG signals and offers a great range of features which change according to the patient’s anesthesia state in a statistically significant way.

Keywords

Time-frequency distributions - impedance cardiography - entropy - singular value decomposition

Author Contribution J Escrivá Muñoz did the data base analysis and writing of the manuscript, and P Gambús collected the cases for the data base; EW Jensen and M Vallverdú act as academic advisors in the corresponding author’s PhD program.


 

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