Skip to content
Microsystems, Electronics and Acoustics
ISSN 2523-4447 · e‑ISSN 2523-4455 Open Access · CC BY-SA 4.0
Vol. 23 · Issue 4 · 2018 Aug 31, 2018 Electronic Systems and Signals

Techniques and Methods for Biosignal Analysis for Monitoring the Depth of Anesthesia

MF
Maksym M. Fedorchuk Corresponding Національний Технічний Університет України "Київський Політехнічний Інститут імені Ігоря Сікорського" Ukraine
AP
Anton Oleksandrovych Popov Національний Технічний Університет України "Київський Політехнічний Інститут імені Ігоря Сікорського" Ukraine
Pages12-21 PublishedAug 31, 2018
PDF (Ukrainian)
VOL 23 · ISS 4 · 2018 View issue

Abstract

In this paper present the main existing methods and means of determining the depth of anesthesia. Among the methods considered, the following groups were distinguished: analysis of electroencephalograms (EEG), analysis of electrocardiograms (ECG), and complex analysis of biosignals. The results obtained in previous studies are systematized and prospective directions for further research are determined, in particular the use of machine learning tools for the analysis of electrocardiograms and their characteristics and training of deep neural networks. In order to study the capabilities of neural networks as systems for classifying biosignals, a deep convolution neural network was constructed to classify ECG signals with the presence or absence of arrhythmias. Input data for those network were 30-second ECG signals without preliminary processing, and the classification accuracy was 68.3%. The results of such a classification system indicate the advisability of using deep training to determine arrhythmias, and the need for preprocessing ECG and pre-identified characteristics, among which can be the magnitude of the signal energy in different frequency ranges, the signal after the removal of the trend and the signal parameters in the time domain. Current studies in the field of determining the depth of anesthesia by analyzing EEG and ECG signals make not only the technical limitations of currently used technologies, but also the fact that the number of anesthetics having a unique effect on the brain and its signals is constantly increasing. Among the technical constraints, the sensitivity of sedimentation depth to noise is the most important and the unequal response to anesthetics of different types. In this direction, many studies are devoted to improving the accuracy of determining the depth of the nerve system, by analyzing the EEG, EEG and ECG signals, only the ECG, and through the comprehensive analysis of the signals that can be obtained from the patient. In many studies, the authors conclude that alternative methods for determining the depth of a patient's sedation have better results, but at the same time they are insufficiently tested or have other technical limitations. A promising technology for determining the depth of nerve sedation via ECG analysis may also be the use of neural networks, since such systems are capable of training on labeled data, they can classify data in conditions of unknown patterns and are resistant to noise in incoming signals. Simultaneously with the use of neural networks perspective, one can use the methods of their analysis, as classification systems, in order to identify the best configurations of their parameters. Systems for the analysis of classification systems can serve both for selecting the best of them and for their adaptive combination in order to improve the classification result. Ref. 54, tabl. 1.

Keywords

#biosignal analysis #depth of anesthesia #electroencephalogram #heart rate variability #electrocardiogram features #neural networks

References

  1. T. G. Short, K. Leslie, M. T. V. Chan, D. Campbell, C. Frampton, and P. Myles, “Rationale and Design of the Balanced Anesthesia Study,” Anesth. Analg., vol. 121, no. 2, pp. 357–365, 2015, PMID: 25993386, DOI: 10.1213/ANE.0000000000000797.
  2. M. D. Kertai et al., “Association of Perioperative Risk Factors and Cumulative Duration of Low Bispectral Index with Intermediate-term Mortality after Cardiac Surgery in the B-Unaware Trial,” Anesthesiology, vol. 112, no. 5, pp. 1116–1127, May 2010, PMID: 20418692, DOI: 10.1097/ALN.0b013e3181d5e0a3.
  3. M. D. Kertai et al., “Bispectral Index Monitoring, Duration of Bispectral Index Below 45, Patient Risk Factors, and Intermediate-term Mortality after Noncardiac Surgery in the B-Unaware Trial,” Anesthesiology, vol. 114, no. 3, pp. 545–556, Mar. 2011, PMID: 21293252, DOI: 10.1097/ALN.0b013e31820c2b57.
  4. M.-L. Lindholm et al., “Mortality Within 2 Years After Surgery in Relation to Low Intraoperative Bispectral Index Values and Preexisting Malignant Disease,” Anesth. Analg., vol. 108, no. 2, pp. 508–512, Feb. 2009, PMID: 19151279, DOI: 10.1213/ane.0b013e31818f603c.
  5. T. G. Monk, V. Saini, B. C. Weldon, and J. C. Sigl, “Anesthetic Management and One-Year Mortality After Noncardiac Surgery,” Anesth. Analg., vol. 100, no. 1, pp. 4–10, Jan. 2005, PMID: 15616043, DOI: 10.1213/01.ANE.0000147519.82841.5E.
  6. K. Leslie, M. T. V. Chan, P. S. Myles, A. Forbes, and T. J. McCulloch, “Posttraumatic Stress Disorder in Aware Patients from the B-Aware Trial,” Anesth. Analg., vol. 110, no. 3, pp. 823–828, Mar. 2010, PMID: 19861364, DOI: 10.1213/ANE.0b013e3181b8b6ca.
  7. P. S. Sebel et al., “The Incidence of Awareness During Anesthesia: A Multicenter United States Study,” Anesth. Analg., vol. 99, no. 3, pp. 833–839, Sep. 2004, PMID: 15333419, DOI: 10.1213/01.ANE.0000130261.90896.6C.
  8. H. C. Hemmings, M. H. Akabas, P. A. Goldstein, J. R. Trudell, B. A. Orser, and N. L. Harrison, “Emerging molecular mechanisms of general anesthetic action,” Trends Pharmacol. Sci., vol. 26, no. 10, pp. 503–510, Oct. 2005, PMID: 16126282, DOI: 10.1016/j.tips.2005.08.006.
  9. P.-L. Chau, “New insights into the molecular mechanisms of general anaesthetics,” Br. J. Pharmacol., vol. 161, no. 2, pp. 288–307, Sep. 2010, PMID: 20735416, DOI: 10.1111/j.1476-5381.2010.00891.x.
  10. P. Gifani, H. R. Rabiee, M. H. Hashemi, P. Taslimi, and M. Ghanbari, “Optimal fractal-scaling analysis of human EEG dynamic for depth of anesthesia quantification,” J. Franklin Inst., vol. 344, no. 3–4, pp. 212–229, May 2007, DOI: 10.1016/j.jfranklin.2006.08.004.
  11. M. Mäenpää et al., “Delta Entropy of Heart Rate Variability Along with Deepening Anesthesia,” Anesth. Analg., vol. 112, no. 3, pp. 587–592, Mar. 2011, PMID: 21233497, DOI: 10.1213/ANE.0b013e318208074d.
  12. B.-R. Lee, D.-O. Won, K.-S. Seo, H. J. Kim, and S.-W. Lee, “Classification of wakefulness and anesthetic sedation using combination feature of EEG and ECG,” in 2017 5th International Winter Conference on Brain-Computer Interface (BCI), 2017, pp. 88–90, DOI: 10.1109/IWW-BCI.2017.7858168.
  13. I. J. Rampil, “A primer for EEG signal processing in anesthesia.,” Anesthesiology, vol. 89, no. 4, pp. 980–1002, Oct. 1998, PMID: 9778016.
  14. H. Viertiö-Oja et al., “Description of the Entropy algorithm as applied in the Datex-Ohmeda S/5 Entropy Module.,” Acta Anaesthesiol. Scand., vol. 48, no. 2, pp. 154–61, Feb. 2004, PMID: 14995936.
  15. D. Drover and H. R. Ortega, “Patient state index.,” Best Pract. Res. Clin. Anaesthesiol., vol. 20, no. 1, pp. 121–8, Mar. 2006, PMID: 16634419.
  16. S. Kreuer and W. Wilhelm, “The Narcotrend monitor.,” Best Pract. Res. Clin. Anaesthesiol., vol. 20, no. 1, pp. 111–9, Mar. 2006, PMID: 16634418.
  17. Q. Liu, Y.-F. Chen, S.-Z. Fan, M. F. Abbod, and J.-S. Shieh, “Quasi-Periodicities Detection Using Phase-Rectified Signal Averaging in EEG Signals as a Depth of Anesthesia Monitor,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 25, no. 10, pp. 1773–1784, Oct. 2017, DOI: 10.1109/TNSRE.2017.2690449.
  18. A. Shalbaf, M. Saffar, J. W. Sleigh, and R. Shalbaf, “Monitoring the Depth of Anesthesia Using a New Adaptive Neurofuzzy System,” IEEE J. Biomed. Heal. Informatics, vol. 22, no. 3, pp. 671–677, May 2018, DOI: 10.1109/JBHI.2017.2709841.
  19. Soo-young Ye and Do-un Jeong, “Comparison of PSD of HRV and DFA of EEG during general anesthesia,” in 5th International Conference on Computer Sciences and Convergence Information Technology, 2010, pp. 560–565, DOI: 10.1109/ICCIT.2010.5711118.
  20. Y. Ren et al., “System of Multi-parameter for Anaesthesia depth monitor,” in International Symposium on Bioelectronics and Bioinformations 2011, 2011, pp. 45–48, DOI: 10.1109/ISBB.2011.6107641.
  21. Y. Shiogai, M. Dhamala, K. Oshima, and M. Hasler, “Cortico-Cardio-Respiratory Network Interactions during Anesthesia,” PLoS One, vol. 7, no. 9, p. e44634, Sep. 2012, DOI: 10.1371/journal.pone.0044634.
  22. Soo-young Ye and Do-un Jeong, “Relation between heart rate variability and pulse transit time according to anesthetic concentration,” in 5th International Conference on Computer Sciences and Convergence Information Technology, 2010, pp. 566–569, DOI: 10.1109/ICCIT.2010.5711119.
  23. M. Sadrawi, S.-Z. Fan, M. F. Abbod, K.-K. Jen, and J.-S. Shieh, “Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks,” Biomed Res. Int., vol. 2015, pp. 1–13, 2015, DOI: 10.1155/2015/536863.
  24. S. B. Nagaraj et al., “Automatic Classification of Sedation Levels in ICU Patients Using Heart Rate Variability,” Crit. Care Med., vol. 44, no. 9, pp. e782–e789, Sep. 2016, PMID: 27035240, DOI: 10.1097/CCM.0000000000001708.
  25. P. F. Prior, Monitornyy kontrol’ funktsiy mozga: nepreryvanaya registratsiya elektricheskoy aktivnosti mozga [Monitor control of brain function: continuous recording of brain electrical activity]. Moscow, USSR, 1982.
  26. G. Barr, J. G. Jakobsson, A. Owall, and R. E. Anderson, “Nitrous oxide does not alter bispectral index: study with nitrous oxide as sole agent and as an adjunct to i.v. anaesthesia.,” Br. J. Anaesth., vol. 82, no. 6, pp. 827–30, Jun. 1999, PMID: 10562773.
  27. J. W. Johansen and P. S. Sebel, “Development and clinical application of electroencephalographic bispectrum monitoring.,” Anesthesiology, vol. 93, no. 5, pp. 1336–44, Nov. 2000, PMID: 11046224.
  28. M. Messner, U. Beese, J. Romstöck, M. Dinkel, and K. Tschaikowsky, “The bispectral index declines during neuromuscular block in fully awake persons.,” Anesth. Analg., vol. 97, no. 2, p. 488–91, table of contents, Aug. 2003, PMID: 12873942.
  29. F. S. Glumcher et al., Rukovodstvo po anesteziologii [Anesthesiology Guide], 2ed ed. Kyiv, Ukraine: VSI “Meditsina,” 2010, ISBN: 978-617-505-020-0.
  30. S. Pilge, R. Zanner, G. Schneider, J. Blum, M. Kreuzer, and E. F. Kochs, “Time delay of index calculation: analysis of cerebral state, bispectral, and narcotrend indices.,” Anesthesiology, vol. 104, no. 3, pp. 488–94, Mar. 2006, PMID: 16508396.
  31. C. Bandt and B. Pompe, “Permutation Entropy: A Natural Complexity Measure for Time Series,” Phys. Rev. Lett., vol. 88, no. 17, p. 174102, Apr. 2002, PMID: 12005759, DOI: 10.1103/PhysRevLett.88.174102.
  32. A. Plastino and O. A. Rosso, “Entropy and statistical complexity in brain activity,” Europhys. News, vol. 36, no. 6, pp. 224–228, Nov. 2005, DOI: 10.1051/epn:2005614.
  33. J. Kortelainen, E. Väyrynen, and T. Seppänen, “Depth of Anesthesia During Multidrug Infusion: Separating the Effects of Propofol and Remifentanil Using the Spectral Features of EEG,” IEEE Trans. Biomed. Eng., vol. 58, no. 5, pp. 1216–1223, May 2011, PMID: 21216702, DOI: 10.1109/TBME.2010.2103560.
  34. T. T. Nguyen-Ky, Peng Wen, Y. Li, and R. Gray, “Measuring and Reflecting Depth of Anesthesia Using Wavelet and Power Spectral Density,” IEEE Trans. Inf. Technol. Biomed., vol. 15, no. 4, pp. 630–639, Jul. 2011, DOI: 10.1109/TITB.2011.2155081.
  35. S. B. Nagaraj et al., “Patient-Specific Classification of ICU Sedation Levels From Heart Rate Variability*,” Crit. Care Med., vol. 45, no. 7, pp. e683–e690, Jul. 2017, DOI: 10.1097/CCM.0000000000002364.
  36. D. L. Hudson and M. E. Cohen, Neural Networks and Artificial Intelligence for Biomedical Engineering. Wiley-IEEE Press, 1999, ISBN: 978-0780334045.
  37. M. E. Cohen and D. L. Hudson, “Neural Network Models for Biosignal Analysis,” in 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, 2006, pp. 3537–3540, DOI: 10.1109/IEMBS.2006.260393.
  38. A. L. Goldberger et al., “PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.,” Circulation, vol. 101, no. 23, pp. E215-20, Jun. 2000, PMID: 10851218.
  39. S. Min, B. Lee, and S. Yoon, “Deep learning in bioinformatics,” Brief. Bioinform., p. bbw068, Jul. 2016, PMID: 27473064, DOI: 10.1093/bib/bbw068.
  40. S. Stober, D. J. Cameron, and J. A. Grahn, “Classifying EEG Recordings of Rhythm Perception,” in ISMIR, 2014.
  41. S. Stober, D. J. Cameron, and J. A. Grahn, “Using Convolutional Neural Networks to Recognize Rhythm Stimuli from Electroencephalography Recordings,” in Advances in Neural Information Processing Systems 27, 2014, pp. 1449–1457, URL: http://papers.nips.cc/paper/5272-using-convolutional-neural-networks-to-recognize-rhythm-stimuli-from-electroencephalography-recordings.pdf .
  42. H. Cecotti and A. Graeser, “Convolutional Neural Network with embedded Fourier Transform for EEG classification,” in 2008 19th International Conference on Pattern Recognition, 2008, pp. 1–4, DOI: 10.1109/ICPR.2008.4761638.
  43. H. Cecotti and A. Graser, “Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 3, pp. 433–445, Mar. 2011, DOI: 10.1109/TPAMI.2010.125.
  44. M. Soleymani, S. Asghari-Esfeden, M. Pantic, and Y. Fu, “Continuous emotion detection using EEG signals and facial expressions,” in 2014 IEEE International Conference on Multimedia and Expo (ICME), 2014, pp. 1–6, DOI: 10.1109/ICME.2014.6890301.
  45. S. Kiranyaz, T. Ince, and M. Gabbouj, “Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks,” IEEE Trans. Biomed. Eng., vol. 63, no. 3, pp. 664–675, Mar. 2016, DOI: 10.1109/TBME.2015.2468589.
  46. X. An, D. Kuang, X. Guo, Y. Zhao, and L. He, “A Deep Learning Method for Classification of EEG Data Based on Motor Imagery,” 2014, pp. 203–210, URL: http://link.springer.com/10.1007/978-3-319-09330-7_25 .
  47. K. Li, X. Li, Y. Zhang, and A. Zhang, “Affective state recognition from EEG with deep belief networks,” in 2013 IEEE International Conference on Bioinformatics and Biomedicine, 2013, pp. 305–310, DOI: 10.1109/BIBM.2013.6732507.
  48. X. Jia, K. Li, X. Li, and A. Zhang, “A novel semi-supervised deep learning framework for affective state recognition on EEG signals,” Proc. - IEEE 14th Int. Conf. Bioinforma. Bioeng. BIBE 2014, pp. 30–37, 2014, DOI: 10.1109/BIBE.2014.26.
  49. W.-L. Zheng, H.-T. Guo, and B.-L. Lu, “Revealing critical channels and frequency bands for emotion recognition from EEG with deep belief network,” in 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER), 2015, pp. 154–157, DOI: 10.1109/NER.2015.7146583.
  50. S. Jirayucharoensak, S. Pan-Ngum, and P. Israsena, “EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation,” Sci. World J., vol. 2014, pp. 1–10, 2014, DOI: 10.1155/2014/627892.
  51. M. Fedorchuk and B. Lamiroy, “Binary Classifier Evaluation Without Ground Truth,” in Ninth International Conference on Advances in Pattern Recognition (ICAPR-2017), 2017.
  52. M. Fedorchuk and B. Lamiroy, “Statistic metrics for evaluation of binary classifiers without ground-truth,” in 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), 2017, pp. 1066–1071, DOI: 10.1109/UKRCON.2017.8100414.
  53. G. Rätsch, “A Brief Introduction into Machine Learning,” in 21st Chaos Computer Club, 2004, URL: https://events.ccc.de/congress/2004/fahrplan/files/105-machine-learning-paper.pdf .
  54. R. E. Schapire, “The Boosting Approach to Machine Learning: An Overview,” in Lecture Notes in Statistics, Springer, New York, NY, 2003, pp. 149–171, URL: http://link.springer.com/10.1007/978-0-387-21579-2_9 .
§ 06 — Related

Similar articles in this journal

Related peer-reviewed studies published in this journal.
View all issues

Quasistatic Identification of The Shading Structure of Solar Panels by The Method of Cardinal Number Vectors

PhD, Assoc. Prof. Olena Olehivna Abakumova, Dr.Sc.(Eng.), Prof. Valerii Yakovych Zhuikov, Mykola O. Lukianov

In the article the problem of identification the shading structure of solar panels is considered. The relevance of the study is conditioned by the necessity of rapid and…

#solar panels #photovoltaic surface #shadow structure modeling #quasistatic identification
p. 362283.1-362283.7
10.20535/2523-4455.mea.362283

A A Complex Method for Non-invasive Fetal Electrocardiogram Signal Extraction and Cardiac Arrhythmia Classification

Dmytro A. Shevchuk, PhD, Assoc. Prof. Nataliia Heorhiivna Ivanushkina

This study is devoted to the development of a complex method for extracting the fetal electrocardiogram (fECG) from non-invasive abdominal recordings and for the…

#abdominal electrocardiogram #fetal electrocardiogram #arrhythmia #blind source separation
p. 356487.1-356487.11
10.20535/2523-4455.mea.356487

Simulation Modeling of a Filter-Compensating Converter for a Three-Phase Three-Wire Network

Ivan Andriiovych Vakulenko, PhD, Assoc. Prof. Dmytro Anatoliiovych Mykolaiets, PhD, Assoc. Prof. Kateryna Serhiivna Klen

This paper addresses the critical issue of power quality degradation in modern power systems caused by the proliferation of non-linear loads. The study focuses on the…

#filter-compensating converter #three-phase three-wire network #power quality #total harmonic distortion
p. 361138.1-361138.6
10.20535/2523-4455.mea.361138

Most read articles by the same author(s)

Similar Articles

1-10 of 59

You may also start an advanced similarity search for this article.