ESTIMATION OF THE DEPTH OF ANESTHESIA BY ELECTROENCEPHALOGRAM USING NEURAL NETWORKS
https://doi.org/10.32603/1993-8985-2019-22-3-106-112
Abstract
Introduction. Monitoring of the depth of anesthesia during surgery is a complex task. Since electroencephalogram (EEG) signals contain valuable information about processes in the brain, EEG analysis is considered to be one of the most useful methods for study and assessment of the depth of anesthesia in clinical applications. Anesthetics affect the frequency composition of the EEG. EEG of awake persons, as a rule, contains mixed alpha and beta rhythms. Changes in the EEG, caused by the transition from the waking state to the state of deep anesthesia, manifest as a shift of the spectral components of the signal to the lower part of the frequency range. Anesthetics cause a whole range of neurophysiological changes, which cannot be correctly assessed with just one indicator.
Objective. In order to describe complex processes during the transition from the waking state to the state of deep anesthesia adequately, it is required to propose a method for assessing the depth of anesthesia, using a comprehensive set of parameters reflecting changes in the EEG signal. The article presents the results of study the possibility of building a regression model based on artificial neural networks (ANN) to determine levels of anesthesia using a set of parameters calculated by EEG.
Materials and methods. The authors of the article propose the method for assessing the level of anesthesia, based on the use of neural networks, which input parameters are time and frequency EEG parameters, namely: spectral entropy (SE); burst-suppression ratio (BSR); spectral edge frequency (SEF95) and log power ratio of the spectrum (RBR) for three pairs of frequency ranges.
Results. The optimal parameters of ANN were determined, at which the highest level of regression is achieved between the calculated and the verified values of the anesthesia depth indices.
Conclusion. In order to create a practical version of the algorithm, it is necessary to investigate further the noise stability of the proposed method and develop a set of algorithmic solutions, which ensure a reliable operation of the algorithm in the presence of noise.
About the Authors
Mokhammed A. Al-Ghaili
Saint Petersburg Electrotechnical University "LETI"
Russian Federation
Russian Federation
Mokhammed A. Al-Ghaili – Master (2013) in Biotechnical Systems and Technologies, postgraduate student of the Department of Bioengineering Systems of Saint Petersburg Electrotechnical University "LETI". The author of 6 scientific publications. Area of expertise: digital processing of biomedical signals; machine learning; pattern recognition.
5, Professor Popov Str., 197376, St. Petersburg
Alexander N. Kalinichenko
Saint Petersburg Electrotechnical University "LETI"
Russian Federation
Russian Federation
Alexander N. Kalinichenko – Dr. of Sci. (Engineering) (2009), Professor of the Department of Bioengineering Systems of Saint Petersburg Electrotechnical University "LETI". The author of more than 160 scientific publications. Area of expertise: computer analysis of biomedical signals; machine learning; pattern recognition.
5, Professor Popov Str., 197376, St. Petersburg
References
1. Rampil IJ. A primer for EEG signal processing in anesthesia. Anesthesiology. 1998, vol. 89, pp. 980–1002.
2. Welling P. G. Pharmacokinetics: Processes, Mathematics, and Applications. 2nd ed. Washington DC: American Chemical Society. 1997, 393 p.
3. Glass P. S., Bloom M., Kearse L., Rosow C., Sebel P., Manberg P. Bispectral analysis measures sedation and memory effects of propofol, midazolam, isoflurane, and alfentanil in normal volunteers. Anesthesiology, 1997, vol. 86(4), pp. 836–847.
4. Traast H. S., Kalkman C. J. Electroencephalographic characteristics of emergence from propofol/sufentanil total intervenouse anesthesia. Anesthesia & Analgesia. 1995, vol. 81(2), pp. 366–371. doi: 10.1097/00000539-199508000-00027
5. Ferenets R., Vanluchene A., Lipping T., Heyse B., Struys M. Behavior of Entropy/Complexity Measures of the Electroencephalogram during Propofol-induced Sedation. Anesthesiology, 2007, vol. 106(4), pp. 696–706. doi:10.1097/01.anes.0000264790.07231.2d
6. Schwender D., Daunderer M., Mulzer S., Klasing S., Finsterer U., Peter K. Spectral edge frequency of the electroencephalogram to monitor ‘depth’ of anaesthesia with isoflurane or propofol. British Journal of Anaesthesia, 1996, vol. 77(2), pp. 179–184. doi: 10.1093/bja/77.2.179
7. Tong S., Thakor N. V. Quantitative EEG Analysis Methods and Clinical Applications. Norwood: Artech House, 2009, 421 p.
8. Hossein R., Alireza M. D., Mehrab G. Estimation the Depth of Anesthesia by the Use of Artificial Neural Network // Artificial Neural Networks. Methodological Advances and Biomedical Applications. Ed. by Kenji Suzuki. 2011. P. 283–302. doi: 10.5772/15139
9. Viertiö-Oja H., Maja V., Särkelä M., Talja P., Tenkanen N., Tolvanen-Laakso H., Paloheimo M., Vakkuri A., Yli-Hankala A., Meriläinen P. Description of the Entropy algorithm as applied in the Datex-Ohmeda S/5 Entropy Module. Acta Anaesthesiol. Scand. 2004, vol. 48(2), pp. 154–161. doi: 10.1111/j.0001-5172.2004.00322.x
10. Al-Ghaili M. A., Kalinichenko A. N. Evaluation of Depth of Anesthesia Based on Joint Analysis of EEG Frequency and Time Parameters. Izvestiya SPBGETU “LETI” [Proceedings of Saint Petersburg Electrotechnical University], 2018, no. 3, pp. 80–85. (In Russian)
11. Amin H. U., Mumtaz W., Subhani A. R., Saad M. N. M., Malik A. S. Classification of EEG Signals Based on Pattern Recogni-tion Approach. Frontiers in Computational Neuroscience, 2017, vol. 11, art.103. P. 1–12.
12. Lee B., Won D., Seo K., Kim H. J., Lee S. Classification of Wakefulness and Anesthetic Sedation Using Combination Feature of EEG and ECG. Proc. of 2017 5th Intern. Winter Conf. on Brain-Computer Interface (BCI). Sabuk, South Korea, 9–11 Jan. 2017. Piscataway: IEEE, 2017, pp. 88–90. doi: 10.1109/IWW-BCI.2017.7858168
13. Shalbaf R., Behnam H., Sleigh J. W., Steyn-Ross A., Voss L. J. Monitoring the Depth of Anesthesia Using Entropy Features and an Artificial Neural Network. J. of Neuroscience Methods, 2013, vol. 218, iss. 1, pp. 17–24.
14. Benzy V. K., Jasmin E. A., Koshy R. C., Amal F. Wavelet Entropy Based Classification of Depth of Anesthesia. 2016 Intern. Conf. on Computational Techniques in Information and Communication Technologies (ICCTICT), New Delhi, India, 11–13 March 2016. Piscataway, IEEE, 2016, pp. 521–524. doi: 10.1109/ICCTICT.2016.7514635
15. Arslan A., Şen B., Çelebi F. V., Peker M., But A. A Comparison of Different Classification Algorithms for Determining the Depth of Anesthesia Level on a New Set of Attributes. 2015 Intern. Symp. on Innovations in Intelligent Systems and Applications (INISTA), Madrid, Spain, 2–4 Sept. 2015. Piscataway, IEEE, 2015, pp. 1–7. doi: 10.1109/INISTA.2015.7276738
Review
For citations:
Al-Ghaili M.A., Kalinichenko A.N. ESTIMATION OF THE DEPTH OF ANESTHESIA BY ELECTROENCEPHALOGRAM USING NEURAL NETWORKS. Journal of the Russian Universities. Radioelectronics. 2019;22(3):106-112. https://doi.org/10.32603/1993-8985-2019-22-3-106-112
Views:
1017
Email this article 