Техніка та методи аналізу біосигналів для моніторингу глибини анестезії

Основний зміст сторінки статті

Maksym M. Fedorchuk
Anton Oleksandrovych Popov

Анотація

У роботі представлено основні існуючі методи та засоби визначення глибини анестезії. Серед розглянутих методів виділено такі групи: аналіз електроенцефалограм, аналіз електрокардіограм (ЕКГ) та комплексний аналіз біосигналів. Систематизовано отримані у попередніх роботах результати та визначені перспективні напрямки подальших досліджень, зокрема використання засобів машинного навчання для аналізу електрокардіограм та їх характеристик та глибоке навчання нейронних мереж.

З метою вивчення можливостей нейронних мереж як систем класифікації біосигналів було побудовано глибоку згорткову нейронну мережу для класифікації сигналів ЕКГ з наявністю або відсутністю аритмій. Вхідними даними для такої мережі були 30-секундні сигнали ЕКГ без попередньої обробки, і точність класифікації склала 68,3%. Результати роботи такої системи класифікації свідчать про доцільність застосування глибокого навчання для визначення аритмій та про необхідність використання попередньої обробки ЕКГ та попередньо виділених характерних ознак, серед яких можуть бути величина енергії сигналу у різних частотних діапазонах, параметри сигналу після видалення тренду та параметри сигналу у часовому домені.

Бібл. 54, табл. 1.

Блок інформації про статтю

Як цитувати
[1]
M. M. Fedorchuk і A. O. Popov, «Техніка та методи аналізу біосигналів для моніторингу глибини анестезії», Мікросист., Електрон. та Акуст., т. 23, вип. 4, с. 12–21, Сер 2018.
Розділ
Електронні системи та сигнали

Посилання

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

I. J. Rampil, “A primer for EEG signal processing in anesthesia.,” Anesthesiology, vol. 89, no. 4, pp. 980–1002, Oct. 1998, PMID: 9778016.

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.

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.

S. Kreuer and W. Wilhelm, “The Narcotrend monitor.,” Best Pract. Res. Clin. Anaesthesiol., vol. 20, no. 1, pp. 111–9, Mar. 2006, PMID: 16634418.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

F. S. Glumcher et al., Rukovodstvo po anesteziologii [Anesthesiology Guide], 2ed ed. Kyiv, Ukraine: VSI “Meditsina,” 2010, ISBN: 978-617-505-020-0.

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.

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.

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.

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.

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.

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.

D. L. Hudson and M. E. Cohen, Neural Networks and Artificial Intelligence for Biomedical Engineering. Wiley-IEEE Press, 1999, ISBN: 978-0780334045.

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.

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.

S. Min, B. Lee, and S. Yoon, “Deep learning in bioinformatics,” Brief. Bioinform., p. bbw068, Jul. 2016, PMID: 27473064, DOI: 10.1093/bib/bbw068.

S. Stober, D. J. Cameron, and J. A. Grahn, “Classifying EEG Recordings of Rhythm Perception,” in ISMIR, 2014.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

M. Fedorchuk and B. Lamiroy, “Binary Classifier Evaluation Without Ground Truth,” in Ninth International Conference on Advances in Pattern Recognition (ICAPR-2017), 2017.

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.

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.

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.