According to a recently conducted survey on surgical complication mortality rate, 47% of such cases are due to anesthetics overdose. This indicates that there is an urgent need to moderate the level of anesthesia. Recently deep learning (DL) methods have played a major role in estimating the depth of Anesthesia (DOA) of patients and has played an essential role in control anesthesia overdose. In this paper, Electroencephalography (EEG) signals have been used for the prediction of DOA. EEG signals are very complex signals which may require months of training and advanced signal processing techniques. It is a point of debate whether DL methods are an improvement over the already existing traditional EEG signal processing approaches. One of the DL algorithms is Convolutional neural network (CNN) which is very popular algorithm for object recognition and is widely growing its applications in processing hierarchy in the human visual system. In this paper, various decomposition methods have been used for extracting the features EEG signal. After acquiring the necessary signals values in image format, several CNN models have been deployed for classification of DOA depending upon their Bispectral Index (BIS) and the signal quality index (SQI). The EEG signals were converted into the frequency domain using and Empirical Mode Decomposition (EMD), and Ensemble Empirical Mode Decomposition (EEMD). However, because of the inter mode mixing observed in EMD method; EEMD have been utilized for this study. The developed CNN models were used to predict the DOA based on the EEG spectrum images without the use of handcrafted features which provides intuitive mapping with high efficiency and reliability. The best trained model gives an accuracy of 83.2%. Hence, this provides further scope and research which can be carried out in the domain of visual mapping of DOA using EEG signals and DL methods.
Citation: Ravichandra Madanu, Farhan Rahman, Maysam F. Abbod, Shou-Zen Fan, Jiann-Shing Shieh. Depth of anesthesia prediction via EEG signals using convolutional neural network and ensemble empirical mode decomposition[J]. Mathematical Biosciences and Engineering, 2021, 18(5): 5047-5068. doi: 10.3934/mbe.2021257
According to a recently conducted survey on surgical complication mortality rate, 47% of such cases are due to anesthetics overdose. This indicates that there is an urgent need to moderate the level of anesthesia. Recently deep learning (DL) methods have played a major role in estimating the depth of Anesthesia (DOA) of patients and has played an essential role in control anesthesia overdose. In this paper, Electroencephalography (EEG) signals have been used for the prediction of DOA. EEG signals are very complex signals which may require months of training and advanced signal processing techniques. It is a point of debate whether DL methods are an improvement over the already existing traditional EEG signal processing approaches. One of the DL algorithms is Convolutional neural network (CNN) which is very popular algorithm for object recognition and is widely growing its applications in processing hierarchy in the human visual system. In this paper, various decomposition methods have been used for extracting the features EEG signal. After acquiring the necessary signals values in image format, several CNN models have been deployed for classification of DOA depending upon their Bispectral Index (BIS) and the signal quality index (SQI). The EEG signals were converted into the frequency domain using and Empirical Mode Decomposition (EMD), and Ensemble Empirical Mode Decomposition (EEMD). However, because of the inter mode mixing observed in EMD method; EEMD have been utilized for this study. The developed CNN models were used to predict the DOA based on the EEG spectrum images without the use of handcrafted features which provides intuitive mapping with high efficiency and reliability. The best trained model gives an accuracy of 83.2%. Hence, this provides further scope and research which can be carried out in the domain of visual mapping of DOA using EEG signals and DL methods.
| [1] | A. Gottschalk, H. V. Aken, M. Zenz, T. Standl, Is anesthesia dangerous?, Dtsch. Arzteblatt Int., 108 (2011), 469-474. |
| [2] |
B. Musizza, S. Ribaric, Monitoring the Depth of Anaesthesia, Sensors, 10 (2010), 10896-10935. doi: 10.3390/s101210896
|
| [3] | M. G. Frasch, L. D. Durosier, N. Gold, M. Cao, B. Matushewski, L. Keenliside, et al., Adaptive shut-down of EEG activity predicts critical acidemia in the near-term ovine fetus, Physiol. Rep., 3 (2015), e12435. |
| [4] |
M. K. Kiymik, I. Güler, A. Dizibüyük, M. Akin, Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application, Comput. Biol. Med., 35 (2005), 603-616. doi: 10.1016/j.compbiomed.2004.05.001
|
| [5] |
V. Lalitha, C. Eswaran, Automated detection of anesthetic depth levels using chaotic features with artificial neural networks, J. Med. Syst., 31 (2007), 445-452. doi: 10.1007/s10916-007-9083-y
|
| [6] | A. Hutt, The anesthetic propofol shifts the frequency of maximum spectral power in EEG during general anesthesia: analytical insights from a linear model, Front. Comput. Neurosci., 7 (2013). |
| [7] |
X. S. Zhang, R. J. Roy, E. W. Jensen, EEG complexity as a measure of depth of anesthesia for patients, IEEE Trans. Biomed. Eng., 48 (2001), 1424-1433. doi: 10.1109/10.966601
|
| [8] | H. U. Amin, W. Mumtaz, A. R. Subhani, M. N. M. Saad, A. S. Malik, Classification of EEG signals based on pattern recognition approach, Front. Comput. Neurosci., 11 (2017). |
| [9] | U. R. Acharya, S. L. Oh, Y. Hagiwara, J. H. Tan, H. Adeli, Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals, Comput. Biol. Med., 100 (2017), 270-278. |
| [10] | S. Tripathi, S. Acharya, R. D. Sharma, S. Mittal, S. Bhattacharya, Using deep and convolutional neural networks for accurate emotion classification on DEAP dataset, in Twenty-ninth IAAI conference, (2017), 4746-4752. |
| [11] |
O. Tsinalis, P. M. Matthews, Y. Guo, Automatic sleep stage scoring using time-frequency analysis and stacked sparse autoencoders, Ann. Biomed. Eng., 44 (2016), 1587-1597. doi: 10.1007/s10439-015-1444-y
|
| [12] |
Y. R. Tabar, U. Halici, A novel deep learning approach for classification of EEG motor imagery signals, J. Neural Eng., 14 (2017), 016003. doi: 10.1088/1741-2560/14/1/016003
|
| [13] | G. Kotsovolis, G. Komninos, Awareness during anesthesia: How sure can we be that the patient is sleeping indeed?, Hippokratia, 13 (2009), 83. |
| [14] | A. Petsiti, V. Tassoudis, G. Vretzakit, D. Zacharoulis, K. Tepetes, G. Ganeli, et al., Depth of anesthesia as a risk factor for perioperative morbidity, Anesthesiol. Res. Pract., 2015. |
| [15] |
Y. Bengio, A. Courville, P. Vincent, Representation learning: A review and new perspectives, IEEE Trans. Pattern Anal. Mach. Intell., 35 (2013), 1798-1828. doi: 10.1109/TPAMI.2013.50
|
| [16] | K. Kuizenga, J. M. Wierda, C. J. Kalkman, Biphasic EEG changes in relation to loss of consciousness during induction with thiopental, propofol, etomidate, midazolam or sevoflurane, Br. J. Anaesth., 86 (2001), 354-360. |
| [17] | O. Tsinalis, P. M. Matthews, Y. Guo, S. Zafeiriou, Automatic sleep stage scoring with single-channel EEG using convolutional neural networks, preprint, arXiv: 1610.01683 |
| [18] |
G. Muhammad, M. Masud, S. U. Amin, R. Alrobaea, M. F. Alhamid, Automatic Seizure Detection in a Mobile Multimedia Framework, In IEEE Access, 6 (2018), 45372-45383. doi: 10.1109/ACCESS.2018.2859267
|
| [19] | M. Särkelä, S. Mustola, T. Seppänen, M. Koskinen, P. Lepola, K. Suominen, et al., Automatic analysis and monitoring of burst suppression in anesthesia, J. Clin. Monit. Comput., 17 (2002), 125-134. |
| [20] | N. D. Truong, A. D. Nguyen, L. Kuhlmann, M. R. Bonyadi, J. Yang, O. Kavehei, A generalised seizure prediction with convolutional neural networks for intracranial and scalp electroencephalogram data analysis, preprint, arXiv: 1707.01976. |
| [21] | M. Bueno-López, E. Giraldo, M. Molinas, Analysis of neural activity from EEG data based on EMD frequency bands, in 2017 24th IEEE International Conference on Electronics, Circuits and Systems (ICECS), (2017), 401-405. |
| [22] |
N. Ji, L. Ma, H. Dong, X. Zhang, EEG signals feature extraction based on DWT and EMD combined with approximate entropy, Brain Sci., 9 (2019), 201. doi: 10.3390/brainsci9080201
|
| [23] |
Q. Liu, L. Ma, S. Z. Fan, M. F. Abbod, J. S. Shieh, Sample entropy analysis for the estimating depth of anaesthesia through human EEG signal at different levels of unconsciousness during surgeries, PeerJ, 6 (2018), e4817. doi: 10.7717/peerj.4817
|
| [24] | Q. Wei, Y. Li, S. Z. Fan, Q. Liu, M. F. Abbod, C. W. Lu, et al., A critical care monitoring system for depth of anaesthesia analysis based on entropy analysis and physiological information database, Australas. Phys. Eng. Sci. Med., 37 (2014), 591-605. |
| [25] | Q. Liu, L. Ma, S. Z. Fan, M. F. Abbod, C. W. Lu, T. Y. Lin, et al., Design and evaluation of a real time physiological signals acquisition system implemented in multi-operating rooms for anesthesia, J. Med. Syst., 42 (2018), 1-19. |
| [26] |
H. Ge, G. Chen, H. Yu, H. Chen, F. An, Theoretical analysis of empirical mode decomposition, Symmetry, 10 (2018), 623. doi: 10.3390/sym10110623
|
| [27] | A. Krizhevsky, Learning Multiple Layers of Features from Tiny Images, M.S. thesis, University of Toronto, Toronto, Canada, 2009. |
| [28] | X. Liu, D. H. Kim, C. Wu, O. Chen, Resource and data optimization for hardware implementation of deep neural networks targeting FPGA-based edge devices, in 2018 ACM/IEEE International Workshop on System Level Interconnect Prediction (SLIP), San Francisco, CA, (2018), 1-8. |
| [29] | S. H. Hasanpour, M. Rouhani, M. Fayyaz, M. Sabokrou, Let's keep it simple, using simple architectures to outperform deeper and more complex architectures, preprint, arXiv: 1608.06037. |
| [30] | T. Sainath, O. Vinyals, A. Senior, H. Sak, Convolutional, long short-term memory, fully connected deep neural networks, in Proceedings of the 40th International Conference on Acoustics, Speech and Signal Processing, Brisbane, Australia, (2015), 4580-4584. |
| [31] | W. L. Mao, H. I. K. Fathurrahman, Y. Lee, T. W. Chang, EEG dataset classification using CNN method, in Journal of Physics: Conference Series, IOP Publishing, 1456 (2020), 012017. |
| [32] | J. Thomas, L. Comoretto, J. Jin, J. Dauwels, S. S. Cash, M. B. Westover, EEG classification via convolutional neural network-based interictal epileptiform event detection, in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (2018), 3148-3151. |
| [33] |
S. J. Pan, Q. Yang, A survey on transfer learning, IEEE Trans. Knowl. Data Eng., 22 (2010), 1345-1359. doi: 10.1109/TKDE.2009.191
|
| [34] | I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, et al., Generative adversarial nets, in Proceedings of the 27th International Conference on Neural Information Processing Systems, Cambridge, MA, USA, 2 (2014), 2672-2680. |
| [35] | I. Z. Yalniz, H. Jégou, K. Chen, M. Paluri, D. Mahajan, Billion-scale semi-supervised learning for image classification, preprint, arXiv: 1905.00546. |
Figures(9) / Tables(4)
Ravichandra Madanu, Farhan Rahman, Maysam F. Abbod, Shou-Zen Fan, Jiann-Shing Shieh. Depth of anesthesia prediction via EEG signals using convolutional neural network and ensemble empirical mode decomposition[J]. Mathematical Biosciences and Engineering, 2021, 18(5): 5047-5068. doi: 10.3934/mbe.2021257
DownLoad: