An EEG Signal Recognition Algorithm During Epileptic Seizure Based on Distributed Edge Computing.

Shi Qiu; Keyang Cheng; Tao Zhou; Rabia Tahir; Liang Ting

doi:10.9781/ijimai.2022.07.001

Authors

Shi Qiu Chinese Academy of Sciences
Keyang Cheng Jiangsu University
Tao Zhou North Minzu University
Rabia Tahir Jiangsu University
Liang Ting First Affiliated Hospital of Xi'an Jiaotong University

DOI:

https://doi.org/10.9781/ijimai.2022.07.001

Keywords:

Clinical Feature, Cloud Computing, Deep Learning, Edge Computing, Electroencephalography, Epilepsy, Fuzzy, Takagi-Sugeno-Kang (TSK)

Supporting Agencies

This work is supported by Science and Technology Rising Star of Shaanxi Youth (2021KJXX-61), The Shanxi National Science Foundation (2020JQ-518), Natural Science Foundation of China (62062003), North Minzu University Research Project of Talent Introduction (2020KYQD08), Key Research and Development Project of Ningxia (2020BEB04022), The Open Project Program of the State Key Lab of CAD&CG (A2206).

Abstract

Epilepsy is one kind of brain diseases, and its sudden unpredictability is the main cause of disability and even death. Thus, it is of great significance to identify electroencephalogram (EEG) during the seizure quickly and accurately. With the rise of cloud computing and edge computing, the interface between local detection and cloud recognition is established, which promotes the development of portable EEG detection and diagnosis. Thus, we construct a framework for identifying EEG signals in epileptic seizure based on cloud-edge computing. The EEG signals are obtained in real time locally, and the horizontal viewable model is established at the edge to enhance the internal correlation of the signals. The Takagi-Sugeno-Kang (TSK) fuzzy system is established to analyze the epileptic signals. In the cloud, the fusion of clinical features and signal features is established to establish a deep learning framework. Through local signal acquisition, edge signal processing and cloud signal recognition, the diagnosis of epilepsy is realized, which can provide a new idea for the real-time diagnosis and feedback of EEG during epileptic seizure.

Downloads

Download data is not yet available.

References

Y. Si, “Machine learning applications for electroencephalograph signals in epilepsy: a quick review,” Acta Epileptologica, vol. 2, pp. 1-7, 2020.

A. Sharmila, and P. Geethanjali, “DWT Based Time Domain Features on Detection of Epilepsy Seizures from EEG Signal,” In Biomedical Signal Processing. Springer, Singapore. pp. 181-200, 2020.

C. R. Coughlin, “Pyridoxine-dependent epilepsy is more than just epilepsy,” Developmental Medicine & Child Neurology, vol. 62, no. 3, pp. 268-268, 2020.

T. Zhou, H. Lu, Z. Yang, S. Qiu, B. Huo and Y. Dong, “The ensemble deep learning model for novel COVID-19 on CT images,” Applied Soft Computing, vol. 98, p. 106885, 2021.

L. Gu, D. Zeng, S. Guo, A. Barnawi and Y. Xiang, “Cost efficient resource management in fog computing supported medical cyber-physical system,” IEEE Transactions on Emerging Topics in Computing, vol. 5, no. 1, pp. 108-119, 2015.

N. Abirami, S. Karthik and M. Kanimozhi, “Automated brain tumor detection and identification using medical imaging,” International Journal of Research in Computer Applications and Robotics, vol. 3, no. 9, pp. 85-91, 2015.

W. Shi, J. Cao, Q. Zhang, Y. Li and L. Xu, “Edge computing: Vision and challenges,” IEEE internet of things journal, vol. 3, no. 5, pp. 637-646, 2016.

C. Aggarwal and K. Srivastava, “Securing IoT devices using SDN and edge computing,” In 2016 2nd International Conference on Next Generation Computing Technologies (NGCT) IEEE, pp. 877-882, 2016.

M. P. Hosseini, T. X. Tran, D. Pompili, K. Elisevich and H. Soltanian-Zadeh, “Deep learning with edge computing for localization of epileptogenicity using multimodal rs-fMRI and EEG big data,” In 2017 IEEE international conference on autonomic computing (ICAC) IEEE, pp. 83-92,2017.

D. Singh, G. Tripathi, A. M. Alberti and A. Jara, “Semantic edge computing and IoT architecture for military health services in battlefield,” In 2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC) IEEE, pp. 185-190, 2017.

X. Li, R. Ding, X. Liu, W. Yan, J. Xu, H. Gao and X. Zheng, “Comec: Computation offloading for video-based heart rate detection app in mobile edge computing,” In 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications, IEEE, pp. 1038-1039, 2018.

S. Oueida, Y. Kotb, M. Aloqaily, Y. Jararweh, T. Baker, “An edge computing based smart healthcare framework for resource management,” Sensors, vol. 18, no. 12, pp. 4307, 2018.

A. A. Abdellatif, A. Mohamed, C. F. Chiasserini, M. Tlili and A. Erbad, “Edge computing for smart health: Context-aware approaches, opportunities, and challenges,” IEEE Network, vol. 33, no. 3, pp. 196-203, 2019.

D. Lin and Y. Tang, “Edge computing-based mobile health system: network architecture and resource allocation,” IEEE Systems Journal, vol. 14, no. 2, pp. 1716-1727, 2019.

I. V. Pustokhina, D. A. Pustokhin, D. Gupta, A. Khanna, K. Shankar and G. N. Nguyen, “An effective training scheme for deep neural network in edge computing enabled Internet of medical things (IoMT) systems,” IEEE Access, vol. 8, pp. 107112-107123, 2020.

C. Dou, S. Zhang, H. Wang, L. Sun, Y. Huang and W. Yue, “Adhd fmri short-time analysis method for edge computing based on multi-instance learning,” Journal of Systems Architecture, vol. 111, pp. 101834, 2020.

M. A. Rahman and M. S. Hossain, “An internet of medical things-enabled edge computing framework for tackling COVID-19,” IEEE Internet of Things Journal. vol. 8, no. 21, pp. 15847-15854, 2021.

L. Xu and M. Xu, “Epilepsy surgery in China: past, present, and future,” European journal of neurology, vol. 17, no. 2, pp. 189-193, 2010.

J. G. Jefferys, “Advances in understanding basic mechanisms of epilepsy and seizures,” Seizure, vol. 19, no. 10, pp. 638-646, 2010.

A. T. Berg, “Epilepsy, cognition, and behavior: the clinical picture,” Epilepsia, vol. 52, no.7-12, 2011.

S. Koçer and M. R. Canal, “Classifying epilepsy diseases using artificial neural networks and genetic algorithm,” Journal of medical systems, vol. 35, no. 4, pp. 489-498, 2011.

A. M. Pack, D. S. Reddy, S. Duncan and A. Herzog, “Neuroendocrinological aspects of epilepsy: important issues and trends in future research,” Epilepsy & Behavior, vol.22, no. 1, pp. 94-102, 2011.

K. L. Margrove, A. K. Thapar, S. A. Mensah and Kerr, M. P. “Helpseeking and treatment preferences for depression in epilepsy,” Epilepsy & Behavior, vol. 22, no. 4, pp. 740-744, 2011.

N. Rafiuddin, Y. U. Khan and O. Farooq, “Feature extraction and classification of EEG for automatic seizure detection,” In 2011 International Conference on Multimedia, Signal Processing and Communication Technologies, pp.184-187, 2011.

M. Pediaditis, M. Tsiknakis and N. Leitgeb, “Vision-based motion detection, analysis and recognition of epileptic seizures-a systematic review,” Computer methods and programs in biomedicine, vol. 108, no, 3, pp. 1133-1148, 2012.

M. Musselman and D. Djurdjanovic, “Time-frequency distributions in the classification of epilepsy from EEG signals,” Expert systems with applications, vol. 39, no. 13, pp. 11413-11422, 2012.

N. F. Chang, T. C. Chen, C. Y. Chiang and L. G. Chen, “Channel selection for epilepsy seizure prediction method based on machine learning,” In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.5162-5165, 2012.

K. Rosas, I. Parrón, P. Serrano and J. M. Cimadevilla, “Spatial recognition memory in a virtual reality task is altered in refractory temporal lobe epilepsy,” Epilepsy & Behavior, vol. 28, no. 2, pp.227-2312013.

S. A. Hosseini, M. R. Akbarzadeh-T and M. B. Naghibi-Sistani, “Qualitative and quantitative evaluation of EEG signals in epileptic seizure recognition,” International Journal of Intelligent Systems and Applications, vol. 5, no. 6, p. 41, 2013.

S. Kiranyaz, T. Ince, M. Zabihi and D. Ince, “Automated patient-specific classification of long-term electroencephalography,” Journal of biomedical informatics, vol. 49, pp.16-31, 2014.

A. Gomez-Ibañez, E. Urrestarazu and C. Viteri, “Recognition of facial emotions and identity in patients with mesial temporal lobe and idiopathic generalized epilepsy: an eye-tracking study,” Seizure, vol. 23, no. 10, pp. 892-898, 2014.

J. R. Villar, M. Menéndez, J. Sedano, E. de la Cal and V. M. González, “Analyzing accelerometer data for epilepsy episode recognition.” In 10th International Conference on Soft Computing Models in Industrial and Environmental Applications, pp.39-48, 2015.

Z. Tao, C. Wan-Zhong and L. Ming-Yang, “Recognition of epilepsy electroencephalography based on Ada Boost algorithm,” Acta Physica Sinica, vol. 64, no. 12, pp. 128701, 2015.

K. Samiee, S. Kiranyaz, M. Gabbouj, T. Saramäki, “Long-term epileptic EEG classification via 2D mapping and textural features,” Expert Systems with Applications, vol.42, no. 20, pp. 7175-7185, 2015.

J. Yan, Y. Wang, G. Ouyang, T. Yu and X. Li, “Using max entropy ratio of recurrence plot to measure electrocorticogram changes in epilepsy patients,” Physica A: Statistical Mechanics and its Applications, vol. 443, pp. 109-116, 2016.

K. I. Qazi, H. K. Lam, B. Xiao, G. Ouyang and X. Yin, “Classification of epilepsy using computational intelligence techniques,” CAAI Transactions on Intelligence Technology, vol. 1, no. 2, pp. 137-149, 2016.

M. Li, W. Chen and T. Zhang, “Classification of epilepsy EEG signals using DWT-based envelope analysis and neural network ensemble,” Biomedical Signal Processing and Control, vol. 31, pp. 357-365, 2017.

L. N. Sepeta, K. B. Casaletto, V. Terwilliger, J. Facella-Ervolini, M. Sady, J. Mayo, M. M. Berl, “The role of executive functioning in memory performance in pediatric focal epilepsy,” Epilepsia, vol. 58, no. 2, pp. 300- 310, 2017.

S. Qiu, J. Li, M. Cong, C. Wu, Y. Qin and T. Liang, “Detection of solitary pulmonary nodules based on brain-computer interface,” Computational and Mathematical Methods in Medicine, pp. 1-10, 2020.

I. Ullah, M. Hussain and H. Aboalsamh, “An automated system for epilepsy detection using EEG brain signals based on deep learning approach,” Expert Systems with Applications, vol. 107, pp. 61-71, 2018.

Z. Jiang, F. L. Chung and S. Wang, “Recognition of multiclass epileptic EEG signals based on knowledge and label space inductive transfer,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no.4, pp. 630-642, 2019.

K. G. Parthiban, S. Vijayachitra and R. Dhanapal, “Hybrid dragonfly optimization-based artificial neural network for the recognition of epilepsy,” International Journal of Computational Intelligence Systems, vol. 12, no. 2, pp.1261-1269, 2019.

K. P. Thanaraj, B. Parvathavarthini, U. J. Tanik, V. Rajinikanth, S. Kadry and K. Kamalanand, “Implementation of deep neural networks to classify EEG signals using gramian angular summation field for epilepsy diagnosis,” Computer Vision and Pattern Recognition, 2020.

J. Prasanna, M. S. P. Subathra, M. A. Mohammed, R. Damaševičius, N. J. Sairamya and S. T. George, “Automated epileptic seizure detection in pediatric subjects of CHB-MIT EEG database-a survey,” Journal of Personalized Medicine, vol. 11, no. 10, p. 1028, 2021.