[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Adaptive octopus deep transfer learning based epileptic seizure classification on field programmable gate arrays

  • Original Paper
  • Published:
Evolving Systems Aims and scope Submit manuscript

Abstract

Seizures are a type of neurological illness that can disrupt the processes of the human brain. In most cases, epileptic abnormalities may be detected with direct visual scanning. However, owing to various technical artefacts, this scanning takes more time and is limited. As a result, an effective deep learning-based computer-aided diagnosis system for automatically differentiating seizure signals from non-seizure signals is required. Even if the classification accuracy of deep learning algorithms is sufficient, executing them on field programmable gate arrays (FPGA) is computationally quite expensive. In this paper, a new adaptive octopus deep transfer learning (AODTL) based epileptic seizure classification model is proposed to identify the best trade-off between the classification accuracy and hardware complexity. The proposed model selects the most significant features from the scalogram images using jellyfish search optimizer. Also, it fine-tunes the hyper-parameters automatically using the octopus optimizer. These optimizers are used to reduce the number of parameters required for the proposed AODTL classifier, so that the computational complexity is reduced. The implementation of the proposed work is carried out in Xilinx working platform and validated on the Temple University Hospital Seizure Detection Corpus (TUH EEG) database. Finally, the result of the proposed method showed that the diagnosis and classification of deep transfer learning model with maximum accuracy can be accomplished on FPGA. The maximum performance of 99.48% accuracy, latency of 6.1 ms, slice LUTs of 898 and power of 1.043 µW are achieved when testing on the FPGA board for classifying the epileptic seizures.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

Data sharing not applicable to this article.

References

  • Ahmadi A, Shalchyan V, Daliri MR (2017a) A new method for epileptic seizure classification in EEG using adapted wavelet packets. In: 2017a electric electronics, computer science, biomedical engineerings’ meeting EBBTIEEE. Istanbul, Turkey, pp 1–4

  • Ahmadi A, Tafakori S, Shalchyan V, Daliri MR (2017b) Epileptic seizure classification using novel entropy features applied on maximal overlap discrete wavelet packet transform of EEG signals. In: 2017b 7th international conference on computer and knowledge engineering (ICCKE). Mashhad, Iran, pp 390–395

  • Alzami F, Tang J, Yu Z, Wu S, Chen CP, You J, Zhang J (2018) Adaptive hybrid feature selection-based classifier ensemble for epileptic seizure classification. IEEE Access 6:29132–29145

    Article  Google Scholar 

  • Bhagat PN, Ramesh KS, Patil ST (2019) An automatic diagnosis of epileptic seizure based on optimization using Electroencephalography Signals. J Crit Rev 6(5):200–212

    Google Scholar 

  • Chou JS, Truong DN (2021) A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean. Appl Math Comput 389:125535

    MathSciNet  MATH  Google Scholar 

  • Daoud HG, Abdelhameed AM, Bayoumi M (2018) Automatic epileptic seizure detection based on empirical mode decomposition and deep neural network. In: 2018 IEEE 14th international colloquium on signal processing and its applications (CSPA). Penang, Malaysia, pp 182–186

  • Elhosary, Heba, Michael H. Zakhari, Mohamed A. Elgammal, Mohamed A. Abd El Ghany, Khaled N. Salama, and Hassan Mostafa (2019). Low-power hardware implementation of a support vector machine training and classification for neural seizure detection. In: IEEE transactions on biomedical circuits and systems 13(6):1324–1337

  • Falco-Walter JJ, Scheffer IE, Fisher RS (2018) The new definition and classification of seizures and epilepsy. Epilepsy Res 139:73–79

    Article  Google Scholar 

  • Geethu V, Santhoshkumar S (2020) An efficient FPGA realization of seizure detection from EEG signal using wavelet transform and statistical features. IETE J Res 66(3):315–325

    Article  Google Scholar 

  • Glory H, Anila C Vigneswaran, Jagtap Sujeet S, Shruthi R, Hariharan G, Sriram VS (2021) AHW-BGOA-DNN: A novel deep learning model for epileptic seizure detection. Neural Computing and Applications 33(11):6065–6093

  • Hou Y, Jia S, Lun X, Hao Z, Shi Y, Li Y, Zeng R, Lv J (2022) GCNs-net: a graph convolutional neural network approach for decoding time-resolved eeg motor imagery signals, pp 1–12

  • Hussain W, Sadiq MT, Siuly S, Rehman AU (2021) Epileptic seizure detection using 1 D-convolutional long short-term memory neural networks. Appl Acoust 177:107941

    Article  Google Scholar 

  • Imah EM, Widodo A (2017) A comparative study of machine learning algorithms for epileptic seizure classification on EEG signals. In: 2017 International conference on advanced computer science and information systems (ICACSIS). Bali, Indonesia, pp 401–408

  • Indira PB, Krishna RD (2021) Optimized adaptive neuro fuzzy inference system (OANFIS) based EEG signal analysis for seizure recognition on FPGA. Biomed Signal Process Control 66:102484

    Article  Google Scholar 

  • Jafari AP, Sagedy C, Smith E, Mohsenin T (2015) A low power seizure detection processor based on direct use of compressively-sensed data and employing a deterministic random matrix. In: 2015 IEEE biomedical circuits and systems conference (BioCAS). Atlanta, GA, USA, pp 1–4

  • Janjarasjitt S (2017) Epileptic seizure classifications of single-channel scalp EEG data using wavelet-based features and SVM. Med Biol Eng Comput 55(10):1743–1761

    Article  Google Scholar 

  • Jose JP, Sundaram M, Jaffino G (2020) FPGA implementation of epileptic seizure detection using ELM classifier. In: 2020 sixth international conference on bio signals, images, and instrumentation (ICBSII). Chennai, India, pp 1–5

  • Karakaya B, Kaya T, Gulten A (2018) FPGA-based ANN design for detecting epileptic seizure in EEG signal. Balk J Electr Comput Eng 6(2):15–19

    Google Scholar 

  • Li Y, Yang Z (2017) Application of EOS-ELM with binary Jaya-based feature selection to real-time transient stability assessment using PMU data. IEEE Access 5:23092–23101

    Article  Google Scholar 

  • Li Y, Wang XD, Luo ML, Li K, Yang XF, Guo Q (2017) Epileptic seizure classification of EEGs using time–frequency analysis based multiscale radial basis functions. IEEE J Biomed Health Inform 22(2):386–397

    Article  Google Scholar 

  • Liu T, Truong ND, Nikpour A, Zhou L, Kavehei O (2020) Epileptic seizure classification with symmetric and hybrid bilinear models. IEEE J Biomed Health Inform 24(10):2844–2851

    Article  Google Scholar 

  • Lüders H, Akamatsu N, Amina S, Baumgartner C, Benbadis S, Bermeo-Ovalle A, Bleasel A (2019) Critique of the 2017 epileptic seizure and epilepsy classifications. Epilepsia 60(6):1032–1039

    Google Scholar 

  • Mursalin M, Islam SS, Noman MK, Al-Jumaily AA (2019) Epileptic seizure classification using statistical sampling and a novel feature selection algorithm. arXiv preprint arXiv:1902.09962

  • Narin A (2022) Detection of focal and non-focal epileptic seizure using continuous wavelet transform-based scalogram images and pre-trained deep neural networks. IRBM, vol 43 (1), pp 22–31

  • Peng, Hong, Cancheng Li, Jinlong Chao, Tao Wang, Chengjian Zhao, Xiaoning Huo, and Bin Hu (2021) A novel automatic classification detection for epileptic seizure based on dictionary learning and sparse representation. Neurocomputing 424:179–192

  • Purnomo A, Tjandrasa H (2020) Epileptic seizure classification using deep batch normalization neural network. Lontar Komputer: Jurnal Ilmiah Teknologi Informasi 11(3):124–131

    Article  Google Scholar 

  • Rashid MM, Ahmad M (2017) Epileptic seizure classification using statistical features of EEG signal. In: 2017 International conference on electrical, computer and communication engineering (ECCE), Cox’s Bazar, pp 308–312.

  • Rout SK, Sahani M, Dora C, Biswal PK, Biswal B (2022) An efficient epileptic seizure classification system using empirical wavelet transform and multi-fuse reduced deep convolutional neural network with digital implementation. Biomed Signal Process Control 72:103281

    Article  Google Scholar 

  • Sahani M, Rout SK, Dash PK (2021) FPGA implementation of epileptic seizure detection using semisupervised reduced deep convolutional neural network. Appl Soft Comput 110:107639

    Article  Google Scholar 

  • Sharma R, Pachori RB, Sircar P (2020) Seizures classification based on higher order statistics and deep neural network. Biomed Signal Process Control 59:101921

    Article  Google Scholar 

  • Subasi A (2007) EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst Appl 32(4):1084–1093

    Article  Google Scholar 

Download references

Funding

No funding is provided for the preparation of manuscript.

Author information

Authors and Affiliations

  1. ECE Department, Matrusri Engineering College, Hyderabad, Telangana, 500059, India

    B. Indira Priyadarshini

  2. ECE Department, Chaitanya Bharathi Institute of Technology, Gandipet, Hyderabad, Telangana, 500075, India

    D. Krishna Reddy

Authors
  1. B. Indira Priyadarshini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. Krishna Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors have equal contributions in this work.

Corresponding author

Correspondence to B. Indira Priyadarshini.

Ethics declarations

Conflict of interest

Authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Consent to participate

All the authors involved have agreed to participate in this submitted article.

Consent to publish

All the authors involved in this manuscript give full consent for publication of this submitted article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Indira Priyadarshini, B., Reddy, D.K. Adaptive octopus deep transfer learning based epileptic seizure classification on field programmable gate arrays. Evolving Systems 14, 479–499 (2023). https://doi.org/10.1007/s12530-022-09474-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12530-022-09474-w

Keywords

Search

Navigation