135
Views
0
CrossRef citations to date
0
Altmetric
Medical Electronics

Alcoholic EEG Signal Classification Using Multi-Heuristic Classifiers with Stochastic Gradient Descent Technique for Tuning the Hyperparameters

ORCID Icon, & ORCID Icon

References

  • A. J. Gabor, “Seizure detection using a self-organizing neural network: validation and comparison with other detection strategies,” Electroencephalogr. Clin. Neurophysiol., Vol. 107, no. 1, pp. 27–32, 1998. doi:10.1016/S0013-4694(98)00043-1.
  • S. K. Prabhakar, H. Rajaguru, and S. Lee. “A comprehensive analysis of alcoholic EEG signals with detrend fluctuation analysis and post classifiers,” In: 2019 7th International Winter Conference on Brain-Computer Interface (BCI), 1–6, 2019. doi:10.1109/IWW-BCI.2019.8737328.
  • M. Van Gils, et al., “Signal processing in prolonged EEG recordings during intensive care,” IEEE Eng. Med. Biol. Mag., Vol. 16, no. 6, pp. 56–63, 1997. doi:10.1109/51.637118.
  • V. Srinivasan, C. Eswaran, and N. Sriraam, “Artificial neural network based epileptic detection using time-domain and frequency-domain features,” J. Med. Syst., Vol. 29, no. 6, pp. 647–660, 2005. doi:10.1007/s10916-005-6133-1.
  • A. Liu, et al., “Detection of neonatal seizures through computerized EEG analysis,” Electroencephalogr. Clin. Neurophysiol., Vol. 82, no. 1, pp. 30–37, 1992. doi:10.1016/0013-4694(92)90179-L.
  • F. Mormann, et al., “Seizure prediction: the long and winding road,” Brain, Vol. 130, no. 2, pp. 314–333, 2007. doi:10.1093/brain/awl241.
  • S. K. Prabhakar, and H. Rajaguru. “Comparison of Isomap and matrix factorization with mahalanobis based sparse representation classifier for epilepsy classification from EEG signals,” In: 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), 2017, pp. 580–583. doi:10.1109/R10-HTC.2017.8289027.
  • S. Deivasigamani, C. Senthilpari, and W. H. Yong, “RETRACTED ARTICLE: Machine learning method based detection and diagnosis for epilepsy in EEG signal,” J. Amb. Intell. Human. Comput., Vol. 12, no. 3, pp. 4215–4221, 2021. doi:10.1007/s12652-020-01816-3.
  • H. Rajaguru, and S. K. Prabhakar. “Analysis of adaboost classifier from compressed EEG features for epilepsy detection,” In: 2017 International Conference on Computing Methodologies and Communication (ICCMC), 981–984, 2017. doi:10.1109/ICCMC.2017.8282614.
  • H. Rajaguru, and S. K. Prabhakar. “Hilbert transform with Elman backpropagation and multilayer perceptrons for epilepsy classification” In: 2017 International Conference of Electronics, Communication and Aerospace Technology (ICECA), vol. 1, pp. 571–576, 2017. doi:10.1109/ICECA.2017.8203601.
  • R. Chatterjee, D. Guha, D. K. Sanyal, and S. N. Mohanty. “Discernibility matrix based dimensionality reduction for EEG signal,” In: 2016 IEEE Region 10 Conference (TENCON), 2703–2706, 2016. doi:10.1109/TENCON.2016.7848530.
  • P. Gaur, R. B. Pachori, H. Wang, and G. Prasad, “An automatic subject specific intrinsic mode function selection for enhancing Two-class EEG-based motor imagery-brain computer interface,” IEEE Sens. J., Vol. 19, no. 16, pp. 6938–6947, 2019. doi:10.1109/JSEN.2019.2912790.
  • D. Zhang, L. Yao, K. Chen, and J. Monaghan, “A convolutional recurrent attention model for subject-independent EEG signal analysis,” IEEE Signal Process. Lett., Vol. 26, no. 5, pp. 715–719, 2019. doi:10.1109/LSP.2019.2906824.
  • R. Sehgal, D. Mehrotra, and M. Bala, “Designing ANFIS model to predict the reliability of component-based system,” in Soft computing: theories and applications, . Advances in Intelligent Systems and Computing, 584, M. Pant, K. Ray, T. Sharma, S. Rawat, and A. Bandyopadhyay, Ed. Singapore: Springer, 2018, pp. 1–9. doi:10.1007/978-981-10-5699-4_1.
  • T. Zhang, W. Chen, and M. Li, “Classification of inter-ictal and ictal EEGs using multi-basis MODWPT, dimensionality reduction algorithms and LS-SVM: A comparative study,” Biomed. Signal Process. Contr., Vol. 47, pp. 240–251, 2019. doi:10.1016/j.bspc.2018.08.038.
  • J.-H. Jeong, K.-H. Shim, D.-J. Kim, and S.-W. Lee, “Brain-controlled robotic arm system based on multi-directional CNN-BiLSTM network using EEG signals,” IEEE Trans. Neural Syst. Rehabil. Eng., Vol. 28, no. 5, pp. 1226–1238, 2020. doi:10.1109/TNSRE.2020.2981659.
  • N. Mammone, et al., “Brain network analysis of compressive sensed high-density EEG signals in AD and MCI subjects,” IEEE Trans. Indust. Informatics, Vol. 15, no. 1, pp. 527–536, 2019. doi:10.1109/TII.2018.2868431.
  • L. Bi, H. Wang, T. Teng, and C. Guan, “A novel method of emergency situation detection for a brain-controlled vehicle by combining EEG signals with surrounding information,” IEEE Trans. Neural Syst. Rehabil. Eng., Vol. 26, no. 10, pp. 1926–1934, 2018. doi:10.1109/TNSRE.2018.2868486.
  • A. Ghaemi, E. Rashedi, A. M. Pourrahimi, M. Kamandar, and F. Rahdari, “Automatic channel selection in EEG signals for classification of left or right hand movement in Brain Computer Interfaces using improved binary gravitation search algorithm,” Biomed. Signal Process. Contr., Vol. 33, pp. 109–118, 2017. doi:10.1016/j.bspc.2016.11.018.
  • X. Xiong, Z. Yu, T. Ma, H. Wang, X. Lu, and H. Fan, “Classifying action intention understanding EEG signals based on weighted brain network metric features,” Biomed. Signal Process. Contr., Vol. 59, pp. 101893, 2020. doi:10.1016/j.bspc.2020.101893.
  • S. Patidar, et al., “An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism,” Appl. Soft Comput., Vol. 50, pp. 71–78, 2017. doi:10.1016/j.asoc.2016.11.002.
  • S. K. Prabhakar, and H. Rajaguru. “Alcoholic EEG signal classification with correlation dimension based distance metrics approach and Modified Adaboost classification,” Heliyon, Vol. 6, no. 12, pp. e05689, 2020.
  • S.-H. Kim, Z. Woo Geem, and G.-T. Han, “Hyperparameter optimization method based on harmony search algorithm to improve performance of 1D CNN human respiration pattern recognition system,” IEEE Sensors J., Vol. 20, no. 13, pp. 1–19, 2020. doi:10.1109/JSEN.2020.2991711.
  • L. F. Ahyar, S. Suyanto, and A. Arifianto. “Firefly algorithm-based hyperparameters setting of DRNN for weather prediction,” In: 2020 International Conference on Data Science and Its Applications (ICoDSA), 1–5, 2020. doi:10.1109/ICoDSA50139.2020.9212921.
  • A. A. Mas’ud, A. Sundaram, J. A. Ardila-Rey, R. Schurch, F. Muhammad-Sukki, and N. A. Bani, “Application of the Gaussian Mixture Model to Classify Stages of Electrical Tree Growth in Epoxy Resin,” Sensors, Vol. 21, no. 7, pp. C1–C1, 2021. doi:10.1109/JSEN.2021.3059585.
  • UCI KDD database [Online]. Available: https://archive.ics.uci.edu/ml/datasets/eegdatabase.
  • H. Polat, and M. S. Ozerdem. “Epileptic seizure detection from EEG signals by using wavelet and Hilbert transform,” In: 2016 XII International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH), 66–69, 2016. doi:10.1109/MEMSTECH.2016.7507522.
  • T. M. Bakhsh, S. Meshgini, and A. Farzamnia. “Identification of Epilepsy utilizing Hilbert Transform and SVM based Classifier,” In: 2020 28th Iranian Conference on Electrical Engineering (ICEE), 1–5, 2020. doi:10.1109/ICEE50131.2020.9260716.
  • R. Usharani, and M. Murali, “A review of dimensionality reduction methods applied on clinical data of diabetic neuropathy complaints,” Intern. J. Bioinform. Res. Applic., Vol. 17, no. 4, pp. 324–342, 2021. doi:10.1504/IJBRA.2021.117930.
  • O. Hirose, “Acceleration of non-rigid point set registration with Downsampling and Gaussian process regression,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 43, no. 8, pp. 2858–2865, 2021. doi:10.1109/TPAMI.2020.3043769.
  • S. K. Prabhakar, H. Rajaguru, and S. Lee, “Metaheuristic-Based dimensionality reduction and classification analysis of PPG signals for interpreting cardiovascular disease,” IEEE. Access., Vol. 7, pp. 165181–165206, 2019. doi:10.1109/ACCESS.2019.2950220.
  • J. F. Kenney, and E. S. Keeping. Mathematics of statistics, Pt. 2. 2nd ed. Princeton, NJ: Van Nostrand, 1951.
  • W. Zhou, Y. Liu, Q. Yuan, and X. Li, “Epileptic seizure detection using lacunarity and Bayesian linear discriminant analysis in intracranial EEG,” IEEE Trans. Biomed. Eng., Vol. 60, no. 12, pp. 3375–3381, 2013. doi:10.1109/TBME.2013.2254486.
  • S. Yuan, J. Liu, J. Shang, et al., “The earth mover’s distance and Bayesian linear discriminant analysis for epileptic seizure detection in scalp EEG,” Biomed. Eng. Lett, Vol. 8, pp. 373–382, 2018. doi:10.1007/s13534-018-0082-3.
  • S. Weisberg. Applied linear regression. Vol. 528. Hoboken, New Jersey, U.S.: John Wiley & Sons, 2005.
  • D. C. Montgomery, E. A. Peck, and G. Geoffrey Vining. Introduction to linear regression analysis. Hoboken, New Jersey, U.S.: John Wiley & Sons, 2021.
  • A. Subasi, and E. Ercelebi, “Classification of EEG signals using neural network and logistic regression,” Comput. Meth. Progr. Biomed., Vol. 78, no. 2, pp. 87–99, 2005. doi:10.1016/j.cmpb.2004.10.009.
  • E. M. Thomas, A. Temko, G. Lightbody, W. P. Marnane, and G. B. Boylan. “A Gaussian mixture model based statistical classification system for neonatal seizure detection,” In: 2009 IEEE International Workshop on Machine Learning for Signal Processing, 1–6, 2009. doi:10.1109/MLSP.2009.5306203.
  • Y. Li, et al., “Epileptic seizure detection based on time-frequency images of EEG signals using Gaussian mixture model and gray level co-occurrence matrix features,” Int. J. Neural Syst., Vol. 28, no. 07, pp. 1850003, 2018. doi:10.1142/S012906571850003X.
  • M. Sabeti, S. D. Katebi, R. Boostani, and G. W. Price, “A new approach for EEG signal classification of schizophrenic and control participants,” Expert Syst. Applic., Vol. 38, no. 3, pp. 2063–2071, 2011. doi:10.1016/j.eswa.2010.07.145.
  • S. Chatterjee, S. Pratiher, and R. Bose, “Multifractal detrended fluctuation analysis based novel feature extraction technique for automated detection of focal and non-focal electroencephalogram signals,” IET Sci. Measur. Technol., Vol. 11, no. 8, pp. 1014–1021, 2017. doi:10.1049/iet-smt.2017.0117.
  • A. Liu, et al., “Feature selection for motor imagery EEG classification based on firefly algorithm and learning automata,” Sensors, Vol. 17, no. 11, pp. 2576, 2017. doi:10.3390/s17112576.
  • S. Mohdiwale, M. Sahu, G. R. Sinha, and V. Bhateja, “Statistical wavelets with harmony search- based optimal feature selection of EEG signals for motor imagery classification,” IEEE Sensors J., Vol. 21, no. 13, pp. 14263–14271, 2021. doi:10.1109/JSEN.2020.3026172.
  • M. Sreeshakthy, and J. Preethi, “Classification of human emotion from deapeeg signal using hybrid improved neural networks with cuckoo search,” BRAIN. Broad Res. Artif. Intell. Neurosci., Vol. 63, no. 4, pp. 60–73, 2016.
  • X. Liu, and H. Fu, “PSO-based support vector machine with cuckoo search technique for clinical disease diagnoses,” The Scientific World J., Vol. 43, no. 1, pp. 1–7, 2014.
  • Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE, Vol. 86, no. 11, pp. 2278–2324, 1998. doi:10.1109/5.726791.
  • S. Ruder. “An overview of gradient descent optimization algorithms.” arXiv preprint arXiv:1609.04747, pp. 1–14, 2016.
  • V. Maeda-Gutiérrez, et al., “Comparison of convolutional neural network architectures for classification of tomato plant diseases,” Appl. Sci., Vol. 10, no. 4, pp. 1–15, 2020.
  • M. G. Shankar, and C. Ganesh Babu, “An exploration of ECG signal feature selection and classification using machine learning techniques,” Int. J. Innov. Technol. Explor. Eng. Regul., Vol. 9, no. 3, pp. 797–804, 2020. doi:10.35940/ijitee.C8728.019320.
  • D. Chicco, and G. Jurman, “The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation,” BMC Gen., Vol. 21, no. 1, pp. 1–13, 2021.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.