Abstract
In the recent past, the micro vascular cranial nerve palsy has been detected from the EEG signal using the discrete wavelet transform and multi class support vector machine approach which examines each and every frequencies and features with effective manner. Though the epilepsy are identified using the various techniques, the accuracy and efficiency of the system with less error rate of the classifiers are still one of the major issues in Medical Internet of Things Environment (MIoT). Even though these methods retrieves the cranial nerve palsy which is termed as lack of function of nerves successfully, the efficiency of the system is must be improved. So effective epilepsy which causes cranial nerve palsy need to be analyzed and detect in an automatic manner for minimizing the number of deaths. These problems are reduced by using optimized signal decomposition, Exact feature extraction, selection and the recognition with less error rate has been computed with the help of the Fuzzy based twofold graphic discrete wavelet transform (FTF-TGTWT), hybrid Fuzzy based spearman rank correlation (HF-SRC) Then the performance of the system is analyzed using the experimental results and discussions.
Similar content being viewed by others
References
Anand S, Nath V. Study and Design of Smart Embedded System for remote health monitoring using internet of things. In: Nanoelectronics, circuits and communication systems. Singapore: Springer; 2019. p. 409–14.
Chu NN. Surprising prevalence of electroencephalogram brain-computer Interface to internet of things [future directions]. IEEE Consumer Electronics Magazine. 2017;6(2):31–9.
Kim YJ, Lee JY, Oh S, Park M, Jung HY, Sohn BK, et al. Associations between prospective symptom changes and slow-wave activity in patients with internet gaming disorder: a resting-state EEG study. Medicine. 2017;96(8).
Hussain SA, Mohammed H, Hussain SJ. Detection of brain activity with an automated system hardware for accurate diagnostic of mental disorders. In: Proceedings of the Second International Conference on Internet of things and Cloud Computing. ACM; 2017. p. 79.
Matsuo K, Yamada M, Bylykbashi K, Cuka M, Liu Y, Barolli L. Implementation of an IoT-Based E-Learning Testbed: performance evaluation using mean-shift clustering approach considering four types of brain waves. In: 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA). IEEE; 2018. p. 203–209.
Maeda Y, Kudomi N, Yamamoto Y, Hatakeyama T, Nishiyama Y. Impact of reconstruction algorithm with PSF and TOF and reconstruction parameter in fractal analysis: evaluation by changed the Gaussian filter size. J Nucl Med. 2018;59(supplement 1):1858.
Alickovic E, Kevric J, Subasi A. Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction. Biomedical Signal Processing and Control. 2018;39:94–102.
Mafarja MM, Mirjalili S. Hybrid binary ant lion optimizer with rough set and approximate entropy reducts for feature selection. Soft Computing in Springer. 2018:1–17.
Liu Y, Jiang C, Zhao H. Using contextual features and multi-view ensemble learning in product defect identification from online discussion forums. Decis Support Syst. 2018;105:1–12.
Zhang L, Zhang Q, Du B, Huang X, Tang YY, Tao D. Simultaneous spectral-spatial feature selection and extraction for hyperspectral images. IEEE Transactions on Cybernetics. 2018;48(1):16–28.
Lu H, Li Y, Chen M, Kim H, Serikawa S. Brain intelligence: go beyond artificial intelligence. Mobile Networks and Applications. 2018;23(2):368–75.
Manogaran G, Vijayakumar V, Varatharajan R, Kumar PM, Sundarasekar R, Hsu CH. Machine learning based big data processing framework for cancer diagnosis using hidden Markov model and GM clustering. Wirel Pers Commun. 2018;102(3):2099–116.
Jagadeeswari V, Subramaniyaswamy V, Logesh R, Vijayakumar V. A study on medical internet of things and big data in personalized healthcare system. Health Information Science and Systems in Springer. 2018;6(1):14.
Yamada M, Cuka M, Liu Y, Oda T, Matsuo K, Barolli L. Performance evaluation of an IoT-based E-learning testbed using mean-shift clustering approach considering Theta type of brain waves. In: International conference on intelligent networking and collaborative systems. Cham: Springer; 2017, August. p. 62–72.
Waldstein SM, Montuoro A, Podkowinski D, Philip AM, Gerendas BS, Bogunovic H, et al. Evaluating the impact of vitreomacular adhesion on anti-VEGF therapy for retinal vein occlusion using machine learning. Sci Rep. 2017;7(1):2928.
Steele VR, Rao V, Calhoun VD, Kiehl KA. Machine learning of structural magnetic resonance imaging predicts psychopathic traits in adolescent offenders. NeuroImage. 2017;145:265–73.
Khosravan N, Celik H, Turkbey B, Jones E, Wood B, Bagci U. A Collaborative Computer Aided Diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning. J Med Image Anal. 2018;51:101–115. https://doi.org/10.1016/j.media.2018.10.010.
Kohli MD, Summers RM, Geis JR. Medical image data and datasets in the era of machine learning—whitepaper from the 2016 C-MIMI meeting dataset session. J Digit Imaging. 2017;30(4):392–9.
Narang A, Batra B, Ahuja A, Yadav J, Pachauri N. Classification of EEG signals for epileptic seizures using Levenberg-Marquardt algorithm based multilayer perceptron neural network. J Intell Fuzzy Syst. 2018;34(3):1669–77.
Talboom JS, Huentelman MJ. Big data collision: the internet of things, wearable devices and genomics in the study of neurological traits and disease. Hum Mol Genet. 2018;27(R1):R35–9.
Rahmani AM, Gia TN, Negash B, Anzanpour A, Azimi I, Jiang M, et al. Exploiting smart e-health gateways at the edge of healthcare internet-of-things: a fog computing approach. Futur Gener Comput Syst. 2018;78:641–58.
Tzimourta KD, Tzallas AT, Giannakeas N, Astrakas LG, Tsalikakis DG, Angelidis P, et al. A robust methodology for classification of epileptic seizures in EEG signals. Heal Technol. 2018:1–8.
Vergara, P. M., de la Cal, E., Villar, J. R., González, V. M., & Sedano, J. (2017). An IoT platform for epilepsy monitoring and supervising. Journal of Sensors 2017:18. https://doi.org/10.1155/2017/6043069
Hamad A, Houssein EH, Hassanien AE, Fahmy AA. Hybrid grasshopper optimization algorithm and support vector Machines for automatic seizure detection in EEG signals. In: International conference on advanced machine learning technologies and applications. Cham: Springer; 2018. p. 82–91.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Ethical approval
This article does not contain any studies with animals performed by any of the authors.
Conflict of interest
The authors declare that they have no conflict of interest.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the Internet Of Medical Things In E-Health Hassan Fouad Mohamed-El-Sayed and M. Hemalatha
Rights and permissions
About this article
Cite this article
Baskar, S., Dhulipala, V.R.S., Shakeel, P.M. et al. Hybrid fuzzy based spearman rank correlation for cranial nerve palsy detection in MIoT environment. Health Technol. 10, 259–270 (2020). https://doi.org/10.1007/s12553-019-00294-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12553-019-00294-8