Hybrid fuzzy based spearman rank correlation for cranial nerve palsy detection in MIoT environment
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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.
KeywordsMIoT Fuzzy Cranial nerve palsy Spearman rank correlation Discrete wavelet transform
Compliance with ethical standards
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 was obtained from all individual participants included in the study.
- 3.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).Google Scholar
- 4.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.Google Scholar
- 5.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.Google Scholar
- 6.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.Google Scholar
- 8.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.Google Scholar
- 13.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.Google Scholar
- 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.Google Scholar
- 17.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.
- 22.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.Google Scholar
- 23.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
- 24.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.Google Scholar