2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 2 , Issue 1 , PP: 14-21, 2020 | Cite this article as | XML | Html | PDF | Full Length Article
Shaymaa Adnan Abdulrahman 1 * , Rafah Amer Jaafar 2
Doi: https://doi.org/10.54216/FPA.020103
Alcoholism may be recognized with the use of (EEG) analyzing signals. None-the-less, the analysis of the multi-channel signals of EEG is a complicated issue that usually needs performing complex computation operations and takes quite a long time to execute. The presented research will propose 13 optimal channel to feature extraction. In this research, an innovative horizontal visibility graph entropy (HVGE) method has been proposed for evaluating signals of EEG from controlled drinkers and alcoholic subjects and comparing against an approach of sample entropy (SaE). Values of HVGE and SaE have been obtained from 1200 records of bio-medical signals. While in classification step using SVM as classifier.
Alcoholics , Support Vector Machine , Using Electroencephalogram Signal , Sample Entropy , Classification.
[1]Abdulkader, S.N., Atia, A., Mostafa, M.S.M.: Brain computer interfacing: applications and challenges. Egypt. Inf. J. 16, 213–230 ,2015
[2]Acharya UR, Subburam VS, Chattopadhya S, Suri J, ‘ Automated diagnosis of normal and alcoholic EEG signals’, International Journal of Neural systems, June, pp;1-9, 2012
[3]Bache K, Lichman M (2013) UCI machine learning repository. University of California, Irvine, School of Information and Computer. http://archive.ics.uci.edu/ml
[4]Faust O, Acharya UR, Alen A and Lim CM, Analysis of EEG signals during epileptic and alcoholic states using AR modeling techniques, Innovations and Technology in Biology and Medicine (ITBM-RBM) 29(1) 44-52, 2008
[5]Hayden EP, Wiegand RE, Meyer ET, Bauer LO, O’Connor SJ, Nurnberger JI, Chorlian DB, Porjesz B, Begleiter H Patterns of regional brain activity in alcohol-dependent subjects. Alcohol: Clin Experiment Res 30(12):1986–.2006, 1991
[6]Hussain L, Aziz W, Khan AS, Abbasi AQ, Hassan SZ, Abbasi MM, ‘Classification of EEG Alcoholic and Control subjects using machine learning ensemble methods’, Journal of Multidisciplinary Engineering Science and Technology, vol 2, issue 1, , pp;126- 131.2015
[7]Lin C, Xiong S, ‘ Alcoholism classification based on ICA and SVM Method, Project Report, Rutgers University, pp;1-7.
[8]Oscar-Berman M, Marinkovi K Alcohol: effects on neurobehavioral functions and the brain. Neuropsychol Rev 17(3):239–257, 2007
[9]Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol-Heart Circulat Physiol 278(6):H2039–H2049
[10]Shannon CE (2001) A mathematical theory of communication. ACM SIGMOBILE Mob Computer Commun Rev 5(1):3–55
[11]Shaymaa Adnan Abdulrahman, Wael Khalifa, Mohamed Roushdy, Abdel-Badeeh M. Salem " A survey of biometrics using electroencephalogram EEG " International Journal "Information Content and Processing", Volume 6, Number 1, 2019
[12]Shaymaa Adnan Abdulrahman, Mohamed Roushdy, Abdel-Badeeh M. Salem " Human Identification based on electroencephalography Signals using Sample Entropy and Horizontal Visibility Graphs " WSEAS TRANSACTIONS on SIGNAL PROCESSING , ISSN: 2224-3488 Volume 15, 2019
[13]Shaymaa Adnan Abdulrahman, Mohamed Roushdy, Abdel-Badeeh M. Salem "support vector machine approach for human identification based on EEG signals , journal of mechanics of continua and mathematical sciences , ISSN (Online) : 2454 -7190 Vol.-15, No.-2, pp 270-280 ISSN (Print) 0973-8975, February 2020
[14]Shaymaa Adnan Abdulrahman, Wael Khalifa, Mohamed Roushdy, Abdel-Badeeh M. Salem" Comparative Study for 8 Computational Intelligence Algorithms for Human Identification" Computer Science Review Journal, Vol 36 , 2020
[15] Mohammed, M.A., Al-Khateeb, B., Rashid, A.N., Ibrahim, D.A., Abd Ghani, M.K. and Mostafa, S.A., 2018. Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images. Computers & Electrical Engineering, 70, pp.871-882.
[16] Mohammed, M.A., Abd Ghani, M.K., Arunkumar, N.A., Hamed, R.I., Abdullah, M.K. and Burhanuddin, M.A., 2018. A real time computer aided object detection of nasopharyngeal carcinoma using genetic algorithm and artificial neural network based on Haar feature fear. Future Generation Computer Systems, 89, pp.539-547.
[17] Mohammed, M.A., Abd Ghani, M.K., Hamed, R.I., Ibrahim, D.A. and Abdullah, M.K., 2017. Artificial neural networks for automatic segmentation and identification of nasopharyngeal carcinoma. Journal of computational science, 21, pp.263-274.
[18]Zhu G, Li Y, Wen P (2011) Evaluating functional connectivity in alcoholics based on maximal weight matching. J Adv Computat Intell Intell Inform 15(9):1221–1227, 2011
[19]Ziya E, Akif A, Mehmet RB, ‘The Classification of EEG signals recorded in drunk and non-drunk people’, International Journal of Computer Applications, vol.68, no.10, pp;40-44, 2013
[20]Zou Y, Miao D, Wang D (2010) Research on sample entropy of alcoholic and normal people. Chin J Biomed Eng 29:939–942
[21] Mohammed, M.A., Abd Ghani, M.K., Arunkumar, N.A., Mostafa, S.A., Abdullah, M.K. and Burhanuddin, M.A., 2018. Trainable model for segmenting and identifying Nasopharyngeal carcinoma. Computers & Electrical Engineering, 71, pp.372-387.
[22] Abd Ghani, M.K., Mohammed, M.A., Arunkumar, N., Mostafa, S.A., Ibrahim, D.A., Abdullah, M.K., Jaber, M.M., Abdulhay, E., Ramirez-Gonzalez, G. and Burhanuddin, M.A., 2020. Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques. Neural Computing and Applications, 32(3), pp.625-638.
[23] Obaid, O.I., Mohammed, M.A., Ghani, M.K.A., Mostafa, A. and Taha, F., 2018. Evaluating the performance of machine learning techniques in the classification of Wisconsin Breast Cancer. International Journal of Engineering & Technology, 7(4.36), pp.160-166.
