|
UPSI Digital Repository (UDRep)
|
|
| ||||||||||||||||||||||||||
| Abstract : Universiti Pendidikan Sultan Idris |
| The World Health Organization (WHO) recommended Cerebral disorders treatment within 90 minutes of the first medical encounter. Where the specialist should recover blood flow to the brain rapidly by injecting intravenous drugs in emergency situations. Tele Encephalopathy Diagnosis includes the use of advanced communication technology to convey to the off-site neurologist the pre-hospital 21-lead EEG signal for early triage, which has been shown to minimize time and overall mortality dramatically. However, hospitals also find the implementation of EEG transmission technologies very difficult to implement. Seven major technological obstacles to pre-hospital EEG transmission are established in the project research, in particular, paramedical discomfort and transport delays; signal noise and processing errors; system failure and communication losses; cellular network reliability; non-compliance with digital EEG format requirements; poor communication with electronic medical records; and cost-effective compliance with digital EEG format requirements. In order to solve both of these technical obstacles, current and potential strategies are studied in detail and contain automatic EEG transmitting protocols; remarkable waveform EEGs; optimal routing strategies; and the cloud computing services utilization instead of vendor-specific processing stations. However, transmission quality management strategies and patient observations are necessary to maintain the primary progress in the application of EEG transmission technologies. ? School of Engineering, Taylor's University. |
| References |
Abdulbaqi, A. S., Najim, S. A. -. M., & Mahdi, R. H. (2018). Robust multichannel EEG signals compression model based on hybridization technique. International Journal of Engineering and Technology(UAE), 7(4), 3402-3405. doi:10.14419/ijet.v7i4.14513 Abdulbaqi, A. S., Nejrs, S. M., Mahmood, S. D., & Panessai, I. Y. (2021). A tele encephalopathy diagnosis based on EEG signal compression and encryption doi:10.1007/978-981-33-6835-4_10 Retrieved from www.scopus.com Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., Adeli, H., & Subha, D. P. (2018). Automated EEG-based screening of depression using deep convolutional neural network. Computer Methods and Programs in Biomedicine, 161, 103-113. doi:10.1016/j.cmpb.2018.04.012 Ahmadlou, M., Adeli, H., & Adeli, A. (2011). Fractality and a wavelet-chaos-methodology for EEG-based diagnosis of alzheimer disease. Alzheimer Disease and Associated Disorders, 25(1), 85-92. doi:10.1097/WAD.0b013e3181ed1160 Al Shalabi, L., & Shaaban, Z. (2006). Normalization as a preprocessing engine for data mining and the approach of preference matrix. Paper presented at the Proceedings of International Conference on Dependability of Computer Systems, DepCoS-RELCOMEX 2006, 207-214. doi:10.1109/DEPCOS-RELCOMEX.2006.38 Retrieved from www.scopus.com Aydemir, E., Tuncer, T., & Dogan, S. (2020). A tunable-Q wavelet transform and quadruple symmetric pattern based EEG signal classification method. Medical Hypotheses, 134 doi:10.1016/j.mehy.2019.109519 Bhurane, A. A., Sharma, M., San-Tan, R., & Acharya, U. R. (2019). An efficient detection of congestive heart failure using frequency localized filter banks for the diagnosis with ECG signals. Cognitive Systems Research, 55, 82-94. doi:10.1016/j.cogsys.2018.12.017 Dalgleish, T., & Power, M. (1999). Handbook of Cognition and Emotion, Retrieved from www.scopus.com Dash, D. P., & Kolekar, M. H. (2017). Epileptic seizure detection based on EEG signal analysis using hierarchy based hidden markov model. Paper presented at the 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, , 2017-January 1114-1120. doi:10.1109/ICACCI.2017.8125991 Retrieved from www.scopus.com De Souto, M. C. P., De Araujo, D. S. A., Costa, I. G., Soares, R. G. F., Ludermir, T. B., & Schliep, A. (2008). Comparative study on normalization procedures for cluster analysis of gene expression datasets. Paper presented at the Proceedings of the International Joint Conference on Neural Networks, 2792-2798. doi:10.1109/IJCNN.2008.4634191 Retrieved from www.scopus.com Eesa, A. S., & Arabo, W. K. (2017). A normalization methods for backpropagation: A comparative study. Sci J Univ Zakho., 5(4), 319-323. Retrieved from www.scopus.com Fonseca, I. L. (2003). The impact of data normalisation on unsupervised continuous classification of landforms. Paper presented at the International Geoscience and Remote Sensing Symposium (IGARSS), , 6 3426-3428. Retrieved from www.scopus.com Han, J., & Kamber, M. (2001). Data mining: Concepts and techniques. Data Mining: Concepts and Techniques, Retrieved from www.scopus.com Harender, & Sharma, R. K. (2018). DWT based epileptic seizure detection from EEG signal using k-NN classifier. Paper presented at the Proceedings - International Conference on Trends in Electronics and Informatics, ICEI 2017, , 2018-January 762-765. doi:10.1109/ICOEI.2017.8300806 Retrieved from www.scopus.com Hsu, C. -., Chang, C. -., & Lin, C. -. (2003). A practical guide to support vector classification. A Practical Guide to Support Vector Classification, Retrieved from www.scopus.com Jatupaiboon, N., Pan-Ngum, S., & Israsena, P. (2013). Emotion classification using minimal EEG channels and frequency bands. Paper presented at the Proceedings of the 2013 10th International Joint Conference on Computer Science and Software Engineering, JCSSE 2013, 21-24. doi:10.1109/JCSSE.2013.6567313 Retrieved from www.scopus.com Jayalakshmi, T., & Santhakumaran, A. (2011). Statistical normalization and back propagation for classification. International Journal of Computer Theory and Engineering, 3(1), 1793-8201. Retrieved from www.scopus.com Jenke, R., Peer, A., & Buss, M. (2014). Feature extraction and selection for emotion recognition from EEG. IEEE Transactions on Affective Computing, 5(3), 327-339. doi:10.1109/TAFFC.2014.2339834 Jiang, Z., Chung, F. -., & Wang, S. (2019). Recognition of multiclass epileptic EEG signals based on knowledge and label space inductive transfer. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(4), 630-642. doi:10.1109/TNSRE.2019.2904708 Kociolek, M., Strzelecki, M., & Szymajda, S. (2018). On the influence of the image normalization scheme on texture classification accuracy. Paper presented at the Signal Processing - Algorithms, Architectures, Arrangements, and Applications Conference Proceedings, SPA, , 2018-September 152-157. doi:10.23919/SPA.2018.8563397 Retrieved from www.scopus.com Koelstra, S., Mühl, C., Soleymani, M., Lee, J. -., Yazdani, A., Ebrahimi, T., . . . Patras, I. (2012). DEAP: A database for emotion analysis; using physiological signals. IEEE Transactions on Affective Computing, 3(1), 18-31. doi:10.1109/T-AFFC.2011.15 Kul, S., Durdu, P. O., & Akbulut, O. (2019). Performance comparison of EEG channels in emotion recognition. Paper presented at the 27th Signal Processing and Communications Applications Conference, SIU 2019, doi:10.1109/SIU.2019.8806538 Retrieved from www.scopus.com Kulkarni, N., & Bairagi, V. (2018). EEG-based diagnosis of alzheimer disease. Chapter Two: Electroencephalogram and its use in Clinical Neuroscience, , 25-35. Retrieved from www.scopus.com Kumar, P., Singhal, A., Saini, R., Roy, P. P., & Dogra, D. P. (2018). A pervasive electroencephalography-based person authentication system for cloud environment. Displays, 55, 64-70. doi:10.1016/j.displa.2018.09.006 Lee, W., Kim, S., Kim, B., Lee, C., Chung, Y. A., Kim, L., & Yoo, S. -. (2017). Non-invasive transmission of sensorimotor information in humans using an EEG/focused ultrasound brain-to-brain interface. PLoS ONE, 12(6) doi:10.1371/journal.pone.0178476 Lehmann, C., Koenig, T., Jelic, V., Prichep, L., John, R. E., Wahlund, L. -., . . . Dierks, T. (2007). Application and comparison of classification algorithms for recognition of alzheimer's disease in electrical brain activity (EEG). Journal of Neuroscience Methods, 161(2), 342-350. doi:10.1016/j.jneumeth.2006.10.023 Lemesle, M., Kubis, N., Sauleau, P., N'Guyen The Tich, S., & Touzery-de Villepin, A. (2015). Tele-transmission of EEG recordings. Neurophysiologie Clinique, 45(1), 121-130. doi:10.1016/j.neucli.2014.12.001 Liu, Y., Huang, H., Xiao, F., Malekian, R., & Wang, W. (2020). Classification and recognition of encrypted EEG data based on neural network. Journal of Information Security and Applications, 54 doi:10.1016/j.jisa.2020.102567 Lou, S., Feng, Y., Li, Z., Zheng, H., & Tan, J. (2020). An integrated decision-making method for product design scheme evaluation based on cloud model and EEG data. Advanced Engineering Informatics, 43 doi:10.1016/j.aei.2019.101028 Louis, E. K. S., & Frey, L. C. (2016). Electroencephalography (EEG): An introductory text and atlas of normal and abnormal findings in adults, children, and infants. Electroencephalography (EEG): An Introductory Text and Atlas of Normal and Abnormal Findings in Adults, Children, and Infants, Retrieved from www.scopus.com Majumdar, A., & Ward, R. K. (2015). Energy efficient EEG sensing and transmission for wireless body area networks: A blind compressed sensing approach. Biomedical Signal Processing and Control, 20, 1-9. doi:10.1016/j.bspc.2015.03.002 Medithe, J. W. C., & Nelakuditi, U. R. (2016). Study of normal and abnormal EEG. Paper presented at the ICACCS 2016 - 3rd International Conference on Advanced Computing and Communication Systems: Bringing to the Table, Futuristic Technologies from Arround the Globe, doi:10.1109/ICACCS.2016.7586341 Retrieved from www.scopus.com Michielli, N., Acharya, U. R., & Molinari, F. (2019). Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals. Computers in Biology and Medicine, 106, 71-81. doi:10.1016/j.compbiomed.2019.01.013 Mikhail, M., El-Ayat, K., Coan, J. A., & Allen, J. J. B. (2013). Using minimal number of electrodes for emotion detection using brain signals produced from a new elicitation technique. International Journal of Autonomous and Adaptive Communications Systems, 6(1), 80-97. doi:10.1504/IJAACS.2013.050696 Milligan, G. W., & Cooper, M. C. (1988). A study of standardization of variables in cluster analysis. Journal of Classification, 5(2), 181-204. doi:10.1007/BF01897163 Mohamad, I. B., & Usman, D. (2013). Standardization and its effects on K-means clustering algorithm. Research Journal of Applied Sciences, Engineering and Technology, 6(17), 3299-3303. doi:10.19026/rjaset.6.3638 Panessai, I. I. Y., & Abdulbaqi, A. S. (2019). An efficient method of EEG signal compression and transmission based telemedicine. Journal of Theoretical and Applied Information Technology, 97(4), 1060-1070. Retrieved from www.scopus.com Patel, V. R., & Mehta, R. G. (2011). Impact of outlier removal and normalization approach in modified k-means clustering algorithm. International Journal of Computer Science Issues, 8(5), 331-336. Retrieved from www.scopus.com Rachim, V. P., Jiang, Y., Lee, H. -., & Chung, W. -. (2017). Demonstration of long-distance hazard-free wearable EEG monitoring system using mobile phone visible light communication. Optics Express, 25(2), 713-719. doi:10.1364/OE.25.000713 Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161-1178. doi:10.1037/h0077714 Sharma, M., Patel, S., Choudhary, S., & Acharya, U. R. (2020). Automated detection of sleep stages using energy-localized orthogonal wavelet filter banks. Arabian Journal for Science and Engineering, 45(4), 2531-2544. doi:10.1007/s13369-019-04197-8 Sharma, M., & Rajendra Acharya, U. (2019). A new method to identify coronary artery disease with ECG signals and time-frequency concentrated antisymmetric biorthogonal wavelet filter bank. Pattern Recognition Letters, 125, 235-240. doi:10.1016/j.patrec.2019.04.014 Sharma, M., Raval, M., & Acharya, U. R. (2019). A new approach to identify obstructive sleep apnea using an optimal orthogonal wavelet filter bank with ECG signals. Informatics in Medicine Unlocked, 16 doi:10.1016/j.imu.2019.100170 Sharma, M., Singh, S., Kumar, A., San Tan, R., & Acharya, U. R. (2019). Automated detection of shockable and non-shockable arrhythmia using novel wavelet-based ECG features. Computers in Biology and Medicine, 115 doi:10.1016/j.compbiomed.2019.103446 Sharma, M., Tan, R. S., & Acharya, U. R. (2018). A novel automated diagnostic system for classification of myocardial infarction ECG signals using an optimal biorthogonal filter bank. Computers in Biology and Medicine, 102, 341-356. doi:10.1016/j.compbiomed.2018.07.005 Sharma, M., Tan, R. -., & Acharya, U. R. (2020). Detection of shockable ventricular arrhythmia using optimal orthogonal wavelet filters. Neural Computing and Applications, 32(20), 15869-15884. doi:10.1007/s00521-019-04061-8 Singh, B., Kaur, A., & Singh, J. (2015). A review of ECG data compression techniques. International Journal of Computer Applications, 116(19), 11-15. Retrieved from www.scopus.com Sudeep, K. S., & Pal, K. K. (2017). Preprocessing for image classification by convolutional neural networks. Paper presented at the 2016 IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016 - Proceedings, 1778-1781. doi:10.1109/RTEICT.2016.7808140 Retrieved from www.scopus.com Theodoridis, S., & Koutroumbas, K. (1999). Pattern Recognition, Retrieved from www.scopus.com Xie, L., Tian, Q., & Zhang, B. (2013). Feature normalization for part-based image classification. Paper presented at the 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings, 2607-2611. doi:10.1109/ICIP.2013.6738537 Retrieved from www.scopus.com Zhang, H., Lin, H., & Li, Y. (2015). Impacts of feature normalization on optical and SAR data fusion for land use/land cover classification. IEEE Geoscience and Remote Sensing Letters, 12(5), 1061-1065. doi:10.1109/LGRS.2014.2377722 Zhang, J., Chen, M., Zhao, S., Hu, S., Shi, Z., & Cao, Y. (2016). ReliefF-based EEG sensor selection methods for emotion recognition. Sensors (Switzerland), 16(10) doi:10.3390/s16101558 |
| This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials. You may use the digitized material for private study, scholarship, or research. |