Abstract
Schizophrenia is a mental disorder where the patient experience changes in thought process, behavior and emotion. The changes listed above occur due to chemical imbalance in brain. Due to the nature of the disorder, the patients confront the family members about the things they hear and hallucinate. Initially, the family members deny to queries made by patient and later the responses evolve into anger and quarrel. The family members often lack awareness about schizophrenia disorder. Hence, there is a need to diagnose auditory hallucination at early stage. The auditory hallucination alters the EEG signal in ear. EEG sensor is designed and the same place behind the ear lobe to acquire the change in EEG pattern. For study the EEG pattern, acquire for normal and schizophrenia person while watching different videos namely funny video and horror video. The EEG signal acquire during movie watching task and transmit EEG to the base station through wireless sensor network for the wavelet analysis and classification to evaluate the efficiency of data transmission in various routing algorithms such as AODV and DSR and co channel interference of spread spectrum modulation address.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Qiao, C., Lin, D., Wang, Y.-P.: The effective diagnosis of schizophrenia by using multi-layer RBMs deep networks. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 603–606 (2015)
Deng, S.P., Lin, D., Calhoun, V.D., Wang, Y.P.: Diagnosing schizophrenia by integrating genomic and imaging data through network fusion, In: Proceedings of the 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1307–1313 (2017)
Anjomshoa, M., Dolatshahi, F., Amirkhani, F., Rahmani, M.M., Mirbagheri, M.H., Aarabi, Structural brain network analysis in schizophrenia using minimum spanning tree. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4075–4078 (2016)
Bekele, E., Bian, D., Peterman, J., Park, S., Sarkar, N.: Design of a virtual reality system for affect analysis in facial expressions (VR-SAAFE); application to schizophrenia. IEEE Trans. Neural Syst. Rehabil. Eng. 25(6), 739–749 (2017)
İçer, S., Esra, Ö., Görüntüleme, T., Uygulama, T., Üniversitesi, E.: Obtaining resting state networks in early onset schizophrenia disease by independent component analysis. 2016 Med. Technol. Natl. Congr. 1(2), 176–180 (2016)
Castro, E., Hjelm, R.D., Plis, S.M., Dinh, L., Turner, J.A., Calhoun, V.D.: Deep independence network analysis of structural brain imaging: application to schizophrenia. IEEE Trans. Med. Imaging 35(7), 1729–1740 (2016)
Cetin, M.S., Stephen, J.M., Calhoun, V.: Sensory load hierarchy-based classification of schizophrenia patients. In: Proceedings of International Conference Image Process. ICIP, vol. 2015–Dec, pp. 467–471 (2015)
Deng, S.P., Lin, D., Calhoun, V.D., Wang, Y.P.: Predicting schizophrenia by fusing networks from SNPs, DNA methylation and fMRI data. In: Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS, vol. 2016–Oct, pp. 1447–1450 (2016)
Fajnerova, S.I., Rodriguez, M., Spaniel, F., Horacek, J., Vleck, K., Levcik, D.: Spatial navigation in virtual reality—from animal models towards schizophrenia. 2015 Int. Conf. Virtual Rehabil. 1(2), 44–50 (2015)
Ginanjar, R., Bustamam, A., Tasman, H.: Implementation of regularized markov clustering algorithm on protein interaction networks of schizophrenia’s risk factor candidate genes. 2016 Int. Conf. Adv. Comput. Sci. Inf. Syst. 1(2), 297–302 (2016)
Gomez-Pilar, J. et al.: Novel measure of the weigh distribution balance on the brain network: graph complexity applied to schizophrenia. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS, vol. 2016–Oct, pp. 700–703 (2016)
Hsieh, T.H., Sun, M.J., Liang, S.F.: Musical perception scaling of AEPs from musicians, schizophrenia and normal people. TAAI 2015—2015 Conf. Technol. Appl. Artif. Intell. 1(3), 358–362 (2016)
Huang, M., Lo, P., Chen, C., Chen, C., Cheng, K.: The application of computerized WCST and long-term evoked potentials for schizophrenia analysis. 2015 Int. Conf. Virtual Rehabil. 2(1), 5165–5168 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Nithya, V., Ramesh, G.P. (2020). Wireless EAR EEG Signal Analysis with Stationary Wavelet Transform for Co Channel Interference in Schizophrenia Diagnosis. In: Balas, V., Kumar, R., Srivastava, R. (eds) Recent Trends and Advances in Artificial Intelligence and Internet of Things. Intelligent Systems Reference Library, vol 172. Springer, Cham. https://doi.org/10.1007/978-3-030-32644-9_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-32644-9_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32643-2
Online ISBN: 978-3-030-32644-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)