Abstract
Learning the electrophysiological activities inside the human mind is a significant step toward studying the human brain. Systems, such as electroencephalography, are significant instruments for considering the neurophysiologic activities, in view of their high value of temporal and spatial resolution. In the biomedical research, identifying brain abnormalities such as autism spectrum disorder through electroencephalography (EEG) signals is an extremely exhausting issue for specialists and human services experts. The high volume of data available with EEG will be a useful biomarker for the classification of autism and typical children. Traditional techniques face challenges to deal with such big data. So we present a strategy for autism identification by analyzing the EEG signal through mathematical model. One such modeling using graph theory is applied in this work. The EEG signals are acquired from 3 autism and 3 typical children. The functional connectivity among the neuron regions are plotted through small world networks. From this graphical models using a software tool Gephi, the graphical parameters as betweenness centrality, degree, weighted degree, closeness centrality, modularity, and clustering coefficient are calculated. There is significant difference among these parameters between autistic and typical children.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Sato JR, Calebe Vidal M, de Siqueira Santos S, Brauer Massirer K, Fujita A (2018) Complex network measures in autism spectrum disorders. IEEE/ACM Trans Comput Biol Bioinform (TCBB) 15(2):581–587
Dejman A, Khadem A, Khorrami A, Comparing the effective connectivity graphs estimated by granger causality index with transfer entropy: a case study on Autism spectrum disorders. In: 2017 24th national and 2nd international Iranian conference on biomedical engineering (ICBME). IEEE, New York, pp 14–15
Riedel BC, Jahanshad N, Thompson PM (2017, April) Graph theoretical approaches towards understanding differences in frontoparietal and default mode networks in Autism. In: 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017). IEEE, New York, pp 460–463
Fan J, Wade JW, Key AP, Warren ZE, Sarkar N (2017) EEG-based affect and workload recognition in a virtual driving environment for ASD intervention. IEEE Trans Biomed Eng 65(1):43–51
Ali S, Mehmood F, Dancey D, Ayaz Y, Khan MJ, Naseer N, Nawaz R (2019) An adaptive multi-robot therapy for improving joint attention and imitation of ASD children. IEEE Access 7:81808–81825
Wang Q, Sourina O (2013) Real-time mental arithmetic task recognition from EEG signals. IEEE Trans Neural Syst Rehabil Eng 21(2):225–232
Fallani FDV, Astolfi L, Cincotti F, Mattia D, Tocci A, Salinari S, Babiloni F (2008) Brain network analysis from high-resolution EEG recordings by the application of theoretical graph indexes. IEEE Trans Neural Syst Rehabil Eng 16(5):442–452
Varatharajah Y, Chong, MJ, Saboo K, Berry B, Brinkmann B, Worrell G, Iyer R (2017) EEG-GRAPH: a factor-graph-based model for capturing spatial, temporal, and observational relationships in electroencephalograms. In: Advances in neural information processing systems, pp 5371–5380
Supriya S, Siuly S, Wang H, Cao J, Zhang Y (2016) Weighted visibility graph with complex network features in the detection of epilepsy. IEEE Access 4:6554–6566
Andersen LR, Krebs JH, Andersen JD (1991) STENO: An expert system for medical diagnosis based on graphical models and model search. J Appl Stat 18(1):139–153
Fan M, Chou CA (2018) Detecting abnormal pattern of epileptic seizures via temporal synchronization of EEG signals. IEEE Trans Biomed Eng 66(3):601–608
Cociu BA, Das S, Billeci L, Jamal W, Maharatna K, Calderoni S, Muratori F (2017) Multimodal functional and structural brain connectivity analysis in autism: a preliminary integrated approach with EEG, fMRI, and DTI. IEEE Trans Cogn Dev Syst 10(2):213–226
Storti SF, Galazzo IB, Khan S, Manganotti P, Menegaz G (2016) Exploring the epileptic brain network using time-variant effective connectivity and graph theory. IEEE J Biomed Health Inform 21(5):1411–1421
Smith K, Spyrou L, Escudero J (2018) Graph-variate signal analysis. IEEE Trans Signal Process 67(2):293–305
Shams WK, Wahab A (2013, March) Source-temporal-features for detection EEG behavior of autism spectrum disorder. In: 2013 5th international conference on information and communication technology for the Muslim world (ICT4M). IEEE, New York, pp 1–5
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mehta, N.P., Menaka, R., Prasad, A.S., Aarthy, T. (2021). Graphical Model and Model Search for Medical Data Analysis. In: Zhou, N., Hemamalini, S. (eds) Advances in Smart Grid Technology. Lecture Notes in Electrical Engineering, vol 688. Springer, Singapore. https://doi.org/10.1007/978-981-15-7241-8_36
Download citation
DOI: https://doi.org/10.1007/978-981-15-7241-8_36
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-7240-1
Online ISBN: 978-981-15-7241-8
eBook Packages: EnergyEnergy (R0)