A probabilistic approach for pediatric epilepsy diagnosis using brain functional connectivity networks

BMC Bioinformatics

Research

Alberto Pinzon-Ardila¹  Sergio M Gonzalez-Arias¹  Prasanna Jayakar²  Anas Salah Eddin³  Shirin Noei⁴  Mercedes Cabrerizo⁵  Hoda Rajaei⁵  Arman Sargolzaei⁵  Malek Adjouadi⁵  Saman Sargolzaei⁵ 

[1] Baptist Health Neuroscience Center, Baptist Hospital, Miami, USA;Brain Institute, Miami Children's Hospital, Miami, USA;College of Innovation and Technology, Florida Polytechnic University, Lakeland, USA;Department of Civil and Environmental Engineering, Florida International University, Miami, USA;Department of Electrical and Computer Engineering, Florida International University, 33174, Miami, FL, USA;

关键词: Gaussian Mixture Model;    Small World Network;    Connectivity Strength;    Pediatric Epilepsy;    Interictal Spike;   

DOI  :  10.1186/1471-2105-16-S7-S9

来源: Springer

【 摘 要 】

BackgroundThe lives of half a million children in the United States are severely affected due to the alterations in their functional and mental abilities which epilepsy causes. This study aims to introduce a novel decision support system for the diagnosis of pediatric epilepsy based on scalp EEG data in a clinical environment.MethodsA new time varying approach for constructing functional connectivity networks (FCNs) of 18 subjects (7 subjects from pediatric control (PC) group and 11 subjects from pediatric epilepsy (PE) group) is implemented by moving a window with overlap to split the EEG signals into a total of 445 multi-channel EEG segments (91 for PC and 354 for PE) and finding the hypothetical functional connectivity strengths among EEG channels. FCNs are then mapped into the form of undirected graphs and subjected to extraction of graph theory based features. An unsupervised labeling technique based on Gaussian mixtures model (GMM) is then used to delineate the pediatric epilepsy group from the control group.ResultsThe study results show the existence of a statistically significant difference (p < 0.0001) between the mean FCNs of PC and PE groups. The system was able to diagnose pediatric epilepsy subjects with the accuracy of 88.8% with 81.8% sensitivity and 100% specificity purely based on exploration of associations among brain cortical regions and without a priori knowledge of diagnosis.ConclusionsThe current study created the potential of diagnosing epilepsy without need for long EEG recording session and time-consuming visual inspection as conventionally employed.

【 授权许可】

CC BY   
© Sargolzaei et al.; licensee BioMed Central Ltd. 2015