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

Authors

Saman Sargolzaei, Department of Electrical and Computer Engineering, Florida International UniversityFollow
Mercedes Cabrerizo, Department of Electrical and Computer Engineering, Florida International UniversityFollow
Arman Sargolzaei, Department of Electrical and Computer Engineering, Florida International UniversityFollow
Shirin Noei, Department of Civil and Environmental Engineering, Florida International UniversityFollow
Anas Salah Eddin, Florida Polytechnic University
Hoda Rajaei, Department of Electrical and Computer Engineering, Florida International UniversityFollow
Alberto Pinzon-Ardila, Baptist HospitalFollow
Sergio M. Gonzalez-Arias, Baptist Hospital
Prasanna Jayakar, Miami Children's Hospital
Malek Adjouadi, Department of Electrical and Computer Engineering, Florida International UniversityFollow

Date of this Version

12-2014

Document Type

Article

Abstract

Background

The 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.

Methods

A 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.

Results

The 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.

Conclusions

The current study created the potential of diagnosing epilepsy without need for long EEG recording session and time-consuming visual inspection as conventionally employed.

Comments

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

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

Recommended Citation

Sargolzaei et al.: A probabilistic approach for pediatric epilepsy diagnosis using brain functional connectivity networks. BMC Bioinformatics 2015 16(Suppl 7):S9.