TY - GEN
T1 - Prediction of seizure spread network via sparse representations of overcomplete dictionaries
AU - Liu, Feng
AU - Xiang, Wei
AU - Wang, Shouyi
AU - Lega, Bradley
N1 - Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - Epilepsy is one of the most common brain disorders and affect people of all ages. Resective surgery is currently the most effective overall treatment for patients whose seizures cannot be controlled by medications. Seizure spread network with secondary epileptogenesis are thought to be responsible for a substantial portion of surgical failures. However, there is still considerable risk of surgical failures for lacking of priori knowledge. Cortico-cortical evoked potentials (CCEP) offer the possibility of understanding connectivity within seizure spread networks to know how seizure evolves in the brain as it measures directly the intracranial electric signals. This study is one of the first works to investigate effective seizure spread network modeling using CCEP signals. The previous unsupervised brain network connectivity problem was converted into a classical supervised sparse representation problem for the first time. In particular, we developed an effective network modeling framework using sparse representation of over-determined features extracted from extensively designed experiments to predict real seizure spread network for each individual patient. The experimental results on five patients achieved prediction accuracy of about 70%, which indicates that it is possible to predict seizure spread network from stimulated CCEP networks. The developed CCEP signal analysis and network modeling approaches are promising to understand network mechanisms of epileptogenesis and have a potential to render clinicians better epilepsy surgical decisions in the future.
AB - Epilepsy is one of the most common brain disorders and affect people of all ages. Resective surgery is currently the most effective overall treatment for patients whose seizures cannot be controlled by medications. Seizure spread network with secondary epileptogenesis are thought to be responsible for a substantial portion of surgical failures. However, there is still considerable risk of surgical failures for lacking of priori knowledge. Cortico-cortical evoked potentials (CCEP) offer the possibility of understanding connectivity within seizure spread networks to know how seizure evolves in the brain as it measures directly the intracranial electric signals. This study is one of the first works to investigate effective seizure spread network modeling using CCEP signals. The previous unsupervised brain network connectivity problem was converted into a classical supervised sparse representation problem for the first time. In particular, we developed an effective network modeling framework using sparse representation of over-determined features extracted from extensively designed experiments to predict real seizure spread network for each individual patient. The experimental results on five patients achieved prediction accuracy of about 70%, which indicates that it is possible to predict seizure spread network from stimulated CCEP networks. The developed CCEP signal analysis and network modeling approaches are promising to understand network mechanisms of epileptogenesis and have a potential to render clinicians better epilepsy surgical decisions in the future.
KW - Brain connectivity
KW - CCEP
KW - Feature selection
KW - Seizure spread network
KW - Sparse representation
UR - http://www.scopus.com/inward/record.url?scp=84989929078&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84989929078&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-47103-7_26
DO - 10.1007/978-3-319-47103-7_26
M3 - Conference contribution
AN - SCOPUS:84989929078
SN - 9783319471020
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 262
EP - 273
BT - Brain Informatics and Health - International Conference, BIH 2016, Proceedings
A2 - Ali, Hesham
A2 - Shi, Yong
A2 - Ascoli, Giorgio A.
A2 - Khazanchi, Deepak
A2 - Hawrylycz, Michael
T2 - International Conference on Brain Informatics and Health, BIH 2016
Y2 - 13 October 2016 through 16 October 2016
ER -