TY - GEN
T1 - Selection of abnormal neural oscillation patterns associated with sentence-level language disorder in Schizophrenia
AU - Xu, Tingting
AU - Stephane, Massoud
AU - Parhi, Keshab K.
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2012
Y1 - 2012
N2 - Language disorder is one of the core symptoms in schizophrenia. We propose a new framework based on machine intelligence techniques to investigate abnormal neural oscillations related to this impairment. Schizophrenia patients and healthy control subjects were instructed to discriminate semantically and syntactically correct sentences from syntactically correct but semantically incorrect sentences presented visually, and 248-channel MEG signals were recorded with a whole head machine during the task performance. Oscillation patterns were extracted from the MEG recordings in 8 frequency sub-bands throughout sentence processing, which form a large feature set. A two-step feature selection algorithm combining F-score filtering and Support Vector Machine recursive feature elimination (SVM-RFE) was designed to pick out a small subset of features which could discriminate patients and controls with high accuracy. We achieved a 90.48% prediction accuracy based on the selected top features, following the leave-one-out cross validation procedure. These top features provide interpretable spectral, spatial, and temporal information about the electrophysiological basis of sentence processing abnormality in schizophrenia which may help understand the underlying mechanism of this disease.
AB - Language disorder is one of the core symptoms in schizophrenia. We propose a new framework based on machine intelligence techniques to investigate abnormal neural oscillations related to this impairment. Schizophrenia patients and healthy control subjects were instructed to discriminate semantically and syntactically correct sentences from syntactically correct but semantically incorrect sentences presented visually, and 248-channel MEG signals were recorded with a whole head machine during the task performance. Oscillation patterns were extracted from the MEG recordings in 8 frequency sub-bands throughout sentence processing, which form a large feature set. A two-step feature selection algorithm combining F-score filtering and Support Vector Machine recursive feature elimination (SVM-RFE) was designed to pick out a small subset of features which could discriminate patients and controls with high accuracy. We achieved a 90.48% prediction accuracy based on the selected top features, following the leave-one-out cross validation procedure. These top features provide interpretable spectral, spatial, and temporal information about the electrophysiological basis of sentence processing abnormality in schizophrenia which may help understand the underlying mechanism of this disease.
UR - http://www.scopus.com/inward/record.url?scp=84880960275&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84880960275&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2012.6347098
DO - 10.1109/EMBC.2012.6347098
M3 - Conference contribution
C2 - 23367032
AN - SCOPUS:84880960275
SN - 9781424441198
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 4923
EP - 4926
BT - 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012
T2 - 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
Y2 - 28 August 2012 through 1 September 2012
ER -