TY - GEN
T1 - Selection of spectro-temporal patterns in multichannel MEG with support vector machines for schizophrenia classification
AU - Ince, Nuri F.
AU - Goksu, Fikri
AU - Pellizzer, Giuseppe
AU - Tewfik, Ahmed
AU - Stephane, Massoud
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2008
Y1 - 2008
N2 - We present a new framework for the diagnosis of schizophrenia based on the spectro-temporal patterns selected by a support vector machine from multichannel magnetoencephalogram (MEG) recordings in a verbal working memory task. In the experimental paradigm, five letters appearing sequentially on a screen were memorized by subjects. The letters constituted a word in one condition and a pronounceable nonword in the other. Power changes were extracted as features in frequency subbands of 248 channel MEG data to form a rich feature dictionary. A support vector machine has been used to select a small subset of features with recursive feature elimination technique (SVM-RFE) and the reduced subset was used for classification. We note that the discrimination between patients and controls in the word condition was higher than in the non-word condition (91.8% vs 83.8%). Furthermore, in the word condition, the most discriminant patterns were extracted in delta (1-4 Hz), theta (4-8Hz) and alpha (12-16 Hz) frequency bands. We note that these features were located around the left frontal, left temporal and occipital areas, respectively. Our results indicate that the proposed approach can quantify discriminative neural patterns associated to a functional task in spatial, spectral and temporal domain. Moreover these features provide interpretable information to the medical expert about physiological basis of the illness and can be effectively used as a biometric marker to recognize schizophrenia in clinical practice.
AB - We present a new framework for the diagnosis of schizophrenia based on the spectro-temporal patterns selected by a support vector machine from multichannel magnetoencephalogram (MEG) recordings in a verbal working memory task. In the experimental paradigm, five letters appearing sequentially on a screen were memorized by subjects. The letters constituted a word in one condition and a pronounceable nonword in the other. Power changes were extracted as features in frequency subbands of 248 channel MEG data to form a rich feature dictionary. A support vector machine has been used to select a small subset of features with recursive feature elimination technique (SVM-RFE) and the reduced subset was used for classification. We note that the discrimination between patients and controls in the word condition was higher than in the non-word condition (91.8% vs 83.8%). Furthermore, in the word condition, the most discriminant patterns were extracted in delta (1-4 Hz), theta (4-8Hz) and alpha (12-16 Hz) frequency bands. We note that these features were located around the left frontal, left temporal and occipital areas, respectively. Our results indicate that the proposed approach can quantify discriminative neural patterns associated to a functional task in spatial, spectral and temporal domain. Moreover these features provide interpretable information to the medical expert about physiological basis of the illness and can be effectively used as a biometric marker to recognize schizophrenia in clinical practice.
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U2 - 10.1109/iembs.2008.4649973
DO - 10.1109/iembs.2008.4649973
M3 - Conference contribution
AN - SCOPUS:61849108248
SN - 9781424418152
T3 - Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"
SP - 3554
EP - 3557
BT - Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
PB - IEEE Computer Society
T2 - 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
Y2 - 20 August 2008 through 25 August 2008
ER -