کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4970103 1450026 2017 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Kernel alignment for identifying objective criteria from brain MEG recordings in schizophrenia
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Kernel alignment for identifying objective criteria from brain MEG recordings in schizophrenia
چکیده انگلیسی
The current wide access to data from different neuroimaging techniques has permitted to obtain data to explore the possibility of finding objective criteria that can be used for diagnostic purposes. In order to decide which features of the data are relevant for the diagnostic task, we present in this paper a simple method for feature selection based on kernel alignment with the ideal kernel in support vector machines (SVM). The method presented shows state-of-the-art performance while being more efficient than other methods for feature selection in SVM. It is also less prone to overfitting due to the properties of the alignment measure. All these abilities are essential in neuroimaging study, where the number of features representing recordings is usually very large compared with the number of recordings. The method has been applied to a dataset in order to determine objective criteria for the diagnosis of schizophrenia. The dataset analyzed has been obtained from multichannel magnetoencephalogram (MEG) recordings, corresponding to the recordings during the performance of a mismatch negativity (MMN) auditory task by a set of schizophrenia patients and a control group. All signal frequency bands are analyzed (from δ (1-4 Hz) to high frequency γ (60-200 Hz)) and the signal correlations among the different sensors for these frequencies are used as features.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition Letters - Volume 93, 1 July 2017, Pages 172-181
نویسندگان
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