Multi-time window resting state functional magnetic resonance image data feature analysis method
The invention relates to a multi-time-window resting state functional magnetic resonance image data feature analysis method, which comprises the following steps of: for all training samples, dividing a time sequence of resting state functional magnetic resonance image data into a plurality of contin...
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Main Authors | , , , , , , |
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Format | Patent |
Language | Chinese English |
Published |
12.04.2024
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Subjects | |
Online Access | Get full text |
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Summary: | The invention relates to a multi-time-window resting state functional magnetic resonance image data feature analysis method, which comprises the following steps of: for all training samples, dividing a time sequence of resting state functional magnetic resonance image data into a plurality of continuous time windows, and carrying out multi-time-window joint feature selection by using sparse consistency learning. According to the method, an L1 norm is used for carrying out sparse constraint on feature weights in a single time window, and an L21 norm is used for carrying out consistency constraint on feature weights of all time windows. Besides, according to a learned feature weight matrix, a sample-time window-brain region-brain connected four-order tensor is constructed, then tensor decomposition is used to obtain factor matrixes of the four dimensions, and finally a multi-core support vector machine is used to carry out fusion classification on the selected features. According to the method, the factor matri |
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Bibliography: | Application Number: CN202410046916 |