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Resting state complex fMRI data ICA-CNN classification framework of patients and healthy people

A healthy human, resting state technology, applied in the field of biomedical signal processing, can solve the problem of shortage of fMRI data, and achieve the effect of reducing the amount of training and improving the accuracy.

Active Publication Date: 2019-08-09
DALIAN UNIV OF TECH
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Problems solved by technology

[0005] The present invention provides a resting-state complex fMRI data ICA-CNN framework for classification of patients and healthy persons, which effectively solves the problem of fMRI data shortage

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  • Resting state complex fMRI data ICA-CNN classification framework of patients and healthy people
  • Resting state complex fMRI data ICA-CNN classification framework of patients and healthy people

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Embodiment Construction

[0031] Combined with the following technical solutions and attached figure 1 , describe a specific embodiment of the present invention in detail.

[0032] Existing K 1 = 42 patients with schizophrenia and K 2 = 40 healthy people (K = K 1 +K 2 =82) Complex fMRI data acquired at rest. In the time dimension, T=146 scans were performed, each scan obtained 53×63×46 whole brain data, and the number of voxels in the brain was V=62336. The steps of adopting the present invention to identify patients with schizophrenia and healthy people are as attached figure 1 shown.

[0033] Step 1: Input multi-subject resting-state complex fMRI data (k=1,…,82) and the category of the subject

[0034] Step 2: For all single subjects Z k Perform PCA dimension reduction, the model order N is from 20 to 140, and take a value every 10, that is, l=13, and obtain 13 different model order N dimensionality reduction data

[0035] Step 3: For all model orders First use the complex EBM algo...

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Abstract

The invention discloses a resting state complex fMRI data ICA-CNN classification framework of patients and healthy people, and belongs to the field of biomedical signal processing. According to the method, an interested functional network separated from resting state complex fMRI data by ICA is taken as a research object, and classification of patients and healthy people is realized by utilizing 2D CNN learning characteristics with few parameters; data augmentation is carried out by utilizing ICA results obtained under multiple groups of model orders, and the problem of fMRI data shortage is solved. Compared with an existing 3D CNN network, the training amount is reduced, and the accuracy is improved. For example, aiming at a plurality of fMRI data collected in an 82 tested resting state,by applying the DMN component extracted by ICA, the slice identification accuracy is higher than that of 3DCNN by (0.728 vs 0.701), and the tested identification accuracy obtained by a tested decisionis further improved by (0.914 vs 0.701).

Description

technical field [0001] The present invention relates to the field of biomedical signal processing, in particular to independent component analysis (ICA) and convolutional neural network ( convolutional neural networks (CNN) classification framework. Background technique [0002] Resting-state fMRI (resting-state fMRI, rs-fMRI) has been widely used in the study of brain function and disease due to its advantages of high resolution, non-invasive, and easy acquisition on patient subjects. Previous studies have shown that rs-fMRI is valuable in extracting brain function information related to neurological diseases. More importantly, due to the additional use of unique phase information, resting-state complex fMRI data contains more brain function information than amplitude fMRI data, and has more potential in the study of brain function and diseases. [0003] At present, deep learning has shown great advantages in the diagnosis of many neurological diseases, including schizoph...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/045G06F18/2134G06F18/2135G06F18/214
Inventor 林秋华邱悦
Owner DALIAN UNIV OF TECH
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