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Diagnosis of schizophrenia from R-fMRI data using Ripplet transform and OLPP

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Abstract

Schizophrenia is a severe brain disease that influences the behaviour and thought of person. These effects may fail in achieving the expected levels of interpersonal, academic, or occupational functioning. Although the underlying mechanism is not yet clear, the early detection of schizophrenia is an attractive and challenging research area. There are differences in brain connections of patients and healthy people. This study presents a new computer-aided diagnosis (CAD) method to diagnose schizophrenia (SZ) patients from normal control (NC) people by using the rest-state functional magnetic resonance imaging (R-fMRI) data. fMRI data has a huge dimension, and extracting efficient features is still an open challenge for a schizophrenia diagnosis. In the proposed method, at first orthogonal locality preserving projection (OLPP) is used to reduce the number of time points in R-fMRI scans. Then, an independent component analysis (ICA) algorithm is employed to estimate the independent components (ICs). Next, orthogonal Ripplet-II transform is applied to each IC to extract features. Afterward, a two-sample T-test is implemented on the extracted features to find the most discriminative features. Then, the number of selected features is reduced by applying OLPP. Finally, a test subject is classified into SZ or NC using a linear support vector machine (SVM) classifier. The proposed method is evaluated on the NAMIC and COBRE databases. The results demonstrate that the introduced method significantly outperforms previously presented methods.

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Abbreviations

1D:

One-dimensional

LOOCV:

Leave-one-out cross-validation.

2D:

Two-dimensional

MEG:

Magnetoencephalography

AOD:

Auditory oddball

MNI:

Montreal neurological institute

ASSET:

Array spatial sensitivity encoding techniques

MPSO:

Modified particle swarm optimization

CAD:

Computer-aided diagnosis

MR:

Magnetic resonance

CLAHE:

Contrast limited adaptive histogram equalization

MRI:

Magnetic resonance imaging

EEG:

Electroencephalogram

NC:

Normal (healthy) control

ELM:

Extreme learning machine

NCSE:

Normalized cumulative sum of eigenvalues

EPI:

Echo planar imaging

OLPP:

Orthogonal locality preserving projection

fMRI:

Functional magnetic resonance imaging

PCA:

Principal component analysis

FLD:

Fisher’s linear discriminant

PCC:

Probability of correct classification

FN:

False negative

RBF:

Radial basis function

FP:

False positive

R-fMRI:

Rest-state functional magnetic resonance imaging

FT:

Fourier transform

SPM:

Statistical parametric mapping

GLM:

General linear model

SVD:

Singular value decomposition

GR:

Generalized Radon

SVM:

Support vector machine

IC:

Independent component

SZ:

Schizophrenia patient

ICA:

Independent component analysis

TN:

Ture negative

IJaya:

Improved Jaya algorithm

TP:

True positive

KPCA:

Kernel principal component analysis

VLBP:

Volume local binary pattern

LDA:

Linear discriminant analysis

WT:

Wavelet transform

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Authors and Affiliations

  1. Department of Electrical Engineering, Urmia University, Urmia, Iran

    Shadi Sartipi & Mahrokh G. Shayesteh

  2. Faculty of Electrical Engineering, Urmia University of Technology, Urmia, Iran

    Hashem Kalbkhani

  3. Wireless Research Laboratory, ACRI, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran

    Mahrokh G. Shayesteh

Authors
  1. Shadi Sartipi
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  2. Hashem Kalbkhani
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  3. Mahrokh G. Shayesteh
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Correspondence to Mahrokh G. Shayesteh.

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Sartipi, S., Kalbkhani, H. & Shayesteh, M.G. Diagnosis of schizophrenia from R-fMRI data using Ripplet transform and OLPP. Multimed Tools Appl 79, 23401–23423 (2020). https://doi.org/10.1007/s11042-020-09122-y

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  • DOI: https://doi.org/10.1007/s11042-020-09122-y

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