title: Exploring multifractal-based features for mild Alzheimer's disease classification.
abstract: Multifractal applications to resting state functional MRI (rs-fMRI) time series for diagnosing Alzheimer's disease (AD) are still limited. We aim to address two issues: (I) if and what multifractal features are sufficiently discriminative to detect AD from the healthy; (II) if AD classification could be further improved by combining multifractal features with traditional features in this field.
  
  
  
  Rs-fMRI data of 25 AD patients and 38 normal controls were analyzed. A set of multifractal features were systematically investigated. Traditional features in monofractal, linear, and network-based categories were also extracted for comparison and combination. Both support vector machines and multiple kernel learning (MKL) were used to perform classification with individual and combined features.
  
  
  
  We identified a multifractal feature, Δf, which has the strongest discriminative power among all the features in our study. Moreover, we found that the classification accuracy could be significantly improved from 69% (by Δf only) to up to 76%, when nonsparse MKL is used to combine Δf with the monofractal feature, Hurst. Finally, we showed that incorporating other multifractal features, α(0), Δα and Pc, could also improve traditional-feature-based AD classification.
  
  
  
  Our work demonstrated the potential usefulness of multifractal analysis for AD research, especially when combining with the traditional rs-fMRI features. It contributes to distinguishing AD from NC subjects. Magn Reson Med 76:259-269, 2016. © 2015 Wiley Periodicals, Inc.
authors.affiliation: School of Electronic Science and Engineering, Nanjing University, Nanjing, China.|School of Computer Science and Software Engineering, University of Wollongong, Wollongong, NSW, Australia.
authors.forename: Huangjing
authors.initials: H
authors.lastname: Ni
authors.affiliation: School of Computer Science and Software Engineering, University of Wollongong, Wollongong, NSW, Australia.
authors.forename: Luping
authors.initials: L
authors.lastname: Zhou
authors.affiliation: School of Electronic Science and Engineering, Nanjing University, Nanjing, China.
authors.forename: Xinbao
authors.initials: X
authors.lastname: Ning
authors.affiliation: School of Computer Science and Software Engineering, University of Wollongong, Wollongong, NSW, Australia.
authors.forename: Lei
authors.initials: L
authors.lastname: Wang
mesh_terms: D000368:Aged; D000465:Algorithms; D000544:Alzheimer Disease / Q000000981:diagnostic imaging*; D001921:Brain / Q000000981:diagnostic imaging*; D003937:Diagnosis, Differential; D005260:Female; D017709:Fractals; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D013382:Subtraction Technique; D060388:Support Vector Machine
keywords: Alzheimer's disease; classification; multifractal; multiple kernel learning