Application of fractal dimension for EEG based diagnosis of encephalopathy

  • Jisu Elsa Jacob
  • Gopakumar Kuttappan Nair
  • Ajith Cherian
  • Thomas Iype


In this study, we have investigated whether fractal dimension is a useful non linear feature for distinguishing electroencephalogram (EEG) of cases with encephalopathy from that of normal healthy EEGs. Both Higuchi’s fractal dimension and Katz’s fractal dimension were computed and were statistically analyzed between the normal and disease groups. Both parameters showed significant difference between the normal and encephalopathy groups, though Higuchi’s fractal dimension showed better discriminating ability. Support Vector Machine (SVM) classifier was also applied for the automated diagnosis of encephalopathy based on EEG. It has been found that SVM classifier performed better when Higuchi’s fractal dimension was utilized as feature set than using both Higuchi’s and Katz’s FD together.


Fractal dimension Higuchi’s fractal dimension Katz’s fractal dimension Electroencephalogram Encephalopathy Support vector machine 


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringSCT College of EngineeringThiruvananthapuramIndia
  2. 2.Department of ECETKM College of EngineeringKollamIndia
  3. 3.Department of NeurologySCTIMSTThiruvananthapuramIndia
  4. 4.Department of NeurologyGovernment Medical CollegeThiruvananthapuramIndia

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