Abstract:
Numerous natural phenomena display repeating self-similar patterns. Fractal is used when a pattern
seems to repeat itself. Fractal and multifractal methods have extensive applications in neurosciences in
which the prevalence of fractal properties like self-similarity in the brain, equipped with a complex structure, in medical data analysis at various levels of observation is admitted. The methods come to the fore
since subtle details are not always detected by physicians, but these are critical particularly in neurological diseases like stroke which may be life-threatening. The aim of this paper is to identify the self-similar,
significant and efficient attributes to achieve high classification accuracy rates for stroke subtypes. Accordingly, two approaches were implemented. The first approach is concerned with application of the fractal
and multifractal methods on the stroke dataset in order to identify the regular, self-similar, efficient and
significant attributes from the dataset, with these steps: a) application of Box-counting dimension generated BC_stroke dataset b) application of Wavelet transform modulus maxima generated WTMM_stroke
dataset. The second approach involves the application of Feed Forward Back Propagation (FFBP) for stroke
subtype classification with these steps: (i) FFBP algorithm was applied on the stroke dataset, BC_stroke
dataset and WTMM_stroke dataset. (ii) Comparative analyses were performed based on accuracy, sensitivity and specificity for the three datasets. The main contribution is that the study has obtained the identification of self-similar, regular and significant attributes from the stroke subtypes datasets by following
multifarious and integrated methodology. The study methodology is based on the singularity spectrum
which provides a value concerning how fractal a set of points are in the datasets (BC_stroke dataset and
WTMM_stroke dataset). The experimental results reveal the applicability, reliability and accuracy of our
proposed integrated method. No earlier work exists in the literature with the relevant stroke datasets
and the methods employed. Therefore, the study aims at pointing a new direction in the relevant fields
concerning the complex dynamic systems and structures which display multifractional nature.
