A computer-aided diagnosis for distinguishing Tourette's syndrome from chronic tic disorder in children by a fuzzy system with a two-step minimization approach

Tang Kai Yin, Nan-Tsing Chiu

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Tourette's syndrome, no longer considered as a rare and unusual disease, is the most severe tic disorder in children. Early differential diagnosis between Tourette's syndrome and chronic tic disorder is difficult but important because proper and early medical therapy can improve the child's condition. Brain single-photon emission computed tomography (SPECT) perfusion imaging with technetium-99m hexamethylpropylene amine oxime is a method to distinguish these two diseases. In this paper, a fuzzy system called characteristic-point-based fuzzy inference system (CPFIS) is proposed to help radiologists perform computer-aided diagnosis (CAD). The CPFIS consists of SPECT-volume processing, input-variables selection, characteristic-points (CPs) derivation, and parameter tuning of the fuzzy system. Experimental results showed that the major fuzzy rules from the obtained CPs match the major patterns of Tourette's syndrome and chronic tic disorder in perfusion imaging. If any case that was diagnosed as chronic tic by the radiologist but as Tourette's syndrome by the CPFIS was taken as Tourette's syndrome, then the accuracy of the radiologist was increased from 87.5% (21 of 24) without the CPFIS to 91.7% (22 of 24) with the CPFIS. All 17 cases of Tourette's syndrome, which is more severe than chronic tic disorder, were correctly classified. Although the construction and application process of the proposed method is complete, more samples should be used and tested in order to design a universally effective CAD without small sample-size concerns in this research.

Original languageEnglish
Pages (from-to)1286-1295
Number of pages10
JournalIEEE Transactions on Biomedical Engineering
Volume51
Issue number7
DOIs
Publication statusPublished - 2004 Jul 1

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

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