Abstract
A suspected metabolic disorder presents a difficult challenge to the physician and the patient. We have developed a fully automated system in order to analyze and classify the magnetic resonance spectroscopy signals of patients with metabolic brain diseases. We utilized wavelets to extract signal features and in the time-frequency representations to optimize the feature extraction procedure. Novel fuzzy membership functions and a fuzzy relational classifier were designed to categorize the metabolic brain diseases in children using the information obtained from the feature extraction routine. The sensitivity (Se) and the positive predictivity (PP) of 88.26% and 91.04% in extracting features and 89.66% and 100%, respectively, in detecting metabolic brain diseases has been achieved.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 75-95 |
| Number of pages | 21 |
| Journal | Fuzzy Sets and Systems |
| Volume | 161 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 1 2010 |
Keywords
- Fractals
- Frequency ordered wavelet packets
- Fuzzy membership functions
- Fuzzy relational classifiers
- Magnetic resonance spectroscopy
- Metabolic brain diseases
- Time-frequency representations
- Time-scale representations
- Wavelets
ASJC Scopus subject areas
- Logic
- Artificial Intelligence