Abstract
In order to detect hearing loss more efficiently and accurately, this study proposed a new method based on fractional Fourier transform (FRFT). Three-dimensional volumetric magnetic resonance images were obtained from 15 patients with left-sided hearing loss (LHL), 20 healthy controls (HC), and 14 patients with right-sided hearing loss (RHL). Twenty-five FRFT spectrums were reduced by principal component analysis with thresholds of 90%, 95%, and 98%, respectively. The classifier is the single-hidden-layer feed-forward neural network (SFN) trained by the Levenberg–Marquardt algorithm. The results showed that the accuracies of all three classes are higher than 95%. In all, our method is promising and may raise interest from other researchers.
| Original language | English |
|---|---|
| Article number | 194 |
| Journal | Entropy |
| Volume | 18 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - May 2016 |
| Externally published | Yes |
Keywords
- Artificial neural network
- Computer-aided diagnosis
- Fractional Fourier transform
- Hearing loss
- Levenberg–Marquardt algorithm
- Principal component analysis
- Unified time-frequency domain
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