Enhancing Dyslexia Detection and Classification based on Wavelet Scattered Transform and Dirichlet Mixture Model
Enhancing Dyslexia Detection and Classification based on Wavelet Scattered Transform and Dirichlet Mixture Model |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-6 |
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| Year of Publication : 2025 | ||
| Author : J. Jincy, P. Subha Hency Jose, P.Sweety Jose, S.Thomas George, Mary Vasanthi Soosaimariyan, Sujin Jose Arul | ||
| DOI : 10.14445/22315381/IJETT-V73I6P109 | ||
How to Cite?
J. Jincy, P. Subha Hency Jose, P.Sweety Jose, S.Thomas George, Mary Vasanthi Soosaimariyan, Sujin Jose Arul, "Enhancing Dyslexia Detection and Classification based on Wavelet Scattered Transform and Dirichlet Mixture Model," International Journal of Engineering Trends and Technology, vol. 73, no. 6, pp.91-101, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I6P109
Abstract
Since traditional diagnosis methods rely on behavioural testing rather than biological signs, detecting dyslexia is complicated. Information Extraction about the brain actions participated in various jobs, and delving into their biological underpinnings is challenging. As a result, using biomarkers can aid in both the diagnosis and a deeper comprehension of particular learning problems, including dyslexia. Differences between controls and dyslexic subjects can be found using Electroencephalography (EEG) signals with the proper signal processing and artificial intelligence approaches. In this work, we have combined the benefits of the Dirichlet Mixture Model (DMM) and wavelet scattered transform (WST) to convert the EEG data into high-level representations that enable the detection of dyslexic discriminative descriptors. Furthermore, we have developed a predictive Stoer-Wagner algorithm-based Naïve Bayes (SW-NB) classifier that performs exceptionally well with an exploratory examination of the EEG signals to detect dyslexia accurately. The suggested SW-NB classification model, in conjunction with WS-DMM, may significantly increase the accuracy of disease identification. A dataset of confused students' brainwaves is used to test the suggested classification model, and the findings are more promising.
Keywords
EEE signal, Dyslexia detection, Wavelet scattered dirichlet mixture model, Signal denoising, Naïve bayes classifier.
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