A novel and efficient Wavelet Scattering Transform approach for primitive-stage dyslexia-detection using electroencephalogram signals

Parmar, Shankar; Paunwala, Chirag N.

doi:10.1016/j.health.2023.100194

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…The wavelet scattering transform (WST) provides a robust time-frequency (t-f) representation of the nonstationary and nonlinear signals [11]. The combination of wavelet transform and scatterplot provides more comprehensive spectro-temporal characteristics of the signal.…”

Section: Wavelet Scattering Transform (Wst)mentioning

confidence: 99%

Predicting Maturity of Coconut Fruit from Acoustic Signal with Applications of Deep Learning

Sattar

2024

The 2nd International Online Conference on Agriculture

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Section: Wavelet Scattering Transform (Wst)mentioning

confidence: 99%

Predicting Maturity of Coconut Fruit from Acoustic Signal with Applications of Deep Learning

Sattar

2024

The 2nd International Online Conference on Agriculture

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“…The findings show that the method proposed in diagnosing and following Dyslexia can give positive results using these EOG signals. In 2023, Shankar Parmar and Chirag Paunwala [9] show that Dyslexia is a neurological disorder affecting reading and writing abilities. The age range of 7-12 years old is ideal for detecting Dyslexia in its primitive stages.…”

Section: Introductionmentioning

confidence: 99%

Classification of Dyslexia Among School Students Using Deep Learning

Alia Hussein,

Ahmed Talib Abdulameer,

Ali Abdulkarim

et al. 2024

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Dyslexia is a common learning disorder that affects children’s reading and writing skills. Early identification of Dyslexia is essential for providing appropriate interventions and support to affected children. Traditional methods of diagnosing Dyslexia often rely on subjective assessments and the expertise of specialists, leading to delays and potential inaccuracies in diagnosis. This study proposes a novel approach for diagnosing dyslexic children using spectrogram analysis and convolutional neural networks (CNNs). Spectrograms are visual representations of audio signals that provide detailed frequency and intensity information. CNNs are powerful deep-learning models capable of extracting complex patterns from data. In this research, raw audio signals from dyslexic and non-dyslexic children are transformed into spectrogram images. These images are then used as input for a CNN model trained on a large dataset of dyslexic and non-dyslexic samples. The CNN learns to automatically extract discriminative features from the spectrogram images and classify them into dyslexic and non-dyslexic categories. This study’s results demonstrate the proposed approach’s effectiveness in diagnosing dyslexic children. The CNN accurately identified dyslexic individuals based on the spectrogram features, outperforming traditional diagnostic methods. Spectrograms and CNNs provide a more objective and efficient approach to dyslexia diagnosis, enabling earlier intervention and support for affected children. This research contributes to the field of dyslexia diagnosis by harnessing the power of machine learning and audio analysis techniques. Facilitating faster and more accurate identification of Dyslexia in children, ultimately improving their educational outcomes and quality of life.

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Early detection of dyslexia based on EEG with novel predictor extraction and selection

Parmar,

Paunwala

2023

Discov Artif Intell

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Dyslexia is a learning disorder caused by difficulties in the brain’s processing of letters and words. This study used EEG recordings to detect dyslexia at a young age. EEG recordings of 53 individuals, including 29 dyslexic and 24 normal individuals, were collected while they were engaged in two distinct mental activities known as the N-Back task and the Oddball task. Predictors were extracted using several methods and reduced using Principal Component Analysis (PCA). A relief-based strategy was applied to select predictors, and Support Vector Machine (SVM) classifier was used to achieve an average accuracy of 79.3% for dyslexia detection, which is better than the performance of its predecessors. The results indicate that EEG recordings and machine learning methods could be useful for identifying dyslexia in children.

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A novel and efficient Wavelet Scattering Transform approach for primitive-stage dyslexia-detection using electroencephalogram signals

Cited by 5 publications

References 26 publications

Predicting Maturity of Coconut Fruit from Acoustic Signal with Applications of Deep Learning

Predicting Maturity of Coconut Fruit from Acoustic Signal with Applications of Deep Learning

Classification of Dyslexia Among School Students Using Deep Learning

Early detection of dyslexia based on EEG with novel predictor extraction and selection

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