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Robust Low Complexity Framework for Early Diagnosis of Autism Spectrum Disorder Based on Cross Wavelet Transform and Deep Transfer Learning

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Abstract

Autism spectrum disorder (ASD) starts in the early childhood. Therefore, its diagnosis and classification at the right time would prevent the damages in long terms. EEG signals are non-invasive brain activity signals with excellent temporal resolution and low costs. In this article, the goal is to propose a unified framework for early, efficient and noise robust diagnosis of ASD using EEG signals and with the help of deep transfer learning. In the proposed method, other that the proposed unified diagnosis framework, the main contribution is to use Cross Wavelet Transform (XWT) images for representation of brain signals. After pre-processing and segmentation of the signals, a reference signal is separated from the normal class. Using the reference signal, XWT images are generated. Produced images are fed as input to deep network architectures such as AlexNet, GoogleNet VGG19, ResNet-50 and ResNet-101 in a transfer learning procedure. Transfer learning is applied to make use of information from a source image classification domain while compensating the scarcity of ASD and normal subjects. The approach is evaluated on a dataset of 34 ASD samples and 11 normal case in two different without-voice and with-voice conditions. To validate the early diagnosis hypothesis, EEG signals from children older than 5 years are used as the training set and EEG signals from younger subjects are used as the validation set. Experiments on the proposed framework show that the ResNet-101 deep architecture has achieved the best classification performance. This classification performance is higher than recent reported approaches in terms of classification accuracy, sensitivity, specificity and F1 measure. The results show the effectiveness of the proposed approach in early diagnosis of autism spectrum disorder and also demonstrates the auditory impact on the diagnosis of autism. Also, having evaluated the approach on with-voice and without-voice datasets, the results denote the robustness of the approach against artefacts even when the child subject has the least concentration.

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Data availability

The datasets used during this study will be available from the corresponding author on reasonable request.

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Also the authors declare that no funding was received for this research.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Sadjad University, Mashhad, Iran

    Amir Toranjsimin & Saeed Zahedirad

  2. Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran

    Mohammad Hossein Moattar

Authors
  1. Amir Toranjsimin
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  2. Saeed Zahedirad
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  3. Mohammad Hossein Moattar
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Contributions

All authors contributed to the study conception and design. Material preparation and analysis were performed by AT and SZ. The first draft of the manuscript was written by AT and SZ. Supervision and reviewing and editing the article were performed by MHM. All authors read and approved the final manuscript.

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Correspondence to Mohammad Hossein Moattar.

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Toranjsimin, A., Zahedirad, S. & Moattar, M.H. Robust Low Complexity Framework for Early Diagnosis of Autism Spectrum Disorder Based on Cross Wavelet Transform and Deep Transfer Learning. SN COMPUT. SCI. 5, 231 (2024). https://doi.org/10.1007/s42979-023-02564-9

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